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You searched for +publisher:"University of Michigan" +contributor:("Baveja, Satinder Singh"). Showing records 1 – 30 of 42 total matches.

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University of Michigan

1. Sleight, Jason Lee. Agent-Driven Representations, Algorithms, and Metrics for Automated Organizational Design.

Degree: PhD, Computer Science and Engineering, 2015, University of Michigan

 As cooperative multiagent systems (MASs) increase in interconnectivity, complexity, size, and longevity, coordinating the agents' reasoning and behaviors becomes increasingly difficult. One approach to address… (more)

Subjects/Keywords: Artificial Intelligence; Multiagent Coordination; Organizational Design; Computer Science; Engineering

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APA (6th Edition):

Sleight, J. L. (2015). Agent-Driven Representations, Algorithms, and Metrics for Automated Organizational Design. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113616

Chicago Manual of Style (16th Edition):

Sleight, Jason Lee. “Agent-Driven Representations, Algorithms, and Metrics for Automated Organizational Design.” 2015. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/113616.

MLA Handbook (7th Edition):

Sleight, Jason Lee. “Agent-Driven Representations, Algorithms, and Metrics for Automated Organizational Design.” 2015. Web. 11 Aug 2020.

Vancouver:

Sleight JL. Agent-Driven Representations, Algorithms, and Metrics for Automated Organizational Design. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/113616.

Council of Science Editors:

Sleight JL. Agent-Driven Representations, Algorithms, and Metrics for Automated Organizational Design. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113616


University of Michigan

2. El Moaqet, Hisham Mohammad Wahbi Subhi. A Framework for Evaluation and Identication of Time Series Models for Multi-Step Ahead Prediction of Physiological Signals.

Degree: PhD, Mechanical Engineering, 2015, University of Michigan

 Significant interest exists in the potential to use continuous physiological monitoring to prevent respiratory complications and death, especially in the postoperative period. Smart alarm-threshold based… (more)

Subjects/Keywords: Time Series Modeling; Multi-Step Ahead Prediction; Physiological Signals; Mechanical Engineering; Engineering

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APA (6th Edition):

El Moaqet, H. M. W. S. (2015). A Framework for Evaluation and Identication of Time Series Models for Multi-Step Ahead Prediction of Physiological Signals. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/116666

Chicago Manual of Style (16th Edition):

El Moaqet, Hisham Mohammad Wahbi Subhi. “A Framework for Evaluation and Identication of Time Series Models for Multi-Step Ahead Prediction of Physiological Signals.” 2015. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/116666.

MLA Handbook (7th Edition):

El Moaqet, Hisham Mohammad Wahbi Subhi. “A Framework for Evaluation and Identication of Time Series Models for Multi-Step Ahead Prediction of Physiological Signals.” 2015. Web. 11 Aug 2020.

Vancouver:

El Moaqet HMWS. A Framework for Evaluation and Identication of Time Series Models for Multi-Step Ahead Prediction of Physiological Signals. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/116666.

Council of Science Editors:

El Moaqet HMWS. A Framework for Evaluation and Identication of Time Series Models for Multi-Step Ahead Prediction of Physiological Signals. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/116666


University of Michigan

3. Wintermute, Samuel B. Abstraction, Imagery, and Control in Cognitive Architecture.

Degree: PhD, Computer Science & Engineering, 2010, University of Michigan

 This dissertation presents a theory describing the components of a cognitive architecture supporting intelligent behavior in spatial tasks. In this theory, an abstract symbolic representation… (more)

Subjects/Keywords: Cognitive Architecture; Artificial Intelligence; Mental Imagery; Computer Science; Engineering; Science

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APA (6th Edition):

Wintermute, S. B. (2010). Abstraction, Imagery, and Control in Cognitive Architecture. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/78795

Chicago Manual of Style (16th Edition):

Wintermute, Samuel B. “Abstraction, Imagery, and Control in Cognitive Architecture.” 2010. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/78795.

