subject:(Distributed Artificial Intelligence)

Victoria University of Wellington

1. Kinzett, Alan David. Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming.

Degree: 2011, Victoria University of Wellington

► In tree-based genetic programming (GP) there is a tendency for the program trees to increase in size from one generation to the next. If this… (more)

▼ In tree-based genetic programming (GP) there is a tendency for the program trees to increase in size from one generation to the next. If this increase in program size is not accompanied by an improvement in fitness then this unproductive increase is known as bloat. It is standard practice to place some form of control on program size. This can be done by limiting the number of nodes or the depth of the program trees, or by adding a component to the fitness function that rewards smaller programs (parsimony pressure) or by simplifying individual programs using algebraic methods. This thesis proposes a novel program simplification method called numerical simplification that uses only the range of values the nodes take during fitness evaluation. The effect of online program simplification, both algebraic and numerical, on program size and resource usage is examined. This thesis also examines the distribution of program fragments within a genetic programming population and how this is changed by using simplification. It is shown that both simplification approaches result in reductions in average program size, memory used and computation time and that numerical simplification performs at least as well as algebraic simplification, and in some cases will outperform algebraic simplification. This reduction in program size and the resources required to process the GP run come without any significant reduction in accuracy. It is also shown that although the two online simplification methods destroy some existing program fragments, they generate new fragments during evolution, which compensates for any negative effects from the disruption of existing fragments. It is also shown that, after the first few generations, the rate new fragments are created, the rate fragments are lost from the population, and the number of distinct (different) fragments in the population remain within a very narrow range of values for the remainder of the run. Advisors/Committee Members: Zhang, Mengjie.

Subjects/Keywords: Computational intelligence; Genetic programming; Distributed artificial intelligence

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APA (6^th Edition):

Kinzett, A. D. (2011). Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/1863

Chicago Manual of Style (16^th Edition):

Kinzett, Alan David. “Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming.” 2011. Masters Thesis, Victoria University of Wellington. Accessed January 22, 2021. http://hdl.handle.net/10063/1863.

MLA Handbook (7^th Edition):

Kinzett, Alan David. “Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming.” 2011. Web. 22 Jan 2021.

Vancouver:

Kinzett AD. Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming. [Internet] [Masters thesis]. Victoria University of Wellington; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10063/1863.

Council of Science Editors:

Kinzett AD. Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming. [Masters Thesis]. Victoria University of Wellington; 2011. Available from: http://hdl.handle.net/10063/1863

Rochester Institute of Technology

2. Galatic, Paul. Divide and Conquer in Neural Style Transfer for Video.

Degree: MS, Computer Science (GCCIS), 2020, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/10384

► Neural Style Transfer is a class of neural algorithms designed to redraw a given image in the style of another image, traditionally a famous… (more)

Subjects/Keywords: Artificial; Distributed; Intelligence; Neural; Style; Transfer

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APA (6^th Edition):

Galatic, P. (2020). Divide and Conquer in Neural Style Transfer for Video. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10384

Chicago Manual of Style (16^th Edition):

Galatic, Paul. “Divide and Conquer in Neural Style Transfer for Video.” 2020. Masters Thesis, Rochester Institute of Technology. Accessed January 22, 2021. https://scholarworks.rit.edu/theses/10384.

MLA Handbook (7^th Edition):

Galatic, Paul. “Divide and Conquer in Neural Style Transfer for Video.” 2020. Web. 22 Jan 2021.

Vancouver:

Galatic P. Divide and Conquer in Neural Style Transfer for Video. [Internet] [Masters thesis]. Rochester Institute of Technology; 2020. [cited 2021 Jan 22]. Available from: https://scholarworks.rit.edu/theses/10384.

Council of Science Editors:

Galatic P. Divide and Conquer in Neural Style Transfer for Video. [Masters Thesis]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10384

University of Georgia

3. Barnhard, David Howard. Distributed collaborative robotic mapping.

Degree: 2014, University of Georgia

URL: http://hdl.handle.net/10724/22575

► The utilization of multiple robots to map an unknown environment is a challenging problem within Artificial Intelligence. This thesis first presents previous efforts to develop… (more)

Subjects/Keywords: Robotics; Artificial Intelligence; Distributed Processing; Collaborative Robotics

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APA (6^th Edition):

Barnhard, D. H. (2014). Distributed collaborative robotic mapping. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/22575

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Barnhard, David Howard. “Distributed collaborative robotic mapping.” 2014. Thesis, University of Georgia. Accessed January 22, 2021. http://hdl.handle.net/10724/22575.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Barnhard, David Howard. “Distributed collaborative robotic mapping.” 2014. Web. 22 Jan 2021.

Vancouver:

Barnhard DH. Distributed collaborative robotic mapping. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10724/22575.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Barnhard DH. Distributed collaborative robotic mapping. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/22575

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

4. Abdi, Afshin. Distributed learning and inference in deep models.

Degree: PhD, Electrical and Computer Engineering, 2020, Georgia Tech

URL: http://hdl.handle.net/1853/63671

► In recent years, the size of deep learning problems has been increased significantly, both in terms of the number of available training samples as well… (more)

▼ In recent years, the size of deep learning problems has been increased significantly, both in terms of the number of available training samples as well as the number of parameters and complexity of the model. In this thesis, we considered the challenges encountered in training and inference of large deep models, especially on nodes with limited computational power and capacity. We studied two classes of related problems; 1) distributed training of deep models, and 2) compression and restructuring of deep models for efficient distributed and parallel execution to reduce inference times. Especially, we considered the communication bottleneck in distributed training and inference of deep models. Data compression is a viable tool to mitigate the communication bottleneck in distributed deep learning. However, the existing methods suffer from a few drawbacks, such as the increased variance of stochastic gradients (SG), slower convergence rate, or added bias to SG. In my Ph.D. research, we have addressed these challenges from three different perspectives: 1) Information Theory and the CEO Problem, 2) Indirect SG compression via Matrix Factorization, and 3) Quantized Compressive Sampling. We showed, both theoretically and via simulations, that our proposed methods can achieve smaller MSE than other unbiased compression methods with fewer communication bit-rates, resulting in superior convergence rates. Next, we considered federated learning over wireless multiple access channels (MAC). Efficient communication requires the communication algorithm to satisfy the constraints imposed by the nodes in the network and the communication medium. To satisfy these constraints and take advantage of the over-the-air computation inherent in MAC, we proposed a framework based on random linear coding and developed efficient power management and channel usage techniques to manage the trade-offs between power consumption and communication bit-rate. In the second part of this thesis, we considered the distributed parallel implementation of an already-trained deep model on multiple workers. Since latency due to the synchronization and data transfer among workers adversely affects the performance of the parallel implementation, it is desirable to have minimum interdependency among parallel sub-models on the workers. To achieve this goal, we developed and analyzed RePurpose, an efficient algorithm to rearrange the neurons in the neural network and partition them (without changing the general topology of the neural network) such that the interdependency among sub-models is minimized under the computations and communications constraints of the workers. Advisors/Committee Members: Fekri, Faramarz (advisor), AlRegib, Ghassan (committee member), Romberg, Justin (committee member), Bloch, Matthieu (committee member), Maguluri, Siva Theja (committee member).

Subjects/Keywords: Machine learning; Artificial intelligence; Distributed training; Distributed learning

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APA (6^th Edition):

Abdi, A. (2020). Distributed learning and inference in deep models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63671

Chicago Manual of Style (16^th Edition):

Abdi, Afshin. “Distributed learning and inference in deep models.” 2020. Doctoral Dissertation, Georgia Tech. Accessed January 22, 2021. http://hdl.handle.net/1853/63671.

MLA Handbook (7^th Edition):

Abdi, Afshin. “Distributed learning and inference in deep models.” 2020. Web. 22 Jan 2021.

Vancouver:

Abdi A. Distributed learning and inference in deep models. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/1853/63671.

Council of Science Editors:

Abdi A. Distributed learning and inference in deep models. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63671

University of California – Berkeley

5. Curtis, Kristal Lyn. Leveraging Similar Regions to Improve Genome Data Processing.

Degree: Computer Science, 2015, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/0bq516zw

► Though DNA sequencing has improved dramatically over the past decade, variant calling, which is the process of reconstructing a patient’s genome from the reads that… (more)

Subjects/Keywords: Computer science; Bioinformatics; Artificial intelligence; Distributed algorithms; Genomics; Machine learning

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APA (6^th Edition):

Curtis, K. L. (2015). Leveraging Similar Regions to Improve Genome Data Processing. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/0bq516zw

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Curtis, Kristal Lyn. “Leveraging Similar Regions to Improve Genome Data Processing.” 2015. Thesis, University of California – Berkeley. Accessed January 22, 2021. http://www.escholarship.org/uc/item/0bq516zw.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Curtis, Kristal Lyn. “Leveraging Similar Regions to Improve Genome Data Processing.” 2015. Web. 22 Jan 2021.

