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You searched for +publisher:"Oregon State University" +contributor:("Tadepalli, Prasad"). Showing records 1 – 30 of 42 total matches.

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Oregon State University

1. Raghavan, Aswin. Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces.

Degree: PhD, Computer Science, 2017, Oregon State University

 Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and… (more)

Subjects/Keywords: Planning under uncertainty; Markov processes

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APA (6th Edition):

Raghavan, A. (2017). Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/60559

Chicago Manual of Style (16th Edition):

Raghavan, Aswin. “Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces.” 2017. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/60559.

MLA Handbook (7th Edition):

Raghavan, Aswin. “Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces.” 2017. Web. 18 Apr 2019.

Vancouver:

Raghavan A. Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/60559.

Council of Science Editors:

Raghavan A. Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/60559


Oregon State University

2. Proper, Scott. Scaling multiagent reinforcement learning.

Degree: PhD, Computer Science, 2009, Oregon State University

 Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity or "outcome space" explosion. Multiagent… (more)

Subjects/Keywords: Reinforcement learning; Reinforcement learning

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APA (6th Edition):

Proper, S. (2009). Scaling multiagent reinforcement learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/13662

Chicago Manual of Style (16th Edition):

Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/13662.

MLA Handbook (7th Edition):

Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Web. 18 Apr 2019.

Vancouver:

Proper S. Scaling multiagent reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/13662.

Council of Science Editors:

Proper S. Scaling multiagent reinforcement learning. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/13662


Oregon State University

3. Cho, Seungyun. Modeling and dynamics analysis of P2P networks based on evolutionary games.

Degree: MS, Computer Science, 2011, Oregon State University

 Since free riders in P2P network reduce the system's performance, how to maintain and encourage the nodes' cooperation is an important aspect of P2P related… (more)

Subjects/Keywords: peer to peer networks; Peer-to-peer architecture (Computer networks)

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APA (6th Edition):

Cho, S. (2011). Modeling and dynamics analysis of P2P networks based on evolutionary games. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/19966

Chicago Manual of Style (16th Edition):

Cho, Seungyun. “Modeling and dynamics analysis of P2P networks based on evolutionary games.” 2011. Masters Thesis, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/19966.

MLA Handbook (7th Edition):

Cho, Seungyun. “Modeling and dynamics analysis of P2P networks based on evolutionary games.” 2011. Web. 18 Apr 2019.

Vancouver:

Cho S. Modeling and dynamics analysis of P2P networks based on evolutionary games. [Internet] [Masters thesis]. Oregon State University; 2011. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/19966.

Council of Science Editors:

Cho S. Modeling and dynamics analysis of P2P networks based on evolutionary games. [Masters Thesis]. Oregon State University; 2011. Available from: http://hdl.handle.net/1957/19966


Oregon State University

4. Wynkoop, Michael S. Learning MDP action models via discrete mixture trees.

Degree: MS, Computer Science, 2008, Oregon State University

 This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the… (more)

Subjects/Keywords: Dynamic Bayesian Network; Reinforcement learning (Machine learning)  – Mathematical models

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APA (6th Edition):

Wynkoop, M. S. (2008). Learning MDP action models via discrete mixture trees. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/9096

Chicago Manual of Style (16th Edition):

Wynkoop, Michael S. “Learning MDP action models via discrete mixture trees.” 2008. Masters Thesis, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/9096.

MLA Handbook (7th Edition):

Wynkoop, Michael S. “Learning MDP action models via discrete mixture trees.” 2008. Web. 18 Apr 2019.

Vancouver:

Wynkoop MS. Learning MDP action models via discrete mixture trees. [Internet] [Masters thesis]. Oregon State University; 2008. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/9096.

Council of Science Editors:

Wynkoop MS. Learning MDP action models via discrete mixture trees. [Masters Thesis]. Oregon State University; 2008. Available from: http://hdl.handle.net/1957/9096


Oregon State University

5. Balla, Radha-Krishna. UCT for tactical assault battles in real-time strategy games.

Degree: MS, Computer Science, 2009, Oregon State University

 We consider the problem of tactical assault planning in real-time strategy games where a team of friendly agents must launch an assault on an enemy.… (more)

Subjects/Keywords: Automated Planning; Computer war games  – Computer programs

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APA (6th Edition):

Balla, R. (2009). UCT for tactical assault battles in real-time strategy games. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10680

Chicago Manual of Style (16th Edition):

Balla, Radha-Krishna. “UCT for tactical assault battles in real-time strategy games.” 2009. Masters Thesis, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/10680.