MLA Handbook (7th Edition):

Wintermute, Samuel B. “Abstraction, Imagery, and Control in Cognitive Architecture.” 2010. Web. 11 Aug 2020.

Vancouver:

Wintermute SB. Abstraction, Imagery, and Control in Cognitive Architecture. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/78795.

Council of Science Editors:

Wintermute SB. Abstraction, Imagery, and Control in Cognitive Architecture. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/78795


University of Michigan

4. Talvitie, Erik N. Simple Partial Models for Complex Dynamical Systems.

Degree: PhD, Computer Science & Engineering, 2010, University of Michigan

 An agent in an unknown environment may wish to learn a model that allows it to make predictions about future events and anticipate the consequences… (more)

Subjects/Keywords: Artificial Intelligence; Machine Learning; Computer Science; Engineering

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APA (6th Edition):

Talvitie, E. N. (2010). Simple Partial Models for Complex Dynamical Systems. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/78893

Chicago Manual of Style (16th Edition):

Talvitie, Erik N. “Simple Partial Models for Complex Dynamical Systems.” 2010. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/78893.

MLA Handbook (7th Edition):

Talvitie, Erik N. “Simple Partial Models for Complex Dynamical Systems.” 2010. Web. 11 Aug 2020.

Vancouver:

Talvitie EN. Simple Partial Models for Complex Dynamical Systems. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/78893.

Council of Science Editors:

Talvitie EN. Simple Partial Models for Complex Dynamical Systems. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/78893


University of Michigan

5. Laber, Eric B. Adaptive Confidence Intervals for Non-Smooth Functionals.

Degree: PhD, Statistics, 2011, University of Michigan

 Many quantities of interest in modern statistical analysis are non-smooth functionals of the underlying generative distribution, the observed data, or both. Examples include the test… (more)

Subjects/Keywords: Nonregular Functionals; Adaptive Confidence Intervals; Statistics and Numeric Data; Science

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APA (6th Edition):

Laber, E. B. (2011). Adaptive Confidence Intervals for Non-Smooth Functionals. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/84551

Chicago Manual of Style (16th Edition):

Laber, Eric B. “Adaptive Confidence Intervals for Non-Smooth Functionals.” 2011. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/84551.

MLA Handbook (7th Edition):

Laber, Eric B. “Adaptive Confidence Intervals for Non-Smooth Functionals.” 2011. Web. 11 Aug 2020.

Vancouver:

Laber EB. Adaptive Confidence Intervals for Non-Smooth Functionals. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/84551.

Council of Science Editors:

Laber EB. Adaptive Confidence Intervals for Non-Smooth Functionals. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/84551


University of Michigan

6. Katz-Samuels, Julian. Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Michigan

 Many applications can be modeled as follows: an agent is given access to several distributions and she wishes to determine those that meet some pre-specified… (more)

Subjects/Keywords: Adaptive Data Collection; Computer Science; Engineering

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APA (6th Edition):

Katz-Samuels, J. (2019). Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151539

Chicago Manual of Style (16th Edition):

Katz-Samuels, Julian. “Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria.” 2019. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/151539.

MLA Handbook (7th Edition):

Katz-Samuels, Julian. “Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria.” 2019. Web. 11 Aug 2020.

Vancouver:

Katz-Samuels J. Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/151539.

Council of Science Editors:

Katz-Samuels J. Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151539


University of Michigan

7. Dhiman, Vikas. Towards Better Navigation: Optimizing Algorithms for Mapping, Localization and Planning.

Degree: PhD, Electrical Engineering: Systems, 2019, University of Michigan

 Navigation is the problem of going from one place to another. It is an important problem in the areas of autonomous driving, mobile robotics and… (more)

Subjects/Keywords: Mapping; Localization; Reinforcement learning; Navigation; Computer Science; Engineering

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APA (6th Edition):

Dhiman, V. (2019). Towards Better Navigation: Optimizing Algorithms for Mapping, Localization and Planning. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/150011

Chicago Manual of Style (16th Edition):

Dhiman, Vikas. “Towards Better Navigation: Optimizing Algorithms for Mapping, Localization and Planning.” 2019. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/150011.