Vancouver:

Curtis KL. Leveraging Similar Regions to Improve Genome Data Processing. [Internet] [Thesis]. University of California – Berkeley; 2015. [cited 2021 Jan 22]. Available from: http://www.escholarship.org/uc/item/0bq516zw.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Curtis KL. Leveraging Similar Regions to Improve Genome Data Processing. [Thesis]. University of California – Berkeley; 2015. Available from: http://www.escholarship.org/uc/item/0bq516zw

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of California – Irvine

6. AHN, SUNGJIN. Stochastic Gradient MCMC: Algorithms and Applications.

Degree: Computer Science, 2015, University of California – Irvine

URL: http://www.escholarship.org/uc/item/4k8039zm

► Despite the powerful advantages of Bayesian inference such as quantifying uncertainty, ac- curate averaged prediction, and preventing overfitting, the traditional Markov chain Monte Carlo (MCMC)… (more)

▼ Despite the powerful advantages of Bayesian inference such as quantifying uncertainty, ac- curate averaged prediction, and preventing overfitting, the traditional Markov chain Monte Carlo (MCMC) method has been regarded unsuitable for large-scale problems because it required processing the entire dataset per iteration rather than using a small random mini- batch as performed in the stochastic gradient optimization. The first attempt toward the scalable MCMC method based on stochastic gradients is the stochastic gradient Langevin dynamics (SGLD) proposed by Welling and Teh [2011]. Originated from the Langevin Monte Carlo method, SGLD achieved O(n) computation per iteration (here, n is the size of a minibatch) by using stochastic gradients estimated using minibatches and skipping the Metropolis-Hastings accept-reject test.In this thesis, we introduce recent advances in the stochastic gradient MCMC method since the advent of SGLD. Our contributions are two-fold: algorithms and applications. In the algorithm part, we first propose the stochastic gradient Fisher scoring algorithm (SGFS) which resolves two drawbacks of SGLD: the poor mixing rate and the arbitrarily large bias occurred when using large step sizes. Then, we also propose the distributed SGLD (D-SGLD) algorithm which makes it possible to extend the power of stochastic gradient MCMC to the distributed computing systems. In the second part, we apply the developed SG-MCMC algorithms to the most popular large-scale problems: the topic modeling using the latent Dirichlet allocation model, recommender systems using matrix factorization, and community modeling in social networks using mixed membership stochastic blockmodels. By resolving the unique challenges raised by each of the applications, which make it difficult to directly use the existing SG-MCMC methods, we obtain the-state-of-the-art results outperforming existing approaches using collapsed Gibbs sampling, stochastic variational inference, or dis- tributed stochastic gradient descent.

Subjects/Keywords: Artificial intelligence; Bayesian; Distributed; Large-scale; MCMC; Scalable; stochastic gradient

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APA (6^th Edition):

AHN, S. (2015). Stochastic Gradient MCMC: Algorithms and Applications. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/4k8039zm

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

AHN, SUNGJIN. “Stochastic Gradient MCMC: Algorithms and Applications.” 2015. Thesis, University of California – Irvine. Accessed January 22, 2021. http://www.escholarship.org/uc/item/4k8039zm.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

AHN, SUNGJIN. “Stochastic Gradient MCMC: Algorithms and Applications.” 2015. Web. 22 Jan 2021.

Vancouver:

AHN S. Stochastic Gradient MCMC: Algorithms and Applications. [Internet] [Thesis]. University of California – Irvine; 2015. [cited 2021 Jan 22]. Available from: http://www.escholarship.org/uc/item/4k8039zm.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

AHN S. Stochastic Gradient MCMC: Algorithms and Applications. [Thesis]. University of California – Irvine; 2015. Available from: http://www.escholarship.org/uc/item/4k8039zm

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas Tech University

7. Ottaway, Thomas A. A conceptual model and prototype of an adaptive production control system.

Degree: 1995, Texas Tech University

URL: http://hdl.handle.net/2346/20132

► The literature suggests two divergent approaches to the structure of intelligent production control systems. The National Institute for Standards and Technology (NIST), through its Advanced… (more)

Subjects/Keywords: Distributed artificial intelligence; Production control

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APA (6^th Edition):

Ottaway, T. A. (1995). A conceptual model and prototype of an adaptive production control system. (Thesis). Texas Tech University. Retrieved from http://hdl.handle.net/2346/20132

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ottaway, Thomas A. “A conceptual model and prototype of an adaptive production control system.” 1995. Thesis, Texas Tech University. Accessed January 22, 2021. http://hdl.handle.net/2346/20132.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ottaway, Thomas A. “A conceptual model and prototype of an adaptive production control system.” 1995. Web. 22 Jan 2021.

Vancouver:

Ottaway TA. A conceptual model and prototype of an adaptive production control system. [Internet] [Thesis]. Texas Tech University; 1995. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/2346/20132.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ottaway TA. A conceptual model and prototype of an adaptive production control system. [Thesis]. Texas Tech University; 1995. Available from: http://hdl.handle.net/2346/20132

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Georgia State University

8. Rivers, Katelyn. Can Global Workspace Theory Solve the Frame Problem?.

Degree: MA, Philosophy, 2018, Georgia State University

URL: https://scholarworks.gsu.edu/philosophy_theses/223

► The Frame Problem originated as an obstacle for classical, symbolic A.I. that was adopted, expanded, and reformulated by philosophers. The version of the problem… (more)

Subjects/Keywords: Frame Problem; Relevance; Artificial Intelligence; Global Workspace Theory; Distributed Processing; Modularity

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APA (6^th Edition):

Rivers, K. (2018). Can Global Workspace Theory Solve the Frame Problem?. (Thesis). Georgia State University. Retrieved from https://scholarworks.gsu.edu/philosophy_theses/223

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Rivers, Katelyn. “Can Global Workspace Theory Solve the Frame Problem?.” 2018. Thesis, Georgia State University. Accessed January 22, 2021. https://scholarworks.gsu.edu/philosophy_theses/223.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Rivers, Katelyn. “Can Global Workspace Theory Solve the Frame Problem?.” 2018. Web. 22 Jan 2021.

Vancouver:

Rivers K. Can Global Workspace Theory Solve the Frame Problem?. [Internet] [Thesis]. Georgia State University; 2018. [cited 2021 Jan 22]. Available from: https://scholarworks.gsu.edu/philosophy_theses/223.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rivers K. Can Global Workspace Theory Solve the Frame Problem?. [Thesis]. Georgia State University; 2018. Available from: https://scholarworks.gsu.edu/philosophy_theses/223

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Michigan State University

9. Makar, Rajbala. Hierarchical multi agent reinforcement learning.

Degree: MS, Department of Computer Science & Engineering, 2000, Michigan State University

URL: http://etd.lib.msu.edu/islandora/object/etd:28318

Subjects/Keywords: Distributed artificial intelligence; Reinforcement learning

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APA (6^th Edition):

Makar, R. (2000). Hierarchical multi agent reinforcement learning. (Masters Thesis). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:28318

Chicago Manual of Style (16^th Edition):

Makar, Rajbala. “Hierarchical multi agent reinforcement learning.” 2000. Masters Thesis, Michigan State University. Accessed January 22, 2021. http://etd.lib.msu.edu/islandora/object/etd:28318.

MLA Handbook (7^th Edition):

Makar, Rajbala. “Hierarchical multi agent reinforcement learning.” 2000. Web. 22 Jan 2021.

Vancouver:

Makar R. Hierarchical multi agent reinforcement learning. [Internet] [Masters thesis]. Michigan State University; 2000. [cited 2021 Jan 22]. Available from: http://etd.lib.msu.edu/islandora/object/etd:28318.

Council of Science Editors:

Makar R. Hierarchical multi agent reinforcement learning. [Masters Thesis]. Michigan State University; 2000. Available from: http://etd.lib.msu.edu/islandora/object/etd:28318

10. Adhikari, Navin K. Co-evolving Distributed Control for Heterogeneous Agents in RTS Games.

Degree: 2019, University of Nevada – Reno

URL: http://hdl.handle.net/11714/5997

► We investigate competitive and co-operative co-evolutionary approaches to generating transparent distributed control for teams of heterogeneous agents in RTS games. RTS games provide a challenging… (more)

▼ We investigate competitive and co-operative co-evolutionary approaches to generating transparent distributed control for teams of heterogeneous agents in RTS games. RTS games provide a challenging test-bed for AI researchers as they simulate many fundamental AI research problems in a virtual world. In this thesis, we use an open-source RTS game engine called FastEcslent and the popular RTS game, Starcraft II as our test-beds. We represent the problem of generating transparent distributed control in RTS games as a set of numerical parameters that co-evolve to optimize distributed control for agents in multiple skirmish scenarios. This thesis makes three contributions to research in distributed transparent control.First, we remove the need for having a high performing opponent to evolve against by using competitive co-evolution to generate different individual control behaviors for agents working towards single-objective or multi-objective goals from scratch. We then use case-injection to transfer competitively co-evolved behavior from FastEcslent, which runs fast and enables many more fitness evaluations in a reasonable time, to Starcraft II.Second, we remove the need for having one general representation for evolving distributed control for heterogeneous agents by co-operatively co-evolving individual groups of similar agents in Starcraft II. Each group has a different evolutionary representation but shares a common fitness evaluation. Third, we also investigate a new representation for generating such control for ranged agents and show improvement over our previous representation. Results show that we can co-evolve winning distributed control in skirmishes across two different RTS games and with multiple representations. Results also show that we can competitively co-evolve higher performance, faster, with case-injection. We believe these results indicate the viability of our co-evolutionary approaches and representations for generating high quality, transparent, distributed control for heterogeneous agents in RTS games and that case-injection can lead to skill transfer across similar environments. Advisors/Committee Members: Louis, Sushil J. (advisor), Dascalu, Sergiu (committee member), Houmanfar, Ramona (committee member).

Subjects/Keywords: Artificial Intelligence; Coevolution; Distributed Control; Evolutionary Algorithms; Potential Fields; RTS Micro

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APA (6^th Edition):

Adhikari, N. K. (2019). Co-evolving Distributed Control for Heterogeneous Agents in RTS Games. (Thesis). University of Nevada – Reno. Retrieved from http://hdl.handle.net/11714/5997

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Adhikari, Navin K. “Co-evolving Distributed Control for Heterogeneous Agents in RTS Games.” 2019. Thesis, University of Nevada – Reno. Accessed January 22, 2021. http://hdl.handle.net/11714/5997.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Adhikari, Navin K. “Co-evolving Distributed Control for Heterogeneous Agents in RTS Games.” 2019. Web. 22 Jan 2021.