MLA Handbook (7th Edition):

Balla, Radha-Krishna. “UCT for tactical assault battles in real-time strategy games.” 2009. Web. 18 Apr 2019.

Vancouver:

Balla R. UCT for tactical assault battles in real-time strategy games. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/10680.

Council of Science Editors:

Balla R. UCT for tactical assault battles in real-time strategy games. [Masters Thesis]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/10680


Oregon State University

6. Mehta, Neville. Hierarchical structure discovery and transfer in sequential decision problems.

Degree: PhD, Computer Science, 2011, Oregon State University

 Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or… (more)

Subjects/Keywords: hierarchical reinforcement learning; Reinforcement learning

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APA (6th Edition):

Mehta, N. (2011). Hierarchical structure discovery and transfer in sequential decision problems. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/25199

Chicago Manual of Style (16th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/25199.

MLA Handbook (7th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Web. 18 Apr 2019.

Vancouver:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/25199.

Council of Science Editors:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Doctoral Dissertation]. Oregon State University; 2011. Available from: http://hdl.handle.net/1957/25199


Oregon State University

7. Al-Hammali, Hussain Y. Nonuniform Sampling Of Band-limited Functions.

Degree: PhD, Mathematics, 2016, Oregon State University

 In this thesis, we will study certain generalizations of the classical Shannon Sampling Theorem, which allows for the reconstruction of a pi-band-limited, square-integrable function from… (more)

Subjects/Keywords: Nonuniform, Band-limited; Sampling (Statistics)

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APA (6th Edition):

Al-Hammali, H. Y. (2016). Nonuniform Sampling Of Band-limited Functions. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/59862

Chicago Manual of Style (16th Edition):

Al-Hammali, Hussain Y. “Nonuniform Sampling Of Band-limited Functions.” 2016. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/59862.

MLA Handbook (7th Edition):

Al-Hammali, Hussain Y. “Nonuniform Sampling Of Band-limited Functions.” 2016. Web. 18 Apr 2019.

Vancouver:

Al-Hammali HY. Nonuniform Sampling Of Band-limited Functions. [Internet] [Doctoral dissertation]. Oregon State University; 2016. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/59862.

Council of Science Editors:

Al-Hammali HY. Nonuniform Sampling Of Band-limited Functions. [Doctoral Dissertation]. Oregon State University; 2016. Available from: http://hdl.handle.net/1957/59862


Oregon State University

8. Hostetler, Jesse A. Monte Carlo Tree Search with Fixed and Adaptive Abstractions.

Degree: PhD, Computer Science, 2017, Oregon State University

 Monte Carlo tree search (MCTS) is a class of online planning algorithms for Markov decision processes (MDPs) and related models that has found success in… (more)

Subjects/Keywords: Artificial intelligence; Markov processes

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APA (6th Edition):

Hostetler, J. A. (2017). Monte Carlo Tree Search with Fixed and Adaptive Abstractions. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/60635

Chicago Manual of Style (16th Edition):

Hostetler, Jesse A. “Monte Carlo Tree Search with Fixed and Adaptive Abstractions.” 2017. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/60635.

MLA Handbook (7th Edition):

Hostetler, Jesse A. “Monte Carlo Tree Search with Fixed and Adaptive Abstractions.” 2017. Web. 18 Apr 2019.

Vancouver:

Hostetler JA. Monte Carlo Tree Search with Fixed and Adaptive Abstractions. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/60635.