MLA Handbook (7th Edition):

Dhiman, Vikas. “Towards Better Navigation: Optimizing Algorithms for Mapping, Localization and Planning.” 2019. Web. 11 Aug 2020.

Vancouver:

Dhiman V. Towards Better Navigation: Optimizing Algorithms for Mapping, Localization and Planning. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/150011.

Council of Science Editors:

Dhiman V. Towards Better Navigation: Optimizing Algorithms for Mapping, Localization and Planning. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/150011


University of Michigan

8. Holler, John. Learning Dynamics and Reinforcement in Stochastic Games.

Degree: PhD, Mathematics, 2020, University of Michigan

 The theory of Reinforcement Learning provides learning algorithms that are guaranteed to converge to optimal behavior in single-agent learning environments. While these algorithms often do… (more)

Subjects/Keywords: game theory; reinforcement learning; deep learning; learning in games; stochastic games; Mathematics; Science

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APA (6th Edition):

Holler, J. (2020). Learning Dynamics and Reinforcement in Stochastic Games. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/155158

Chicago Manual of Style (16th Edition):

Holler, John. “Learning Dynamics and Reinforcement in Stochastic Games.” 2020. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/155158.

MLA Handbook (7th Edition):

Holler, John. “Learning Dynamics and Reinforcement in Stochastic Games.” 2020. Web. 11 Aug 2020.

Vancouver:

Holler J. Learning Dynamics and Reinforcement in Stochastic Games. [Internet] [Doctoral dissertation]. University of Michigan; 2020. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/155158.

Council of Science Editors:

Holler J. Learning Dynamics and Reinforcement in Stochastic Games. [Doctoral Dissertation]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/155158


University of Michigan

9. Hodges, Mark Richard. Sensor-Based Analysis of Object-Use Patterns for the Automatic Assessment of Cognitive Status.

Degree: PhD, Computer Science & Engineering, 2010, University of Michigan

 Indications of cognitive impairments such as dementia and traumatic brain injury (TBI) are often subtle and may be frequently missed by primary care physicians. These… (more)

Subjects/Keywords: Assistive Technology; Ubiquitous Computing; Cognitive Impairment; Computer Science; Engineering

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APA (6th Edition):

Hodges, M. R. (2010). Sensor-Based Analysis of Object-Use Patterns for the Automatic Assessment of Cognitive Status. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/78894

Chicago Manual of Style (16th Edition):

Hodges, Mark Richard. “Sensor-Based Analysis of Object-Use Patterns for the Automatic Assessment of Cognitive Status.” 2010. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/78894.

MLA Handbook (7th Edition):

Hodges, Mark Richard. “Sensor-Based Analysis of Object-Use Patterns for the Automatic Assessment of Cognitive Status.” 2010. Web. 11 Aug 2020.

Vancouver:

Hodges MR. Sensor-Based Analysis of Object-Use Patterns for the Automatic Assessment of Cognitive Status. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/78894.

Council of Science Editors:

Hodges MR. Sensor-Based Analysis of Object-Use Patterns for the Automatic Assessment of Cognitive Status. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/78894


University of Michigan

10. Sorg, Jonathan Daniel. The Optimal Reward Problem: Designing Effective Reward for Bounded Agents.

Degree: PhD, Computer Science & Engineering, 2011, University of Michigan

 In the field of reinforcement learning, agent designers build agents which seek to maximize reward. In standard practice, one reward function serves two purposes. It… (more)

Subjects/Keywords: Reinforcement Learning; Computer Science; Engineering

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APA (6th Edition):

Sorg, J. D. (2011). The Optimal Reward Problem: Designing Effective Reward for Bounded Agents. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/89705

Chicago Manual of Style (16th Edition):

Sorg, Jonathan Daniel. “The Optimal Reward Problem: Designing Effective Reward for Bounded Agents.” 2011. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/89705.