Vancouver:

Adhikari NK. Co-evolving Distributed Control for Heterogeneous Agents in RTS Games. [Internet] [Thesis]. University of Nevada – Reno; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11714/5997.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Adhikari NK. Co-evolving Distributed Control for Heterogeneous Agents in RTS Games. [Thesis]. University of Nevada – Reno; 2019. Available from: http://hdl.handle.net/11714/5997

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universidade Federal de Viçosa

11. Carlos Alberto Araújo Júnior. Simulação multiagentes aplicada ao planejamento da produção florestal sustentável.

Degree: 2012, Universidade Federal de Viçosa

URL: http://www.tede.ufv.br/tedesimplificado/tde_busca/arquivo.php?codArquivo=4108

►

O presente trabalho objetivou avaliar a aplicação do paradigma da Inteligência Artificial Distribuída em problemas de manejo florestal. Para isso, foram desenvolvidos dois Sistemas Multiagentes:… (more)

▼

O presente trabalho objetivou avaliar a aplicação do paradigma da Inteligência Artificial Distribuída em problemas de manejo florestal. Para isso, foram desenvolvidos dois Sistemas Multiagentes: um para o planejamento de longo prazo de um empreendimento florestal (i) e outro para seqüenciamento da colheita no intervalo de um ano (ii). Para o SMA i, foram modelados três tipos de agentes: agente de colheita, agente de inventário e agente de controladoria. O ambiente considerado foi uma empresa florestal com 120 talhões em área igual a 4.269,29 ha. Os agentes atuaram de maneira sincronizada no ambiente buscando atingir o objetivo global do sistema que foi determinar a sequência de colheita anual que retornasse maior valor presente líquido. Foram consideradas restrições de integridade dos talhões, demandas mínima e máxima, áreas mínima e máxima manejadas anualmente e colheita com prioridade para florestas mais velhas. O SMA foi capaz de gerar cenários viáveis e avaliar qual destes era a melhor solução. O SMA ii considerou a modelagem de três tipos de agentes: agente de cadastro, agente de colheita e agente de controle. O foco do SMA ii foi gerar planos que indicassem os talhões a serem colhidos em cada mês no horizonte de planejamento de um ano. Foram consideradas as restrições de variação na produção mensal menor que 10% e variação na densidade média da madeira entregue na unidade de processamento inferior a 5%. Para este SMA avaliou-se o comportamento do mesmo em duas situações. Na primeira situação o agente de colheita possuía um direcionamento para seqüenciamento de fazendas a serem manejadas e na segunda situação esse conhecimento foi-lhe retirado. O SMA ii foi capaz de gerar cenários de corte que atendessem às restrições estabelecidas. O aumento do grau de conhecimento do agente de colheita em relação à sequência de fazendas para deslocamento da frente de colheita melhorou o desempenho do sistema. Concluiu-se que Sistemas Multiagentes podem ser utilizados como ferramenta para o ordenamento da produção florestal de longo e curto prazos.

This study aimed to evaluate the application of the paradigm of Distributed Artificial Intelligence in forest management problems. For this, we developed two Multi-Agent Systems: one for the long-term planning of a forest schedule (i) and other for the harvester planning in one year (ii). For the MAS (i) were modeled three types of agents: harvest agent, inventory agent and control agent. The environment was a forest company with 120 stands in area equal to 4.269,29 ha. The agents acted in synchrony in the environment trying to achieve the overall goal of the system, that was to determine the sequence of annual harvest to return higher net present value. We considered constraints about integrity of stands, minimum and maximum woods demands, minimum and maximum areas managed and harvested annually with priority given to older forests. The MAS was able to generate feasible scenarios and evaluate which of these was the best solution. The MAS (ii) considered the modeling of three…

Advisors/Committee Members: José Marinaldo Gleriani, Carlos Antônio Alvares Soares Ribeiro, Gilciano Saraiva Nogueira, Hélio Garcia Leite.

Subjects/Keywords: Regulação florestal; Inteligência Artificial Distribuída; Heurística; MANEJO FLORESTAL; Forest regulation; Heuristics; Distributed Artificial Intelligence

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APA (6^th Edition):

Júnior, C. A. A. (2012). Simulação multiagentes aplicada ao planejamento da produção florestal sustentável. (Thesis). Universidade Federal de Viçosa. Retrieved from http://www.tede.ufv.br/tedesimplificado/tde_busca/arquivo.php?codArquivo=4108

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Júnior, Carlos Alberto Araújo. “Simulação multiagentes aplicada ao planejamento da produção florestal sustentável.” 2012. Thesis, Universidade Federal de Viçosa. Accessed January 22, 2021. http://www.tede.ufv.br/tedesimplificado/tde_busca/arquivo.php?codArquivo=4108.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Júnior, Carlos Alberto Araújo. “Simulação multiagentes aplicada ao planejamento da produção florestal sustentável.” 2012. Web. 22 Jan 2021.

Vancouver:

Júnior CAA. Simulação multiagentes aplicada ao planejamento da produção florestal sustentável. [Internet] [Thesis]. Universidade Federal de Viçosa; 2012. [cited 2021 Jan 22]. Available from: http://www.tede.ufv.br/tedesimplificado/tde_busca/arquivo.php?codArquivo=4108.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Júnior CAA. Simulação multiagentes aplicada ao planejamento da produção florestal sustentável. [Thesis]. Universidade Federal de Viçosa; 2012. Available from: http://www.tede.ufv.br/tedesimplificado/tde_busca/arquivo.php?codArquivo=4108

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universidade do Rio Grande do Sul

12. Santos, Daniela Scherer dos. Bee clustering : um algoritmo para agrupamento de dados inspirado em inteligência de enxames.

Degree: 2009, Universidade do Rio Grande do Sul

URL: http://hdl.handle.net/10183/18249

►

Agrupamento de dados é o processo que consiste em dividir um conjunto de dados em grupos de forma que dados semelhantes entre si permaneçam no… (more)

▼

Agrupamento de dados é o processo que consiste em dividir um conjunto de dados em grupos de forma que dados semelhantes entre si permaneçam no mesmo grupo enquanto que dados dissimilares sejam alocados em grupos diferentes. Técnicas tradicionais de agrupamento de dados têm sido usualmente desenvolvidas de maneira centralizada dependendo assim de estruturas que devem ser acessadas e modificadas a cada passo do processo de agrupamento. Além disso, os resultados gerados por tais métodos são dependentes de informações que devem ser fornecidas a priori como por exemplo número de grupos, tamanho do grupo ou densidade mínima/máxima permitida para o grupo. O presente trabalho visa propor o bee clustering, um algoritmo distribuído inspirado principalmente em técnicas de inteligência de enxames como organização de colônias de abelhas e alocação de tarefas em insetos sociais, desenvolvido com o objetivo de resolver o problema de agrupamento de dados sem a necessidade de pistas sobre o resultado desejado ou inicialização de parâmetros complexos. O bee clustering é capaz de formar grupos de agentes de maneira distribuída, uma necessidade típica em cenários de sistemas multiagente que exijam capacidade de auto-organização sem controle centralizado. Os resultados obtidos mostram que é possível atingir resultados comparáveis as abordagens centralizadas.

Clustering can be defined as a set of techniques that separate a data set into groups of similar objects. Data items within the same group are more similar than objects of different groups. Traditional clustering methods have been usually developed in a centralized fashion. One reason for this is that this form of clustering relies on data structures that must be accessed and modified at each step of the clustering process. Another issue with classical clustering methods is that they need some hints about the target clustering. These hints include for example the number of clusters, the expected cluster size, or the minimum density of clusters. In this work we propose a clustering algorithm that is inspired by swarm intelligence techniques such as the organization of bee colonies and task allocation among social insects. Our proposed algorithm is developed in a decentralized fashion without any initial information about number of classes, number of partitions, and size of partition, and without the need of complex parameters. The bee clustering algorithm is able to form groups of agents in a distributed way, a typical necessity in multiagent scenarios that require self-organization without central control. The performance of our algorithm shows that it is possible to achieve results that are comparable to those from centralized approaches.

Advisors/Committee Members: Bazzan, Ana Lucia Cetertich.

Subjects/Keywords: Inteligência artificial; Artificial intelligence; Multiagent systems; Sistemas multiagentes; Insetos sociais; Swarm intelligence; Distributed clustering; Bee colony organization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Santos, D. S. d. (2009). Bee clustering : um algoritmo para agrupamento de dados inspirado em inteligência de enxames. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/18249

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Santos, Daniela Scherer dos. “Bee clustering : um algoritmo para agrupamento de dados inspirado em inteligência de enxames.” 2009. Thesis, Universidade do Rio Grande do Sul. Accessed January 22, 2021. http://hdl.handle.net/10183/18249.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Santos, Daniela Scherer dos. “Bee clustering : um algoritmo para agrupamento de dados inspirado em inteligência de enxames.” 2009. Web. 22 Jan 2021.

Vancouver:

Santos DSd. Bee clustering : um algoritmo para agrupamento de dados inspirado em inteligência de enxames. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2009. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10183/18249.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Santos DSd. Bee clustering : um algoritmo para agrupamento de dados inspirado em inteligência de enxames. [Thesis]. Universidade do Rio Grande do Sul; 2009. Available from: http://hdl.handle.net/10183/18249

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Vermont

13. Wagy, Mark David. Enabling Machine Science through Distributed Human Computing.

Degree: PhD, Computer Science, 2016, University of Vermont

URL: https://scholarworks.uvm.edu/graddis/618

► Distributed human computing techniques have been shown to be effective ways of accessing the problem-solving capabilities of a large group of anonymous individuals over… (more)

Subjects/Keywords: Artificial Intelligence; Crowdsourcing; Distributed Systems; Human Computation; Human Computer Interaction; Machine Learning; Artificial Intelligence and Robotics; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wagy, M. D. (2016). Enabling Machine Science through Distributed Human Computing. (Doctoral Dissertation). University of Vermont. Retrieved from https://scholarworks.uvm.edu/graddis/618

Chicago Manual of Style (16^th Edition):

Wagy, Mark David. “Enabling Machine Science through Distributed Human Computing.” 2016. Doctoral Dissertation, University of Vermont. Accessed January 22, 2021. https://scholarworks.uvm.edu/graddis/618.

MLA Handbook (7^th Edition):

Wagy, Mark David. “Enabling Machine Science through Distributed Human Computing.” 2016. Web. 22 Jan 2021.

Vancouver:

Wagy MD. Enabling Machine Science through Distributed Human Computing. [Internet] [Doctoral dissertation]. University of Vermont; 2016. [cited 2021 Jan 22]. Available from: https://scholarworks.uvm.edu/graddis/618.