Council of Science Editors:

Hostetler JA. Monte Carlo Tree Search with Fixed and Adaptive Abstractions. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/60635


Oregon State University

9. Alkaee Taleghan, Majid. Simulator-Defined MDP Planning with Applications in Natural Resource Management.

Degree: PhD, Computer Science, 2017, Oregon State University

 This work is inspired by problems in natural resource management centered on the challenge of invasive species. Computing optimal management policies for maintaining ecosystem sustainable… (more)

Subjects/Keywords: Markov Decision Processes; Markov processes

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APA (6th Edition):

Alkaee Taleghan, M. (2017). Simulator-Defined MDP Planning with Applications in Natural Resource Management. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/60125

Chicago Manual of Style (16th Edition):

Alkaee Taleghan, Majid. “Simulator-Defined MDP Planning with Applications in Natural Resource Management.” 2017. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/60125.

MLA Handbook (7th Edition):

Alkaee Taleghan, Majid. “Simulator-Defined MDP Planning with Applications in Natural Resource Management.” 2017. Web. 18 Apr 2019.

Vancouver:

Alkaee Taleghan M. Simulator-Defined MDP Planning with Applications in Natural Resource Management. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/60125.

Council of Science Editors:

Alkaee Taleghan M. Simulator-Defined MDP Planning with Applications in Natural Resource Management. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/60125


Oregon State University

10. Zhao, Kai. Structured Learning with Latent Variables: Theory and Algorithms.

Degree: PhD, 2017, Oregon State University

 Most tasks in natural language processing (NLP) try to map structured input (e.g., sentence or word sequence) to some form of structured output (tag sequence,… (more)

Subjects/Keywords: structured learning

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APA (6th Edition):

Zhao, K. (2017). Structured Learning with Latent Variables: Theory and Algorithms. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/61395

Chicago Manual of Style (16th Edition):

Zhao, Kai. “Structured Learning with Latent Variables: Theory and Algorithms.” 2017. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/61395.

MLA Handbook (7th Edition):

Zhao, Kai. “Structured Learning with Latent Variables: Theory and Algorithms.” 2017. Web. 18 Apr 2019.

Vancouver:

Zhao K. Structured Learning with Latent Variables: Theory and Algorithms. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/61395.

Council of Science Editors:

Zhao K. Structured Learning with Latent Variables: Theory and Algorithms. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61395


Oregon State University

11. Wilson, Aaron (Aaron Creighton). Bayesian methods for knowledge transfer and policy search in reinforcement learning.

Degree: PhD, Computer Science, 2012, Oregon State University

 How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action… (more)

Subjects/Keywords: Machine Learning; Reinforcement learning

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APA (6th Edition):

Wilson, A. (. C. (2012). Bayesian methods for knowledge transfer and policy search in reinforcement learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/34550

Chicago Manual of Style (16th Edition):

Wilson, Aaron (Aaron Creighton). “Bayesian methods for knowledge transfer and policy search in reinforcement learning.” 2012. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/34550.

MLA Handbook (7th Edition):

Wilson, Aaron (Aaron Creighton). “Bayesian methods for knowledge transfer and policy search in reinforcement learning.” 2012. Web. 18 Apr 2019.

Vancouver:

Wilson A(C. Bayesian methods for knowledge transfer and policy search in reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2012. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/34550.

Council of Science Editors:

Wilson A(C. Bayesian methods for knowledge transfer and policy search in reinforcement learning. [Doctoral Dissertation]. Oregon State University; 2012. Available from: http://hdl.handle.net/1957/34550


Oregon State University

12. Natarajan, Sriraam. Effective decision-theoretic assistance through relational hierarchical models.

Degree: PhD, Computer Science, 2008, Oregon State University

 Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this… (more)

Subjects/Keywords: Decision-Theory; Decision support systems

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APA (6th Edition):

Natarajan, S. (2008). Effective decision-theoretic assistance through relational hierarchical models. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/7574

Chicago Manual of Style (16th Edition):

Natarajan, Sriraam. “Effective decision-theoretic assistance through relational hierarchical models.” 2008. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/7574.

MLA Handbook (7th Edition):

Natarajan, Sriraam. “Effective decision-theoretic assistance through relational hierarchical models.” 2008. Web. 18 Apr 2019.

Vancouver:

Natarajan S. Effective decision-theoretic assistance through relational hierarchical models. [Internet] [Doctoral dissertation]. Oregon State University; 2008. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/7574.