MLA Handbook (7th Edition):

Sorg, Jonathan Daniel. “The Optimal Reward Problem: Designing Effective Reward for Bounded Agents.” 2011. Web. 11 Aug 2020.

Vancouver:

Sorg JD. The Optimal Reward Problem: Designing Effective Reward for Bounded Agents. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/89705.

Council of Science Editors:

Sorg JD. The Optimal Reward Problem: Designing Effective Reward for Bounded Agents. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/89705


University of Michigan

11. Jordan, Patrick R. Practical Strategic Reasoning with Applications in Market Games.

Degree: PhD, Computer Science & Engineering, 2010, University of Michigan

 Strategic reasoning is part of our everyday lives: we negotiate prices, bid in auctions, write contracts, and play games. We choose actions in these scenarios… (more)

Subjects/Keywords: Empirical Game Theory; Computer Science; Engineering; Science

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APA (6th Edition):

Jordan, P. R. (2010). Practical Strategic Reasoning with Applications in Market Games. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/75848

Chicago Manual of Style (16th Edition):

Jordan, Patrick R. “Practical Strategic Reasoning with Applications in Market Games.” 2010. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/75848.

MLA Handbook (7th Edition):

Jordan, Patrick R. “Practical Strategic Reasoning with Applications in Market Games.” 2010. Web. 11 Aug 2020.

Vancouver:

Jordan PR. Practical Strategic Reasoning with Applications in Market Games. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/75848.

Council of Science Editors:

Jordan PR. Practical Strategic Reasoning with Applications in Market Games. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/75848

12. Tekin, Cem. Online Learning in Bandit Problems.

Degree: PhD, Electrical Engineering: Systems, 2013, University of Michigan

 In a bandit problem there is a set of arms, each of which when played by an agent yields some reward depending on its internal… (more)

Subjects/Keywords: Multi-armed Bandits; Online Learning; Stochastic Optimization; Dynamic Resource Sharing; Sequential Decision Making; Reinforcement Learning; Computer Science; Electrical Engineering; Industrial and Operations Engineering; Engineering

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APA (6th Edition):

Tekin, C. (2013). Online Learning in Bandit Problems. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/97798

Chicago Manual of Style (16th Edition):

Tekin, Cem. “Online Learning in Bandit Problems.” 2013. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/97798.

MLA Handbook (7th Edition):

Tekin, Cem. “Online Learning in Bandit Problems.” 2013. Web. 11 Aug 2020.

Vancouver:

Tekin C. Online Learning in Bandit Problems. [Internet] [Doctoral dissertation]. University of Michigan; 2013. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/97798.

Council of Science Editors:

Tekin C. Online Learning in Bandit Problems. [Doctoral Dissertation]. University of Michigan; 2013. Available from: http://hdl.handle.net/2027.42/97798

13. Guo, Xiaoxiao. Deep Learning and Reward Design for Reinforcement Learning.

Degree: PhD, Computer Science & Engineering, 2017, University of Michigan

 One of the fundamental problems in Artificial Intelligence is sequential decision making in a flexible environment. Reinforcement Learning (RL) gives a set of tools for… (more)

Subjects/Keywords: Reinforcement Learning; Deep Learning; Computer Science; Engineering

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APA (6th Edition):

Guo, X. (2017). Deep Learning and Reward Design for Reinforcement Learning. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/136931

Chicago Manual of Style (16th Edition):

Guo, Xiaoxiao. “Deep Learning and Reward Design for Reinforcement Learning.” 2017. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/136931.

MLA Handbook (7th Edition):

Guo, Xiaoxiao. “Deep Learning and Reward Design for Reinforcement Learning.” 2017. Web. 11 Aug 2020.

Vancouver:

Guo X. Deep Learning and Reward Design for Reinforcement Learning. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/136931.