Council of Science Editors:

Wagy MD. Enabling Machine Science through Distributed Human Computing. [Doctoral Dissertation]. University of Vermont; 2016. Available from: https://scholarworks.uvm.edu/graddis/618

Vanderbilt University

14. Sengupta, Saptarshi. QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives.

Degree: PhD, Electrical Engineering, 2019, Vanderbilt University

URL: http://hdl.handle.net/1803/14438

► With sensor fusion and data-driven approaches taking center stage in ubiquitous computing, customized and application-specific optimization methods are increasingly important. The interest follows in part… (more)

▼ With sensor fusion and data-driven approaches taking center stage in ubiquitous computing, customized and application-specific optimization methods are increasingly important. The interest follows in part from the limitations of specific optimization methods implied by the No Free Lunch Theorem. Applications of computational intelligence are growing exponentially with the widespread availability of increasingly powerful computers. This has made feasible the mimicry of highly interactive multi-agent models of natural systems that solve complicated problems while remaining stable. The emergent behaviors arising in such systems hint at novel methods of optimization that can find solutions to machine learning problems of similar complexity. This dissertation introduces a social, agent-based (swarm) intelligence algorithm viz. the Quantum Double Delta Swarm (QDDS). It is modeled after the mechanism of convergence, under an attractive potential field, to the center of a single well in a double Dirac delta potential-well problem. The swarming model developed here extends the well-known Quantum-behaved Particle Swarm Optimization (QPSO) algorithm to the more stable, double well configuration for optimal solutions of complex engineering design problems. Theoretical foundations and experimental illustrations lead to applications of the model to find solutions of problems in intrinsically high-dimensional feature spaces. In addition, the effects of chaos on the exploratory capacity of the algorithm are studied by including a Chebyshev map driven exploration (C-QDDS) step and benchmarking the results. Visualization of the process is enabled by tracking the trajectory of the best performing agent in each iteration over all episodes across benchmark contours. Under general assumptions common to random search convergence proofs the dynamical limitations of this model’s convergence are critically analyzed. Finally, results are demonstrated on: a) the multidimensional finite impulse response (FIR) filter design problem and b) Neuro-evolution, specifically using a two-layer neural architecture where the C-QDDS search mutates candidate architectures whose weights and biases are then trained using gradient-free swarming. Advisors/Committee Members: Douglas Hardin (committee member), Nilanjan Sarkar (committee member), Don Mitchell Wilkes (committee member), Kazuhiko Kawamura (committee member), Alan Peters (Committee Chair).

Subjects/Keywords: quantum-inspired computational intelligence; stochastic optimization; particle swarm optimization; multiagent systems; swarm intelligence; distributed artificial intelligence

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APA (6^th Edition):

Sengupta, S. (2019). QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14438

Chicago Manual of Style (16^th Edition):

Sengupta, Saptarshi. “QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives.” 2019. Doctoral Dissertation, Vanderbilt University. Accessed January 22, 2021. http://hdl.handle.net/1803/14438.

MLA Handbook (7^th Edition):

Sengupta, Saptarshi. “QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives.” 2019. Web. 22 Jan 2021.

Vancouver:

Sengupta S. QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives. [Internet] [Doctoral dissertation]. Vanderbilt University; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/1803/14438.

Council of Science Editors:

Sengupta S. QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives. [Doctoral Dissertation]. Vanderbilt University; 2019. Available from: http://hdl.handle.net/1803/14438

University of California – Berkeley

15. Sparks, Evan Randall. End-to-End Large Scale Machine Learning with KeystoneML.

Degree: Computer Science, 2016, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/4r73d9rh

► The rise of data center computing and Internet-connected devices has led to an unparalleled explosion in the volumes of data collected across a multitude of… (more)

Subjects/Keywords: Computer science; advanced analytics; artificial intelligence; big data; distributed machine learning; large scale; machine learning

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APA (6^th Edition):

Sparks, E. R. (2016). End-to-End Large Scale Machine Learning with KeystoneML. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/4r73d9rh

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sparks, Evan Randall. “End-to-End Large Scale Machine Learning with KeystoneML.” 2016. Thesis, University of California – Berkeley. Accessed January 22, 2021. http://www.escholarship.org/uc/item/4r73d9rh.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sparks, Evan Randall. “End-to-End Large Scale Machine Learning with KeystoneML.” 2016. Web. 22 Jan 2021.

Vancouver:

Sparks ER. End-to-End Large Scale Machine Learning with KeystoneML. [Internet] [Thesis]. University of California – Berkeley; 2016. [cited 2021 Jan 22]. Available from: http://www.escholarship.org/uc/item/4r73d9rh.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sparks ER. End-to-End Large Scale Machine Learning with KeystoneML. [Thesis]. University of California – Berkeley; 2016. Available from: http://www.escholarship.org/uc/item/4r73d9rh

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California

16. Yeoh, William. Speeding up distributed constraint optimization search algorithms.

Degree: PhD, Computer Science, 2010, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/393626/rec/6011

► Distributed constraint optimization (DCOP) is a model where several agents coordinate with each other to take on values so as to minimize the sum of… (more)

▼ Distributed constraint optimization (DCOP) is a model where several agents coordinate with each other to take on values so as to minimize the sum of the resulting constraint costs, which are dependent on the values of the agents. This model is becoming popular for formulating and solving agent-coordination problems. As a result, researchers have developed a class of DCOP algorithms that use search techniques. For example, Asynchronous Distributed Constraint Optimization (ADOPT) is one of the pioneering DCOP search algorithms that has been widely extended. Since solving DCOP problems optimally is NP-hard, solving large problems efficiently becomes an issue.; DCOP search algorithms can be viewed as distributed versions of centralized search algorithms. Therefore, I hypothesize that one can speed up DCOP search algorithms by applying insights gained from centralized search algorithms, specifically (1) by using an appropriate search strategy, (2) by sacrificing solution optimality, (3) by using more memory, and (4) by reusing information gained from solving similar DCOP problems. However, DCOP search algorithms are sufficiently different from centralized search algorithms that these insights cannot be trivially applied.; To validate my hypotheses: (1) I introduce Branch-and-Bound ADOPT (BnB-ADOPT), an extension of ADOPT that changes the search strategy of ADOPT from memory-bounded best-first search to depth-first branch-and-bound search, resulting in one order of magnitude speedup. These results validate my hypothesis that DCOP search algorithms that employ depth-first branch-and-bound search can be faster than DCOP search algorithms that employ memory-bounded best-first search. (2) I introduce an approximation mechanism that uses weighted heuristic values to trade off solution costs for smaller runtimes. This approximation mechanism allows ADOPT and BnB-ADOPT to terminate faster with larger weights, validating my hypothesis that DCOP search algorithms that use weighted heuristic values can have runtimes that decrease as larger weights are used. Additionally, the new approximation mechanism provides relative error bounds and thus complements existing approximation mechanisms that only provide absolute error bounds. (3) I introduce the MaxPriority, MaxEffort and MaxUtility DCOP-specific caching schemes, which allow ADOPT and BnB-ADOPT to cache DCOP-specific information when they have more memory available and terminate faster with larger amounts of memory. Experimental results show that the MaxEffort and MaxUtility schemes speed up ADOPT more than the currently used generic caching schemes, and the MaxPriority scheme speeds up BnB-ADOPT at least as much as the currently used generic caching schemes. Therefore, these results validate my hypothesis that DCOP-specific caching schemes can reduce the runtime of DCOP search algorithms at least as much as the currently used generic caching schemes.; (4) I introduce an incremental procedure and an incremental pseudo-tree reconstruction algorithm that allow ADOPT and BnB-ADOPT to… Advisors/Committee Members: Koenig, Sven (Committee Chair), Dessouky, Maged M. (Committee Member), Sukhatme, Gaurav S. (Committee Member), Tambe, Milind (Committee Member), Yokoo, Makoto (Committee Member).

Subjects/Keywords: artificial intelligence; multiagent systems; distributed constraint optimization; distributed search

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yeoh, W. (2010). Speeding up distributed constraint optimization search algorithms. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/393626/rec/6011

Chicago Manual of Style (16^th Edition):

Yeoh, William. “Speeding up distributed constraint optimization search algorithms.” 2010. Doctoral Dissertation, University of Southern California. Accessed January 22, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/393626/rec/6011.

MLA Handbook (7^th Edition):

Yeoh, William. “Speeding up distributed constraint optimization search algorithms.” 2010. Web. 22 Jan 2021.

Vancouver:

Yeoh W. Speeding up distributed constraint optimization search algorithms. [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2021 Jan 22]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/393626/rec/6011.

Council of Science Editors:

Yeoh W. Speeding up distributed constraint optimization search algorithms. [Doctoral Dissertation]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/393626/rec/6011

Universidade do Rio Grande do Sul

17. Epstein, Daniel. Um algoritmo distribuído para resolução do problema de geração de estruturas de coalizão com presença de externalidades.

Degree: 2013, Universidade do Rio Grande do Sul

URL: http://hdl.handle.net/10183/103391

► Uma importante parte de um sistema multiagente é o seu mecanismo de coordenação que permite que os agentes possam agir de maneira coesa em direção… (more)

▼ Uma importante parte de um sistema multiagente é o seu mecanismo de coordenação que permite que os agentes possam agir de maneira coesa em direção aos seus objetivos, sejam eles individuais ou coletivos. Um agente pode optar por cooperar para atingir um determinado objetivo que seria inalcançável através de ações individuais, para realizar uma tarefa de maneira mais eficiente ou simplesmente porque ele foi projetado para tal. Em todos os casos, a formação de coalizões (grupos de agentes que concordam em coordenar suas ações em torno de um objetivo comum) é uma questão fundamental. O problema de geração de estruturas de coalizão entre agentes (conjunto de todas as combinações de coalizões) é um tópico de pesquisa que recebeu muita atenção principalmente na resolução do problema quando considerado como um jogo de função característica, onde o valor das coalizões independe dos agentes que não estão presentes nela. Essa abordagem, apesar de ser indicada para muitos tipos de problema, não cobre toda a área de pesquisa do assunto, visto que em muitos casos a criação de uma coalizão irá afetar os demais agentes do sistema. Quando o sistema possui agentes com objetivos sobrepostos ou contrários, uma coalizão cujos recursos são destinados a completar tais objetivos irá influenciar as demais coalizões desse sistema. Essa influência se chama externalidade e, nesses casos, o problema de formação de estruturas de coalizão deve ser tratado como um jogo de partição. Apesar das pesquisas na área de jogos de partição serem recentes, elas trazem resultados promissores e há alguns poucos algoritmos já desenvolvidos para buscar soluções a esse problema. A busca pela melhor estrutura de coalizão geralmente demanda que seja calculado o valor de todas possíveis coalizões, a fim de se encontrar aquele conjunto cuja soma dos valores das coalizões forneça o melhor resultado. Esse processo requer um alto número de computações e de memória, devido à natureza exponencial do problema. Assim, ao invés de apenas um agente central realizar todas as operações, é mais eficiente do ponto de vista do uso de recursos computacionais distribuir essas operações entres os diversos agentes presentes no sistema. Além dos benefícios computacionais, distribuir o processo de busca pela melhor estrutura de coalizão permitiria trabalhar com questões como privacidade e tolerância a falhas, tendo em vista que as informações não estão concentradas em um único agente. Apesar disso, não há na literatura qualquer algoritmo capaz de solucionar o problema de geração de estrutura de coalizão em ambientes distribuídos e que sejam modelados como jogos de partição. A proposta desse trabalho é utilizar a fundamentação teórica existente acerca do problema de formação de estruturas de coalizão (modelados tanto como jogos de função característica quanto como jogos de partição) para criar um algoritmo distribuído capaz de encontrar a estrutura de coalizão ótima em ambientes que possuam externalidade. Esse algoritmo utiliza como base a ordenação das coalizões e dos agentes para… Advisors/Committee Members: Bazzan, Ana Lucia Cetertich.