Council of Science Editors:

Natarajan S. Effective decision-theoretic assistance through relational hierarchical models. [Doctoral Dissertation]. Oregon State University; 2008. Available from: http://hdl.handle.net/1957/7574


Oregon State University

13. Migler-VonDollen, Theresa. The density signature.

Degree: PhD, Computer Science, 2014, Oregon State University

 In real networks, identifying dense regions is of great importance. For example, in a network that represents academic collaboration, authors within the densest component of… (more)

Subjects/Keywords: Density decomposition; Paths and cycles (Graph theory)

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APA (6th Edition):

Migler-VonDollen, T. (2014). The density signature. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/48693

Chicago Manual of Style (16th Edition):

Migler-VonDollen, Theresa. “The density signature.” 2014. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/48693.

MLA Handbook (7th Edition):

Migler-VonDollen, Theresa. “The density signature.” 2014. Web. 18 Apr 2019.

Vancouver:

Migler-VonDollen T. The density signature. [Internet] [Doctoral dissertation]. Oregon State University; 2014. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/48693.

Council of Science Editors:

Migler-VonDollen T. The density signature. [Doctoral Dissertation]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/48693


Oregon State University

14. Potanapalli, Kranti Kumar. Coactive learning for multi-robot search and coverage.

Degree: MS, Computer Science, 2013, Oregon State University

 We investigate a search and coverage planning problem, where an area of interest has to be explored by a number of vehicles, given a fixed… (more)

Subjects/Keywords: Coactive Learning; Swarm intelligence  – Mathematical models

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APA (6th Edition):

Potanapalli, K. K. (2013). Coactive learning for multi-robot search and coverage. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/45107

Chicago Manual of Style (16th Edition):

Potanapalli, Kranti Kumar. “Coactive learning for multi-robot search and coverage.” 2013. Masters Thesis, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/45107.

MLA Handbook (7th Edition):

Potanapalli, Kranti Kumar. “Coactive learning for multi-robot search and coverage.” 2013. Web. 18 Apr 2019.

Vancouver:

Potanapalli KK. Coactive learning for multi-robot search and coverage. [Internet] [Masters thesis]. Oregon State University; 2013. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/45107.

Council of Science Editors:

Potanapalli KK. Coactive learning for multi-robot search and coverage. [Masters Thesis]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/45107


Oregon State University

15. Cross, James Henry III. Parsing with Recurrent Neural Networks.

Degree: PhD, Computer Science, 2016, Oregon State University

 Machine learning models for natural language processing have traditionally relied on large numbers of discrete features, built up from atomic categories such as word forms… (more)

Subjects/Keywords: Neural networks (Computer science)

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APA (6th Edition):

Cross, J. H. I. (2016). Parsing with Recurrent Neural Networks. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/60029

Chicago Manual of Style (16th Edition):

Cross, James Henry III. “Parsing with Recurrent Neural Networks.” 2016. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/60029.

MLA Handbook (7th Edition):

Cross, James Henry III. “Parsing with Recurrent Neural Networks.” 2016. Web. 18 Apr 2019.

Vancouver:

Cross JHI. Parsing with Recurrent Neural Networks. [Internet] [Doctoral dissertation]. Oregon State University; 2016. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/60029.

Council of Science Editors:

Cross JHI. Parsing with Recurrent Neural Networks. [Doctoral Dissertation]. Oregon State University; 2016. Available from: http://hdl.handle.net/1957/60029


Oregon State University

16. Ghaeini, Mohammad Reza. Event Detection with Forward-Backward Recurrent Neural Networks.

Degree: MS, 2017, Oregon State University

 Automatic event extraction from natural text is an important and challenging task for natural language understanding. Traditional event detection methods heavily rely on manually engineered… (more)

Subjects/Keywords: Deep Learning

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APA (6th Edition):

Ghaeini, M. R. (2017). Event Detection with Forward-Backward Recurrent Neural Networks. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/61576

Chicago Manual of Style (16th Edition):

Ghaeini, Mohammad Reza. “Event Detection with Forward-Backward Recurrent Neural Networks.” 2017. Masters Thesis, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/61576.

MLA Handbook (7th Edition):

Ghaeini, Mohammad Reza. “Event Detection with Forward-Backward Recurrent Neural Networks.” 2017. Web. 18 Apr 2019.

Vancouver:

Ghaeini MR. Event Detection with Forward-Backward Recurrent Neural Networks. [Internet] [Masters thesis]. Oregon State University; 2017. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/61576.

Council of Science Editors:

Ghaeini MR. Event Detection with Forward-Backward Recurrent Neural Networks. [Masters Thesis]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61576

17. Sorower, Mohammad Shahed. Improving Automated Email Tagging with Implicit Feedback.

Degree: PhD, Computer Science, 2015, Oregon State University

 Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a… (more)

Subjects/Keywords: implicit feedback; Electronic mail messages

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APA (6th Edition):

Sorower, M. S. (2015). Improving Automated Email Tagging with Implicit Feedback. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/57963

Chicago Manual of Style (16th Edition):

Sorower, Mohammad Shahed. “Improving Automated Email Tagging with Implicit Feedback.” 2015. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/57963.

MLA Handbook (7th Edition):

Sorower, Mohammad Shahed. “Improving Automated Email Tagging with Implicit Feedback.” 2015. Web. 18 Apr 2019.

Vancouver:

Sorower MS. Improving Automated Email Tagging with Implicit Feedback. [Internet] [Doctoral dissertation]. Oregon State University; 2015. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/57963.

Council of Science Editors:

Sorower MS. Improving Automated Email Tagging with Implicit Feedback. [Doctoral Dissertation]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/57963

18. Bjarnason, Ronald V. Monte-Carlo planning for probabilistic domains.

Degree: PhD, Computer Science, 2009, Oregon State University

 This thesis presents a progression of novel planning algorithms that culminates in a new family of diverse Monte-Carlo methods for probabilistic planning domains. We provide… (more)

Subjects/Keywords: Artificial Intelligence; Monte-Carlo method

…permanent collection of Oregon State University libraries. My signature below authorizes release… 

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APA (6th Edition):

Bjarnason, R. V. (2009). Monte-Carlo planning for probabilistic domains. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/13657

Chicago Manual of Style (16th Edition):

Bjarnason, Ronald V. “Monte-Carlo planning for probabilistic domains.” 2009. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/13657.

MLA Handbook (7th Edition):

Bjarnason, Ronald V. “Monte-Carlo planning for probabilistic domains.” 2009. Web. 18 Apr 2019.

Vancouver:

Bjarnason RV. Monte-Carlo planning for probabilistic domains. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/13657.

Council of Science Editors:

Bjarnason RV. Monte-Carlo planning for probabilistic domains. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/13657

19. Jothi, Shankar. Evaluation of Parallel Monte Carlo Tree Search Algorithms in Python.

Degree: MS, Computer Science, 2015, Oregon State University

 Monte-Carlo Tree Search (MCTS) is an online-planning algorithm for decision-theoretic planning in domains with stochastic and combinatorial structure. The general applicability of MCTS makes it… (more)

Subjects/Keywords: Artificial Intelligence; Python (Computer program language)

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APA (6th Edition):

Jothi, S. (2015). Evaluation of Parallel Monte Carlo Tree Search Algorithms in Python. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/56230

Chicago Manual of Style (16th Edition):

Jothi, Shankar. “Evaluation of Parallel Monte Carlo Tree Search Algorithms in Python.” 2015. Masters Thesis, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/56230.

MLA Handbook (7th Edition):

Jothi, Shankar. “Evaluation of Parallel Monte Carlo Tree Search Algorithms in Python.” 2015. Web. 18 Apr 2019.

Vancouver:

Jothi S. Evaluation of Parallel Monte Carlo Tree Search Algorithms in Python. [Internet] [Masters thesis]. Oregon State University; 2015. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/56230.