Subjects/Keywords: Artificial intelligence; Inteligência artificial; Sistemas multiagentes; Multi-agent system; Game theory; Distributed algorithms; Coalition structure generation; Externality

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Epstein, D. (2013). Um algoritmo distribuído para resolução do problema de geração de estruturas de coalizão com presença de externalidades. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/103391

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Epstein, Daniel. “Um algoritmo distribuído para resolução do problema de geração de estruturas de coalizão com presença de externalidades.” 2013. Thesis, Universidade do Rio Grande do Sul. Accessed January 22, 2021. http://hdl.handle.net/10183/103391.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Epstein, Daniel. “Um algoritmo distribuído para resolução do problema de geração de estruturas de coalizão com presença de externalidades.” 2013. Web. 22 Jan 2021.

Vancouver:

Epstein D. Um algoritmo distribuído para resolução do problema de geração de estruturas de coalizão com presença de externalidades. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2013. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10183/103391.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Epstein D. Um algoritmo distribuído para resolução do problema de geração de estruturas de coalizão com presença de externalidades. [Thesis]. Universidade do Rio Grande do Sul; 2013. Available from: http://hdl.handle.net/10183/103391

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

De Montfort University

18. Prueller, Hans. Distributed online machine learning for mobile care systems.

Degree: PhD, 2014, De Montfort University

URL: http://hdl.handle.net/2086/10875

► Telecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and… (more)

▼ Telecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and even health outcomes for patients. In addition, they allow significant cost savings for adult care by reducing the needs for medical staff. A common drawback of current Mobile Care Systems is that they are rather stationary in most cases and firmly installed in patients’ houses or flats, which makes them stay very near to or even in their homes. There is also an upcoming second category of Mobile Care Systems which are portable without restricting the moving space of the patients, but with the major drawback that they have either very limited computational abilities and only a rather low classification quality or, which is most frequently, they only have a very short runtime on battery and therefore indirectly restrict the freedom of moving of the patients once again. These drawbacks are inherently caused by the restricted computational resources and mainly the limitations of battery based power supply of mobile computer systems. This research investigates the application of novel Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the operation of 2 Mobile Care Systems. As a result, based on the Evolving Connectionist Systems (ECoS) paradigm, an innovative approach for a highly efficient and self-optimising distributed online machine learning algorithm called MECoS - Moving ECoS - is presented. It balances the conflicting needs of providing a highly responsive complex and distributed online learning classification algorithm by requiring only limited resources in the form of computational power and energy. This approach overcomes the drawbacks of current mobile systems and combines them with the advantages of powerful stationary approaches. The research concludes that the practical application of the presented MECoS algorithm offers substantial improvements to the problems as highlighted within this thesis.

Subjects/Keywords: 006.3; distributed learning; online learning; artificial intelligence; telecare; mobile patient care; ecos; evolving artificial neural networks

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APA (6^th Edition):

Prueller, H. (2014). Distributed online machine learning for mobile care systems. (Doctoral Dissertation). De Montfort University. Retrieved from http://hdl.handle.net/2086/10875

Chicago Manual of Style (16^th Edition):

Prueller, Hans. “Distributed online machine learning for mobile care systems.” 2014. Doctoral Dissertation, De Montfort University. Accessed January 22, 2021. http://hdl.handle.net/2086/10875.

MLA Handbook (7^th Edition):

Prueller, Hans. “Distributed online machine learning for mobile care systems.” 2014. Web. 22 Jan 2021.

Vancouver:

Prueller H. Distributed online machine learning for mobile care systems. [Internet] [Doctoral dissertation]. De Montfort University; 2014. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/2086/10875.

Council of Science Editors:

Prueller H. Distributed online machine learning for mobile care systems. [Doctoral Dissertation]. De Montfort University; 2014. Available from: http://hdl.handle.net/2086/10875

University of North Texas

19. Balamuru, Vinay Gopal. The Role of Intelligent Mobile Agents in Network Management and Routing.

Degree: 2000, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc2736/

► In this research, the application of intelligent mobile agents to the management of distributed network environments is investigated. Intelligent mobile agents are programs which can… (more)

Subjects/Keywords: Electronic data processing – Distributed processing.; Distributed artificial intelligence.; Intelligent agents (Computer software); distributed network environments; intelligent mobile agents; distributed artificial intelligence

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Balamuru, V. G. (2000). The Role of Intelligent Mobile Agents in Network Management and Routing. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc2736/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Balamuru, Vinay Gopal. “The Role of Intelligent Mobile Agents in Network Management and Routing.” 2000. Thesis, University of North Texas. Accessed January 22, 2021. https://digital.library.unt.edu/ark:/67531/metadc2736/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Balamuru, Vinay Gopal. “The Role of Intelligent Mobile Agents in Network Management and Routing.” 2000. Web. 22 Jan 2021.

Vancouver:

Balamuru VG. The Role of Intelligent Mobile Agents in Network Management and Routing. [Internet] [Thesis]. University of North Texas; 2000. [cited 2021 Jan 22]. Available from: https://digital.library.unt.edu/ark:/67531/metadc2736/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Balamuru VG. The Role of Intelligent Mobile Agents in Network Management and Routing. [Thesis]. University of North Texas; 2000. Available from: https://digital.library.unt.edu/ark:/67531/metadc2736/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

20. Klaimi, Joelle. Gestion multi-agents des smart grids intégrant un système de stockage : cas résidentiel : Multi-agent management of smart grids integrating a storage system : residential case.

Degree: Docteur es, Ingénierie Sociotechnique des Connaissances, des Réseaux et du Développement Durable, 2017, Troyes; Université libanaise

URL: http://www.theses.fr/2017TROY0006

►

Cette thèse s’intéresse à la gestion décentralisée à l’aide des systèmes multi-agents de l’énergie, notamment de sources renouvelables, dans le contexte des réseaux électriques intelligents… (more)

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Cette thèse s’intéresse à la gestion décentralisée à l’aide des systèmes multi-agents de l’énergie, notamment de sources renouvelables, dans le contexte des réseaux électriques intelligents (smart grids). Nos travaux de recherche visent à minimiser la facture énergétique des consommateurs en se focalisant sur deux verrous essentiels que nous nous proposons de lever : (1) résoudre le problème de l’intermittence des énergies renouvelables; (2) minimiser les pertes d’énergie. Pour pallier le problème d’intermittence des énergies renouvelables et dans le but de maintenir un coût énergétique peu onéreux même lors des pics d’utilisation, nous avons intégré un système de stockage intelligent. Nous avons, en effet, proposé des algorithmes permettant d’utiliser les systèmes de stockage intelligents et la négociation multi-agents pour réduire la facture énergétique tout en conservant un taux de décharge minimal de la batterie et une perte énergétique minimale. La validation par simulation de nos contributions a montré que celles-ci répondent aux enjeux identifiés, notamment en réduisant le coût de l’énergie pour les consommateurs en comparaison aux travaux de l’état de l’art.

This thesis focuses on the decentralized management using multi-agent systems of energy, including renewable energy sources, in the smart grid context. Our research aims to minimize consumers’ energy bills by answering two key challenges: (1) handle the problem of intermittency of renewable energy sources; (2) reduce energy losses. To overcome the problem of renewable resources intermittency and in order to minimize energy costs even during peak hours, we integrated an intelligent storage system. To this end, we propose many algorithms in order to use intelligent storage systems and multi-agent negotiation algorithm to reduce energy cost while maintaining a minimal discharge rate of the battery and minimal energy loss. The validation of our contributions has shown that our proposals respond to the identified challenges, including reducing the cost of energy for consumers, in comparison to the state of the art.

Advisors/Committee Members: Jrad, Akil (thesis director), Merghem, Leïla (thesis director), Rahim-Amoud, Rana (thesis director).

Subjects/Keywords: Réseaux électriques intelligents; Energies renouvelables; Intelligence artificielle répartie; Systèmes à paramètres répartis; Smart power grids; Renewable energy sources; Distributed artificial intelligence; Distributed parameter systems; 621.319

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Klaimi, J. (2017). Gestion multi-agents des smart grids intégrant un système de stockage : cas résidentiel : Multi-agent management of smart grids integrating a storage system : residential case. (Doctoral Dissertation). Troyes; Université libanaise. Retrieved from http://www.theses.fr/2017TROY0006

Chicago Manual of Style (16^th Edition):

Klaimi, Joelle. “Gestion multi-agents des smart grids intégrant un système de stockage : cas résidentiel : Multi-agent management of smart grids integrating a storage system : residential case.” 2017. Doctoral Dissertation, Troyes; Université libanaise. Accessed January 22, 2021. http://www.theses.fr/2017TROY0006.