Council of Science Editors:

Jothi S. Evaluation of Parallel Monte Carlo Tree Search Algorithms in Python. [Masters Thesis]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/56230

20. Moissinac, Béatrice. Reinforcement learning-based off-equilibrium incentives to approximate the VCG mechanism.

Degree: MS, Computer Science, 2013, Oregon State University

 Auctions are used to solve resource allocation problem between many agents and many items in real-world settings. Unfortunately, in most cases, it is possible for… (more)

Subjects/Keywords: VCG; Reinforcement learning

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APA (6th Edition):

Moissinac, B. (2013). Reinforcement learning-based off-equilibrium incentives to approximate the VCG mechanism. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/45126

Chicago Manual of Style (16th Edition):

Moissinac, Béatrice. “Reinforcement learning-based off-equilibrium incentives to approximate the VCG mechanism.” 2013. Masters Thesis, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/45126.

MLA Handbook (7th Edition):

Moissinac, Béatrice. “Reinforcement learning-based off-equilibrium incentives to approximate the VCG mechanism.” 2013. Web. 18 Apr 2019.

Vancouver:

Moissinac B. Reinforcement learning-based off-equilibrium incentives to approximate the VCG mechanism. [Internet] [Masters thesis]. Oregon State University; 2013. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/45126.

Council of Science Editors:

Moissinac B. Reinforcement learning-based off-equilibrium incentives to approximate the VCG mechanism. [Masters Thesis]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/45126

21. Doppa, Janardhan Rao. Integrating learning and search for structured prediction.

Degree: PhD, Computer Science, 2014, Oregon State University

 We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of… (more)

Subjects/Keywords: Structured Prediction; Machine learning

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APA (6th Edition):

Doppa, J. R. (2014). Integrating learning and search for structured prediction. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/50908

Chicago Manual of Style (16th Edition):

Doppa, Janardhan Rao. “Integrating learning and search for structured prediction.” 2014. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/50908.

MLA Handbook (7th Edition):

Doppa, Janardhan Rao. “Integrating learning and search for structured prediction.” 2014. Web. 18 Apr 2019.

Vancouver:

Doppa JR. Integrating learning and search for structured prediction. [Internet] [Doctoral dissertation]. Oregon State University; 2014. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/50908.

Council of Science Editors:

Doppa JR. Integrating learning and search for structured prediction. [Doctoral Dissertation]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/50908

22. Hess, Robin W. Toward computer vision for understanding American football in video.

Degree: PhD, Computer Science, 2012, Oregon State University

 In this work, I examine the problem of understanding American football in video. In particular, I present several mid-level computer vision algorithms that each accomplish… (more)

Subjects/Keywords: Computer Vision; Computer vision

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APA (6th Edition):

Hess, R. W. (2012). Toward computer vision for understanding American football in video. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/30348

Chicago Manual of Style (16th Edition):

Hess, Robin W. “Toward computer vision for understanding American football in video.” 2012. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/30348.

MLA Handbook (7th Edition):

Hess, Robin W. “Toward computer vision for understanding American football in video.” 2012. Web. 18 Apr 2019.

Vancouver:

Hess RW. Toward computer vision for understanding American football in video. [Internet] [Doctoral dissertation]. Oregon State University; 2012. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/30348.

Council of Science Editors:

Hess RW. Toward computer vision for understanding American football in video. [Doctoral Dissertation]. Oregon State University; 2012. Available from: http://hdl.handle.net/1957/30348

23. Pinto, Jervis. Incorporating and Learning Behavior Constraints for Sequential Decision Making.

Degree: PhD, Computer Science, 2015, Oregon State University

 Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement… (more)

Subjects/Keywords: online sequential decision making; Reinforcement learning

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APA (6th Edition):

Pinto, J. (2015). Incorporating and Learning Behavior Constraints for Sequential Decision Making. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/56129

Chicago Manual of Style (16th Edition):

Pinto, Jervis. “Incorporating and Learning Behavior Constraints for Sequential Decision Making.” 2015. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/56129.

MLA Handbook (7th Edition):

Pinto, Jervis. “Incorporating and Learning Behavior Constraints for Sequential Decision Making.” 2015. Web. 18 Apr 2019.

Vancouver:

Pinto J. Incorporating and Learning Behavior Constraints for Sequential Decision Making. [Internet] [Doctoral dissertation]. Oregon State University; 2015. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/56129.

Council of Science Editors:

Pinto J. Incorporating and Learning Behavior Constraints for Sequential Decision Making. [Doctoral Dissertation]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/56129

24. Wingo, Patrick C. OME : A Framework for Running Spatially Explicit System Dynamics Models.

Degree: MS, Biological and Ecological Engineering, 2015, Oregon State University

 The Open Modeling Environment (OME) is a tool developed to address some known shortcomings in ecological System Dynamics (SD) modeling research. OME provides a common… (more)

Subjects/Keywords: Modeling; Ecological Systems Theory

…spatially explicit integrated modeling platform developed at Oregon State University (Oregon… …behaviors) (Oregon State University). As a collaborative modeling platform… …goal of assisting in policy decisions (Oregon State University 2014b). Further… …allowing crosscommunication between other modeling components (Oregon State University 2014b… …State University). Envision focuses on simulations revolving around coupled human and… 

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APA (6th Edition):

Wingo, P. C. (2015). OME : A Framework for Running Spatially Explicit System Dynamics Models. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/56380

Chicago Manual of Style (16th Edition):

Wingo, Patrick C. “OME : A Framework for Running Spatially Explicit System Dynamics Models.” 2015. Masters Thesis, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/56380.

MLA Handbook (7th Edition):

Wingo, Patrick C. “OME : A Framework for Running Spatially Explicit System Dynamics Models.” 2015. Web. 18 Apr 2019.

Vancouver:

Wingo PC. OME : A Framework for Running Spatially Explicit System Dynamics Models. [Internet] [Masters thesis]. Oregon State University; 2015. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/56380.

Council of Science Editors:

Wingo PC. OME : A Framework for Running Spatially Explicit System Dynamics Models. [Masters Thesis]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/56380

25. Anderson, Michael M. (Michael Mason). Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models.

Degree: MS, Computer Science, 2013, Oregon State University

 Physical activity recognition using accelerometer data is a rapidly emerging field with many real-world applications. Much of the previous work in this area has assumed… (more)

Subjects/Keywords: Time-series analysis

Oregon State University [37], [41]. This dataset consisted of 91 time… 

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APA (6th Edition):

Anderson, M. M. (. M. (2013). Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/40320

Chicago Manual of Style (16th Edition):

Anderson, Michael M (Michael Mason). “Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models.” 2013. Masters Thesis, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/40320.

MLA Handbook (7th Edition):

Anderson, Michael M (Michael Mason). “Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models.” 2013. Web. 18 Apr 2019.

Vancouver:

Anderson MM(M. Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models. [Internet] [Masters thesis]. Oregon State University; 2013. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/40320.

Council of Science Editors:

Anderson MM(M. Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models. [Masters Thesis]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/40320

26. Xiong, Sicheng. Active learning of constraints for semi-supervised clustering.

Degree: MS, Computer Science, 2013, Oregon State University

 Semi-supervised clustering aims to improve clustering performance by considering user supervision in the form of pairwise constraints. In this paper, we study the active learning… (more)

Subjects/Keywords: Active Learning; Cluster analysis

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APA (6th Edition):

Xiong, S. (2013). Active learning of constraints for semi-supervised clustering. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/38533

Chicago Manual of Style (16th Edition):

Xiong, Sicheng. “Active learning of constraints for semi-supervised clustering.” 2013. Masters Thesis, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/38533.

MLA Handbook (7th Edition):

Xiong, Sicheng. “Active learning of constraints for semi-supervised clustering.” 2013. Web. 18 Apr 2019.

Vancouver:

Xiong S. Active learning of constraints for semi-supervised clustering. [Internet] [Masters thesis]. Oregon State University; 2013. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/38533.

Council of Science Editors:

Xiong S. Active learning of constraints for semi-supervised clustering. [Masters Thesis]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/38533

27. Azimi, Javad. Bayesian optimization with empirical constraints.

Degree: PhD, Computer Science, 2012, Oregon State University

 Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner.… (more)

Subjects/Keywords: Bayesian Optimization; Mathematical optimization

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APA (6th Edition):

Azimi, J. (2012). Bayesian optimization with empirical constraints. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/33664

Chicago Manual of Style (16th Edition):

Azimi, Javad. “Bayesian optimization with empirical constraints.” 2012. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/33664.