MLA Handbook (7^th Edition):

Klaimi, Joelle. “Gestion multi-agents des smart grids intégrant un système de stockage : cas résidentiel : Multi-agent management of smart grids integrating a storage system : residential case.” 2017. Web. 22 Jan 2021.

Vancouver:

Klaimi J. Gestion multi-agents des smart grids intégrant un système de stockage : cas résidentiel : Multi-agent management of smart grids integrating a storage system : residential case. [Internet] [Doctoral dissertation]. Troyes; Université libanaise; 2017. [cited 2021 Jan 22]. Available from: http://www.theses.fr/2017TROY0006.

Council of Science Editors:

Klaimi J. Gestion multi-agents des smart grids intégrant un système de stockage : cas résidentiel : Multi-agent management of smart grids integrating a storage system : residential case. [Doctoral Dissertation]. Troyes; Université libanaise; 2017. Available from: http://www.theses.fr/2017TROY0006

Université Montpellier II

21. Wahbi, Mohamed. Algorithms and ordering heuristics for distributed constraint satisfaction problems : Algorithmes de résolution et heuristiques d'ordonnancement pour les problèmes de satisfaction de contraintes distribués.

Degree: Docteur es, Informatique, 2012, Université Montpellier II

URL: http://www.theses.fr/2012MON20028

►

Les problèmes de satisfaction de contraintes distribués (DisCSP) permettent de formaliser divers problèmes qui se situent dans l'intelligence artificielle distribuée. Ces problèmes consistent à trouver… (more)

▼

Les problèmes de satisfaction de contraintes distribués (DisCSP) permettent de formaliser divers problèmes qui se situent dans l'intelligence artificielle distribuée. Ces problèmes consistent à trouver une combinaison cohérente des actions de plusieurs agents. Durant cette thèse nous avons apporté plusieurs contributions dans le cadre des DisCSPs. Premièrement, nous avons proposé le Nogood-Based Asynchronous Forward-Checking (AFC-ng). Dans AFC-ng, les agents utilisent les nogoods pour justifier chaque suppression d'une valeur du domaine de chaque variable. Outre l'utilisation des nogoods, plusieurs backtracks simultanés venant de différents agents vers différentes destinations sont autorisés. En deuxième lieu, nous exploitons les caractéristiques intrinsèques du réseau de contraintes pour exécuter plusieurs processus de recherche AFC-ng d'une manière asynchrone à travers chaque branche du pseudo-arborescence obtenu à partir du graphe de contraintes dans l'algorithme Asynchronous Forward-Checking Tree (AFC-tree). Puis, nous proposons deux nouveaux algorithmes de recherche synchrones basés sur le même mécanisme que notre AFC-ng. Cependant, au lieu de maintenir le forward checking sur les agents non encore instanciés, nous proposons de maintenir la consistance d'arc. Ensuite, nous proposons Agile Asynchronous Backtracking (Agile-ABT), un algorithme de changement d'ordre asynchrone qui s'affranchit des restrictions habituelles des algorithmes de backtracking asynchrone. Puis, nous avons proposé une nouvelle méthode correcte pour comparer les ordres dans ABT_DO-Retro. Cette méthode détermine l'ordre le plus pertinent en comparant les indices des agents dès que les compteurs d'une position donnée dans le timestamp sont égaux. Finalement, nous présentons une nouvelle version entièrement restructurée de la plateforme DisChoco pour résoudre les problèmes de satisfaction et d'optimisation de contraintes distribués.

Distributed Constraint Satisfaction Problems (DisCSP) is a general framework for solving distributed problems. DisCSP have a wide range of applications in multi-agent coordination. In this thesis, we extend the state of the art in solving the DisCSPs by proposing several algorithms. Firstly, we propose the Nogood-Based Asynchronous Forward Checking (AFC-ng), an algorithm based on Asynchronous Forward Checking (AFC). However, instead of using the shortest inconsistent partial assignments, AFC-ng uses nogoods as justifications of value removals. Unlike AFC, AFC-ng allows concurrent backtracks to be performed at the same time coming from different agents having an empty domain to different destinations. Then, we propose the Asynchronous Forward-Checking Tree (AFC- tree). In AFC-tree, agents are prioritized according to a pseudo-tree arrangement of the constraint graph. Using this priority ordering, AFC-tree performs multiple AFC-ng processes on the paths from the root to the leaves of the pseudo-tree. Next, we propose to maintain arc consistency asynchronously on the future agents instead of only maintaining forward…

Advisors/Committee Members: Bessière, Christian (thesis director), Bouyakhf, El-Houssine (thesis director).

Subjects/Keywords: Intelligence Artificielle; Intelligence Artificielle distribuée; Problèmes de satisfaction de contraintes distribués (DisCSP); Heuristiques d'ordonnancement; Maintenance de la consistance d'arc; DisChoco; Artificial Intelligence; Distributed Artificial Intelligence; Distributed Constraint Satisfaction (DisCSP); Reordering; Maintaining Arc Consistency; DisChoco

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wahbi, M. (2012). Algorithms and ordering heuristics for distributed constraint satisfaction problems : Algorithmes de résolution et heuristiques d'ordonnancement pour les problèmes de satisfaction de contraintes distribués. (Doctoral Dissertation). Université Montpellier II. Retrieved from http://www.theses.fr/2012MON20028

Chicago Manual of Style (16^th Edition):

Wahbi, Mohamed. “Algorithms and ordering heuristics for distributed constraint satisfaction problems : Algorithmes de résolution et heuristiques d'ordonnancement pour les problèmes de satisfaction de contraintes distribués.” 2012. Doctoral Dissertation, Université Montpellier II. Accessed January 22, 2021. http://www.theses.fr/2012MON20028.

MLA Handbook (7^th Edition):

Wahbi, Mohamed. “Algorithms and ordering heuristics for distributed constraint satisfaction problems : Algorithmes de résolution et heuristiques d'ordonnancement pour les problèmes de satisfaction de contraintes distribués.” 2012. Web. 22 Jan 2021.

Vancouver:

Wahbi M. Algorithms and ordering heuristics for distributed constraint satisfaction problems : Algorithmes de résolution et heuristiques d'ordonnancement pour les problèmes de satisfaction de contraintes distribués. [Internet] [Doctoral dissertation]. Université Montpellier II; 2012. [cited 2021 Jan 22]. Available from: http://www.theses.fr/2012MON20028.

Council of Science Editors:

Wahbi M. Algorithms and ordering heuristics for distributed constraint satisfaction problems : Algorithmes de résolution et heuristiques d'ordonnancement pour les problèmes de satisfaction de contraintes distribués. [Doctoral Dissertation]. Université Montpellier II; 2012. Available from: http://www.theses.fr/2012MON20028

Universidade do Porto

22. Malheiro, Maria Benedita Campos Neves. Methodologies for Belief Revision in Multi-agent Systems.

Degree: Engenharia Electrotécnica e de Computadores, 1999, Universidade do Porto

URL: http://dited.bn.pt:80/29534

► The goal of this thesis is twofold: first, we want to present the distributed belief accommodation and revision model for multi-agent systems that has been… (more)

▼ The goal of this thesis is twofold: first, we want to present the distributed belief accommodation and revision model for multi-agent systems that has been developed and, second, we wish to show its applicability to an appropriate domain. The Distributed Belief Accommodation & Revision model, called DeBAteR model, was developed for co-operative heterogeneous multi-agent systems used to model inherently dynamic distributed problems. In these systems, although the agents are able to detect changes both in the environment and in the problem specifications, each agent has only a partial view of the global picture. As a result the information that represents the current state of affairs is dynamic, incomplete and sometimes uncertain. This non-monotonic kind of data is called beliefs ? a belief is a piece of data that is held as correct as long as no contradicting evidence is found or presented. Each agent is expected to include an assumption based truth maintenance module for representing properly this type of data. Our main effort was concentrated on the task of maintaining the system's information, which consists of updating, revising and accommodating the represented beliefs. Belief updating is necessary for including the changes detected by the agents both in the environment and/or in the problem specifications. Belief revision is essential for solving the inconsistencies detected among the represented beliefs. Belief accommodation and revision is crucial for integrating the multiple disparate perspectives regarding the same data items, which may occur whenever there is overlap of expertise domains between the agents. In order to solve the information conflicts that result from the detection of inconsistencies between distinct beliefs or within multi-perspective beliefs we conceived the DeBATeR model. The DeBAteR is fully distributed, provides individual belief autonomy and is made of two methodologies: the pro-active belief accommodation and revision methodology and the delayed belief revision methodology. Whilst the first methodology is used to solve domain independent conflicts, the second methodology was devised for solving domain dependent conflicts. Both methodologies use argumentation for, in the case of the domain independent conflicts, choosing the most credible perspective between the existing multiple perspectives of a belief, and, in the case of the domain dependent conflicts, finding the best alternative belief support set for the affected concepts. These methodologies are distributed and their scope may be internal or collective. The DeBAteR model main contributions are: (i) the pro-active methodology conceived for solving domain independent conflicts and (ii) the capability, not only to represent and maintain individual beliefs and joint beliefs, but also to accommodate, rationally maintain and make use of multi-perspective beliefs. Finally, we describe the developed decision support multi-agent system for choosing adequate project locations, called DIPLOMAT ? Dynamic and Interactive Project Location… Advisors/Committee Members: Oliveira, Eugénio da Costa.

Subjects/Keywords: Multiagent systems; Distributed Artificial Intelligence; Nonmonotonic reasoning and belief revision; Artificial intelligence

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Malheiro, M. B. C. N. (1999). Methodologies for Belief Revision in Multi-agent Systems. (Doctoral Dissertation). Universidade do Porto. Retrieved from http://dited.bn.pt:80/29534

Chicago Manual of Style (16^th Edition):

Malheiro, Maria Benedita Campos Neves. “Methodologies for Belief Revision in Multi-agent Systems.” 1999. Doctoral Dissertation, Universidade do Porto. Accessed January 22, 2021. http://dited.bn.pt:80/29534.

MLA Handbook (7^th Edition):

Malheiro, Maria Benedita Campos Neves. “Methodologies for Belief Revision in Multi-agent Systems.” 1999. Web. 22 Jan 2021.