MLA Handbook (7th Edition):

Azimi, Javad. “Bayesian optimization with empirical constraints.” 2012. Web. 18 Apr 2019.

Vancouver:

Azimi J. Bayesian optimization with empirical constraints. [Internet] [Doctoral dissertation]. Oregon State University; 2012. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/33664.

Council of Science Editors:

Azimi J. Bayesian optimization with empirical constraints. [Doctoral Dissertation]. Oregon State University; 2012. Available from: http://hdl.handle.net/1957/33664


Oregon State University

28. Reddy, Chandrasekhara K. Learning hierarchical decomposition rules for planning : an inductive logic programming approach.

Degree: PhD, Computer Science, 1998, Oregon State University

 Arti cial Intelligence (AI) planning techniques have been central to automating a gamut of tasks from the mundane route planning and beer production to the… (more)

Subjects/Keywords: Machine learning

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APA (6th Edition):

Reddy, C. K. (1998). Learning hierarchical decomposition rules for planning : an inductive logic programming approach. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/10292

Chicago Manual of Style (16th Edition):

Reddy, Chandrasekhara K. “Learning hierarchical decomposition rules for planning : an inductive logic programming approach.” 1998. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/10292.

MLA Handbook (7th Edition):

Reddy, Chandrasekhara K. “Learning hierarchical decomposition rules for planning : an inductive logic programming approach.” 1998. Web. 18 Apr 2019.

Vancouver:

Reddy CK. Learning hierarchical decomposition rules for planning : an inductive logic programming approach. [Internet] [Doctoral dissertation]. Oregon State University; 1998. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/10292.

Council of Science Editors:

Reddy CK. Learning hierarchical decomposition rules for planning : an inductive logic programming approach. [Doctoral Dissertation]. Oregon State University; 1998. Available from: http://hdl.handle.net/1957/10292


Oregon State University

29. Liere, Ray. Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms.

Degree: PhD, Computer Science, 1999, Oregon State University

 We developed and investigated machine learning methods that require minimal preprocessing of the input data, use few training examples, run fast, and still obtain high… (more)

Subjects/Keywords: Text processing (Computer science)

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APA (6th Edition):

Liere, R. (1999). Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/10466

Chicago Manual of Style (16th Edition):

Liere, Ray. “Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms.” 1999. Doctoral Dissertation, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/10466.

MLA Handbook (7th Edition):

Liere, Ray. “Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms.” 1999. Web. 18 Apr 2019.

Vancouver:

Liere R. Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms. [Internet] [Doctoral dissertation]. Oregon State University; 1999. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/10466.

Council of Science Editors:

Liere R. Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms. [Doctoral Dissertation]. Oregon State University; 1999. Available from: http://hdl.handle.net/1957/10466


Oregon State University

30. Chisholm, Michael (Michael Andrew), 1974-. Learning classification rules by randomized iterative local search.

Degree: MS, Computer Science, 1999, Oregon State University

 Learning easily understandable decision rules from examples is one of the classic problems in machine learning. Most learning algorithms for this problem employ some variation… (more)

Subjects/Keywords: Machine learning

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APA (6th Edition):

Chisholm, Michael (Michael Andrew), 1. (1999). Learning classification rules by randomized iterative local search. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/11732

Chicago Manual of Style (16th Edition):

Chisholm, Michael (Michael Andrew), 1974-. “Learning classification rules by randomized iterative local search.” 1999. Masters Thesis, Oregon State University. Accessed April 18, 2019. http://hdl.handle.net/1957/11732.

MLA Handbook (7th Edition):

Chisholm, Michael (Michael Andrew), 1974-. “Learning classification rules by randomized iterative local search.” 1999. Web. 18 Apr 2019.

Vancouver:

Chisholm, Michael (Michael Andrew) 1. Learning classification rules by randomized iterative local search. [Internet] [Masters thesis]. Oregon State University; 1999. [cited 2019 Apr 18]. Available from: http://hdl.handle.net/1957/11732.

Council of Science Editors:

Chisholm, Michael (Michael Andrew) 1. Learning classification rules by randomized iterative local search. [Masters Thesis]. Oregon State University; 1999. Available from: http://hdl.handle.net/1957/11732

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