Vancouver:

Malheiro MBCN. Methodologies for Belief Revision in Multi-agent Systems. [Internet] [Doctoral dissertation]. Universidade do Porto; 1999. [cited 2021 Jan 22]. Available from: http://dited.bn.pt:80/29534.

Council of Science Editors:

Malheiro MBCN. Methodologies for Belief Revision in Multi-agent Systems. [Doctoral Dissertation]. Universidade do Porto; 1999. Available from: http://dited.bn.pt:80/29534

University of Hong Kong

23. 胡跃冰. Consensus control of multi-agent systems.

Degree: 2011, University of Hong Kong

URL: http://hdl.handle.net/10722/143208

Subjects/Keywords: Intelligent agents (Computer software); Distributed artificial intelligence.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

胡跃冰.. (2011). Consensus control of multi-agent systems. (Thesis). University of Hong Kong. Retrieved from http://hdl.handle.net/10722/143208

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

胡跃冰.. “Consensus control of multi-agent systems.” 2011. Thesis, University of Hong Kong. Accessed January 22, 2021. http://hdl.handle.net/10722/143208.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

胡跃冰.. “Consensus control of multi-agent systems.” 2011. Web. 22 Jan 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

胡跃冰.. Consensus control of multi-agent systems. [Internet] [Thesis]. University of Hong Kong; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10722/143208.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

胡跃冰.. Consensus control of multi-agent systems. [Thesis]. University of Hong Kong; 2011. Available from: http://hdl.handle.net/10722/143208

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

University of Georgia

24. Chandrasekaran, Muthukumaran. Approximate model equivalence for interactive dynamic influence diagrams.

Degree: 2014, University of Georgia

URL: http://hdl.handle.net/10724/26271

► Interactive dynamic influence diagrams (I-DIDs) graphically visualize a sequential decision problem for uncertain settings where multiple agents interact not only amongst themselves but also with… (more)

Subjects/Keywords: Distributed Artificial Intelligence; Multiagent Systems; Decision making; Interactive Dynamic Influence Diagrams; Agent modeling; Behavioral equivalence; Subjective equivalence

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chandrasekaran, M. (2014). Approximate model equivalence for interactive dynamic influence diagrams. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/26271

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chandrasekaran, Muthukumaran. “Approximate model equivalence for interactive dynamic influence diagrams.” 2014. Thesis, University of Georgia. Accessed January 22, 2021. http://hdl.handle.net/10724/26271.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chandrasekaran, Muthukumaran. “Approximate model equivalence for interactive dynamic influence diagrams.” 2014. Web. 22 Jan 2021.

Vancouver:

Chandrasekaran M. Approximate model equivalence for interactive dynamic influence diagrams. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10724/26271.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chandrasekaran M. Approximate model equivalence for interactive dynamic influence diagrams. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/26271

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Johannesburg

25. Heydenrych, Mark. An adaptive multi-agent architecture for critical information infrastructure protection.

Degree: 2014, University of Johannesburg

URL: http://hdl.handle.net/10210/12370

►

M.Sc. (Information Technology)

The purpose of the research presented in this dissertation is to explore the uses of an adaptive multi-agent system for critical information… (more)

Subjects/Keywords: Public works - Computer networks - Security measures; National security - Computer networks - Security measures; Multiagent systems; Distributed artificial intelligence

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Heydenrych, M. (2014). An adaptive multi-agent architecture for critical information infrastructure protection. (Thesis). University of Johannesburg. Retrieved from http://hdl.handle.net/10210/12370

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Heydenrych, Mark. “An adaptive multi-agent architecture for critical information infrastructure protection.” 2014. Thesis, University of Johannesburg. Accessed January 22, 2021. http://hdl.handle.net/10210/12370.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Heydenrych, Mark. “An adaptive multi-agent architecture for critical information infrastructure protection.” 2014. Web. 22 Jan 2021.

Vancouver:

Heydenrych M. An adaptive multi-agent architecture for critical information infrastructure protection. [Internet] [Thesis]. University of Johannesburg; 2014. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10210/12370.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Heydenrych M. An adaptive multi-agent architecture for critical information infrastructure protection. [Thesis]. University of Johannesburg; 2014. Available from: http://hdl.handle.net/10210/12370

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Western Ontario

26. Naikade, Priyanka Prakash. Automated Anomaly Detection and Localization System for a Microservices Based Cloud System.

Degree: 2020, University of Western Ontario

URL: https://ir.lib.uwo.ca/etd/7109

► Context: With an increasing number of applications running on a microservices-based cloud system (such as AWS, GCP, IBM Cloud), it is challenging for the cloud… (more)

▼ Context: With an increasing number of applications running on a microservices-based cloud system (such as AWS, GCP, IBM Cloud), it is challenging for the cloud providers to offer uninterrupted services with guaranteed Quality of Service (QoS) factors. Problem Statement: Existing monitoring frameworks often do not detect critical defects among a large volume of issues generated, thus affecting recovery response times and usage of maintenance human resource. Also, manually tracing the root causes of the issues requires a significant amount of time. Objective: The objective of this work is to: (i) detect performance anomalies, in real-time, through monitoring KPIs (Key Performance Indicators) using distributed tracing events, and (ii) identify their root causes. Proposed Solution: This thesis proposes an automated prediction-based anomaly detection and localization system, capable of detecting performance anomalies of a microservice using machine learning techniques, and determine their root-causes using a localization process. Novelty: The originality of this work lies in the detection process that uses a novel ensemble of a time-series forecasting model and three different unsupervised learning techniques that avoid defining static error thresholds to detect an anomaly and, instead follow a dynamic approach. Experimental Results: The proposed detection system was experimented using different variants of ensembles, evaluated on a real-world production dataset out of which two proposed ensembles outperformed the existing static rule-based approach with average F1-scores of 86% and 84%, average precision scores of 82% and 77% and average recall scores of 91% and 93% respectively across 6 experiments. The proposed detection ensembles were also evaluated on the Numenta Anomaly Benchmark (NAB) datasets and results show that the proposed method performs better than the Numenta’s standard HTM model score. Research Methodology: We adopted an agile methodology to conduct our research in an incremental and iterative fashion. Conclusion: The two proposed ensembles for anomaly detection perform better than the existing static rule-based approach.

Subjects/Keywords: Microservices; Cloud Monitoring; Anomaly Detection; Distributed Tracing; Machine Learning; Performance Anomalies; Artificial Intelligence and Robotics; Other Computer Sciences; Software Engineering

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APA (6^th Edition):

Naikade, P. P. (2020). Automated Anomaly Detection and Localization System for a Microservices Based Cloud System. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/7109

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Naikade, Priyanka Prakash. “Automated Anomaly Detection and Localization System for a Microservices Based Cloud System.” 2020. Thesis, University of Western Ontario. Accessed January 22, 2021. https://ir.lib.uwo.ca/etd/7109.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Naikade, Priyanka Prakash. “Automated Anomaly Detection and Localization System for a Microservices Based Cloud System.” 2020. Web. 22 Jan 2021.

Vancouver:

Naikade PP. Automated Anomaly Detection and Localization System for a Microservices Based Cloud System. [Internet] [Thesis]. University of Western Ontario; 2020. [cited 2021 Jan 22]. Available from: https://ir.lib.uwo.ca/etd/7109.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Naikade PP. Automated Anomaly Detection and Localization System for a Microservices Based Cloud System. [Thesis]. University of Western Ontario; 2020. Available from: https://ir.lib.uwo.ca/etd/7109

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Colorado

27. Komendera, Erik. Precise Assembly of Truss Structures by Distributed Robots.

Degree: PhD, Computer Science, 2014, University of Colorado

URL: https://scholar.colorado.edu/csci_gradetds/91

► Assembly robots have been in operation in industry for decades, predictably repeating the same precise motions in closed workspaces to assemble products cheaply and… (more)

▼ Assembly robots have been in operation in industry for decades, predictably repeating the same precise motions in closed workspaces to assemble products cheaply and in mass quantities. However, in the field, robotic assembly has seen only spurts of progress, and no short-term feasible applications. NASA and the space industry desire robotic construction methods to remove the upper limit on size. Space telescopes are highly desired, but require structural precision on the order of microns. Previous approaches were ruled out because the precisely machined components were expensive, heavy, and prone to failure. The recent advent of cheap robotic swarms has revived interest in academia, but most research requires self-correcting, interlocking components, instead of commodity materials. In this thesis, I describe the Intelligent Precision Jigging paradigm, a solution to the problem of practical robotic assembly, with application to precision truss assembly. Intelligent Precision Jigging Robots (IPJRs) are robots that work in groups of three to incrementally assemble a structure. They set and hold distances with high precision, enabling coarse external manipulators to weld the commodity parts together and perform other tasks. To maximize the utility of the IPJR paradigm to the fullest extent, I present algorithms for finding near-optimal assembly sequences and for implementing Simultaneous Localization and Mapping (SLAM) to maintain an estimate of the assembly process through the accumulation of local strut length measurements. I define a model of truss assembly probability and a minimizing metric based on the covariance trace. I show that structure error grows cubically with node count. I present a three-step approach for generating near-optimal assembly sequences; commencing assembly on a central location of the structure, greedily assembling to minimize the covariance trace, and performing a local search on the space of sequences to swap steps until a local minimum is found. I show that this method consistently generates more precise sequences than any process alone. I then simulate the SLAM method with four different estimators commonly used in for SLAM; a least linear squares approach, the Extended Kalman Filter, the Unscented Kalman Filter, and the Maximum Likelihood Estimator. I show that when nonlinearity in the assembly process is dominant, the Maximum Likelihood Estimator is better than the other estimators, but for space telescopes with precision requirements, all four are functionally equivalent. I also show that when SLAM is used, the difference in covariance trace between sequences is reduced, reducing the need for finding globally optimal sequences. SLAM also mitigates the growth of structure error. Finally, I present the results of physical assembly trials on a telescope truss made of aluminum tubes, assembled by three IPJRs using two methods: an open loop approach, and an MLE-SLAM approach. I show that the MLE-SLAM assembly algorithm works even when the physical… Advisors/Committee Members: Nikolaus Correll, Sriram Sankaranarayanan, Tom Yeh, Eric Frew, Daniel Scheeres.

Subjects/Keywords: distributed robots; optimization; robotic assembly; simultaneous localization and mapping; space telescopes; truss structures; Artificial Intelligence and Robotics; Industrial Engineering; Robotics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Komendera, E. (2014). Precise Assembly of Truss Structures by Distributed Robots. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/91

Chicago Manual of Style (16^th Edition):

Komendera, Erik. “Precise Assembly of Truss Structures by Distributed Robots.” 2014. Doctoral Dissertation, University of Colorado. Accessed January 22, 2021. https://scholar.colorado.edu/csci_gradetds/91.

MLA Handbook (7^th Edition):

Komendera, Erik. “Precise Assembly of Truss Structures by Distributed Robots.” 2014. Web. 22 Jan 2021.

Vancouver:

Komendera E. Precise Assembly of Truss Structures by Distributed Robots. [Internet] [Doctoral dissertation]. University of Colorado; 2014. [cited 2021 Jan 22]. Available from: https://scholar.colorado.edu/csci_gradetds/91.

Council of Science Editors:

Komendera E. Precise Assembly of Truss Structures by Distributed Robots. [Doctoral Dissertation]. University of Colorado; 2014. Available from: https://scholar.colorado.edu/csci_gradetds/91

University of Colorado

28. Kanakia, Anshul Pradip. Response Threshold Based Task Allocation in Multi-Agent Systems Performing Concurrent Benefit Tasks with Limited Information.

Degree: PhD, Computer Science, 2015, University of Colorado

URL: https://scholar.colorado.edu/csci_gradetds/108

► One of the most elusive but important goals of swarm robotics is to reproduce the emergent collaborative behavior observed in natural swarming systems through… (more)

▼ One of the most elusive but important goals of swarm robotics is to reproduce the emergent collaborative behavior observed in natural swarming systems through the use of simple decision rules. Examples of collaborative processes in insect colonies such as foraging, scouting (finding shortest paths) for food, and colony defense involve some form of task allocation among individual agents. The robustness of task completion even after major environmental changes is also observed in natural swarm systems. Ants and bees are often unphased by the fact that the magnitude of a task – such as carrying a heavy piece of food – is unknown to every individual and manage to complete the task elegantly even without such critical knowledge. This robustness property is of paramount importance when recreating natural behavior in artificial systems and I believe the use of decentralized agent based task allocation rules is closely related to this property. I therefore present a novel response threshold based strategy for task allocation in multi-agent systems in this dissertation. I prove, using a well known result from the theory of global games, that under the constraints of imperfect knowledge of the environment and imperfect communication response threshold based task allocation leads to an equilibrium inducing strategy for the swarm system. The importance of this result is to provide a formal mathematical basis for the phenomenological justification currently provided in the field of swarm robotics to mimic biological systems. This result therefore provides both, a hypothesis about the inner workings of a wide range of existing approaches with limited communication between agents in artificial swarm systems and also a formal explanation for threshold based task allocation in social insects. These game theory results lead to a novel continuous response threshold algorithm for multi-agent task allocation that generalizes fixed-group task allocation (stick-pulling experiment) and stochastic team size task allocation. This allows variable team sizes to form at task sites within tolerance limits thereby providing a trade-off between exploration and exploitation. The claims made by theoretical proofs for response threshold based task allocation are backed up by physical experiments using the Droplet swarm robot platform. Further simulation experiments provide a basis of comparison between optimal centralized approaches and hybrid approaches for task allocation where each robot decides whether to participate in a task based on its own noisy sensory input and imperfect knowledge from the system controller. I show that in many real world situations it is often impractical to rely on the assumption of perfect system information for controlling a swarm and that centralized task allocation becomes comparable to a response threshold based policy under the influence of noise. Advisors/Committee Members: Nikolaus Correll, Sriram Sankaranarayanan, Gabe Sibley, Ani Hsieh, Behrouz Touri.

Subjects/Keywords: Distributed Algorithms; Game Theory; Multi-Agent Systems; Robotics; Swarm Robotics; Task Allocation; Artificial Intelligence and Robotics; Theory and Algorithms

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kanakia, A. P. (2015). Response Threshold Based Task Allocation in Multi-Agent Systems Performing Concurrent Benefit Tasks with Limited Information. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/108

Chicago Manual of Style (16^th Edition):

Kanakia, Anshul Pradip. “Response Threshold Based Task Allocation in Multi-Agent Systems Performing Concurrent Benefit Tasks with Limited Information.” 2015. Doctoral Dissertation, University of Colorado. Accessed January 22, 2021. https://scholar.colorado.edu/csci_gradetds/108.

MLA Handbook (7^th Edition):

Kanakia, Anshul Pradip. “Response Threshold Based Task Allocation in Multi-Agent Systems Performing Concurrent Benefit Tasks with Limited Information.” 2015. Web. 22 Jan 2021.

Vancouver:

Kanakia AP. Response Threshold Based Task Allocation in Multi-Agent Systems Performing Concurrent Benefit Tasks with Limited Information. [Internet] [Doctoral dissertation]. University of Colorado; 2015. [cited 2021 Jan 22]. Available from: https://scholar.colorado.edu/csci_gradetds/108.

Council of Science Editors:

Kanakia AP. Response Threshold Based Task Allocation in Multi-Agent Systems Performing Concurrent Benefit Tasks with Limited Information. [Doctoral Dissertation]. University of Colorado; 2015. Available from: https://scholar.colorado.edu/csci_gradetds/108

Duke University

29. Wen, Wei. Efficient and Scalable Deep Learning .

Degree: 2019, Duke University

URL: http://hdl.handle.net/10161/20143

► Deep Neural Networks (DNNs) can achieve accuracy superior to traditional machine learning models, because of their large learning capacity and the availability of large… (more)

▼ Deep Neural Networks (DNNs) can achieve accuracy superior to traditional machine learning models, because of their large learning capacity and the availability of large amounts of labeled data. In general, larger DNNs can obtain higher accuracy. However, there are two obstacles which hinder us building larger DNNs: (1) inference of large DNNs is slow which limits their deployment to small devices; (2) training large DNNs is also slow which slows down research exploration. To remove those obstacles, this dissertation focuses on acceleration of DNN inference and training. To accelerate DNN inference, original DNNs are compressed while keeping original accuracy. More specific, Structurally Sparse Deep Neural Networks (SSDNNs) are proposed to remove neural components. In Convolutional Neural Networks (CNNs), neurons, filters, channels and layers can be removed; in Recurrent Neural Networks (RNNs), hidden sizes can be reduced. The study shows that SSDNNs can achieve higher speedup than sparse DNNs which have non-structured sparsity. Besides SSDNNs, a Force Regularization is proposed to enforce DNNs to lower-rank space, such that DNNs can be decomposed to lower-rank architectures with fewer ranks than traditional methods. The dissertation also demonstrates that SSDNNs and Force Regularization are orthogonal and can be combined for higher speedup. To accelerate DNN training, distributed deep learning is required. However, two problems hinder us using more compute nodes for higher training speed: Communication Bottleneck and Generalization Gap. Communication Bottleneck is that communication time will increase and dominate when the distributed systems scale to many compute nodes. To reduce gradient communication in Stochastic Gradient Descent (SGD), SGD with low-precision gradients (TernGrad) is proposed. Moreover, in distributed deep learning, a large batch size is required to exploit system computing power; unfortunately, accuracy will decrease when the batch size is very large, which is referred to as the Generalization Gap. One hypothesis to explain Generalization Gap is that large-batch SGD sticks at sharp minima. The dissertation proposes a stochastic smoothing (SmoothOut) to escape sharp minima. The dissertation will show that TernGrad overcomes Communication Bottleneck and SmoothOut helps to close the Generalization Gap. Advisors/Committee Members: Li, Hai (advisor), Chen, Yiran (advisor).

Subjects/Keywords: Artificial intelligence; Computer science; Computer engineering; Deep Neural Networks; Distributed Training; Model Compression; Quantization; Sharp Minima; Sparsity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wen, W. (2019). Efficient and Scalable Deep Learning . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/20143

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wen, Wei. “Efficient and Scalable Deep Learning .” 2019. Thesis, Duke University. Accessed January 22, 2021. http://hdl.handle.net/10161/20143.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wen, Wei. “Efficient and Scalable Deep Learning .” 2019. Web. 22 Jan 2021.

Vancouver:

Wen W. Efficient and Scalable Deep Learning . [Internet] [Thesis]. Duke University; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10161/20143.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wen W. Efficient and Scalable Deep Learning . [Thesis]. Duke University; 2019. Available from: http://hdl.handle.net/10161/20143

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Lehigh University

30. Tang, Xiaocheng. Big Data Optimization in Machine Learning.

Degree: PhD, Industrial Engineering, 2016, Lehigh University

URL: https://preserve.lehigh.edu/etd/2837

► Modern machine learning practices at the interface of big data, distributed environment and complex learning objectives post great challenges to designing scalable optimization algorithms with… (more)

Subjects/Keywords: artificial intelligence; convergence; distributed algorithm; machine learning; optimization; structured learning; Engineering; Industrial Engineering; Operations Research, Systems Engineering and Industrial Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tang, X. (2016). Big Data Optimization in Machine Learning. (Doctoral Dissertation). Lehigh University. Retrieved from https://preserve.lehigh.edu/etd/2837

Chicago Manual of Style (16^th Edition):

Tang, Xiaocheng. “Big Data Optimization in Machine Learning.” 2016. Doctoral Dissertation, Lehigh University. Accessed January 22, 2021. https://preserve.lehigh.edu/etd/2837.

MLA Handbook (7^th Edition):

Tang, Xiaocheng. “Big Data Optimization in Machine Learning.” 2016. Web. 22 Jan 2021.

Vancouver:

Tang X. Big Data Optimization in Machine Learning. [Internet] [Doctoral dissertation]. Lehigh University; 2016. [cited 2021 Jan 22]. Available from: https://preserve.lehigh.edu/etd/2837.

Council of Science Editors:

Tang X. Big Data Optimization in Machine Learning. [Doctoral Dissertation]. Lehigh University; 2016. Available from: https://preserve.lehigh.edu/etd/2837

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Open Access Theses and Dissertations