Council of Science Editors:

Guo X. Deep Learning and Reward Design for Reinforcement Learning. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/136931

14. Cohn, Robert W. Maximizing Expected Value of Information in Decision Problems by Querying on a Wish-to-Know Basis.

Degree: PhD, Computer Science and Engineering, 2016, University of Michigan

 An agent acting under uncertainty regarding how it should complete the task assigned to it by its human user can learn more about how it… (more)

Subjects/Keywords: Expected Value of Information; Query Selection Algorithms; Computer Science; Engineering

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APA (6th Edition):

Cohn, R. W. (2016). Maximizing Expected Value of Information in Decision Problems by Querying on a Wish-to-Know Basis. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/120772

Chicago Manual of Style (16th Edition):

Cohn, Robert W. “Maximizing Expected Value of Information in Decision Problems by Querying on a Wish-to-Know Basis.” 2016. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/120772.

MLA Handbook (7th Edition):

Cohn, Robert W. “Maximizing Expected Value of Information in Decision Problems by Querying on a Wish-to-Know Basis.” 2016. Web. 11 Aug 2020.

Vancouver:

Cohn RW. Maximizing Expected Value of Information in Decision Problems by Querying on a Wish-to-Know Basis. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/120772.

Council of Science Editors:

Cohn RW. Maximizing Expected Value of Information in Decision Problems by Querying on a Wish-to-Know Basis. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/120772

15. Wiedenbeck, Bryce. Approximate Analysis of Large Simulation-Based Games.

Degree: PhD, Computer Science and Engineering, 2015, University of Michigan

 Game theory offers powerful tools for reasoning about agent behavior and incentives in multi-agent systems. Traditional approaches to game-theoretic analysis require enumeration of all possible… (more)

Subjects/Keywords: simulation-based game theory; Computer Science; Engineering

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APA (6th Edition):

Wiedenbeck, B. (2015). Approximate Analysis of Large Simulation-Based Games. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113587

Chicago Manual of Style (16th Edition):

Wiedenbeck, Bryce. “Approximate Analysis of Large Simulation-Based Games.” 2015. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/113587.

MLA Handbook (7th Edition):

Wiedenbeck, Bryce. “Approximate Analysis of Large Simulation-Based Games.” 2015. Web. 11 Aug 2020.

Vancouver:

Wiedenbeck B. Approximate Analysis of Large Simulation-Based Games. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/113587.

Council of Science Editors:

Wiedenbeck B. Approximate Analysis of Large Simulation-Based Games. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113587

16. Van Esbroeck, Alexander. Learning Better Clinical Risk Models.

Degree: PhD, Computer Science and Engineering, 2015, University of Michigan

 Risk models are used to estimate a patient’s risk of suffering particular outcomes throughout clinical practice. These models are important for matching patients to the… (more)

Subjects/Keywords: clinical risk models; physiological recording; missing values; Computer Science; Engineering

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APA (6th Edition):

Van Esbroeck, A. (2015). Learning Better Clinical Risk Models. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113326

Chicago Manual of Style (16th Edition):

Van Esbroeck, Alexander. “Learning Better Clinical Risk Models.” 2015. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/113326.

MLA Handbook (7th Edition):

Van Esbroeck, Alexander. “Learning Better Clinical Risk Models.” 2015. Web. 11 Aug 2020.

Vancouver:

Van Esbroeck A. Learning Better Clinical Risk Models. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/113326.

Council of Science Editors:

Van Esbroeck A. Learning Better Clinical Risk Models. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113326

17. Gunter, Lacey L. Variable Selection for Decision Making.

Degree: PhD, Statistics, 2009, University of Michigan

 In decision making research, scientists collect a large number of variables that may be useful in deciding which action is best. Researchers might use a… (more)

Subjects/Keywords: Decision Making; Variable Selection; Value of Information; Lasso; Statistics and Numeric Data; Science

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APA (6th Edition):

Gunter, L. L. (2009). Variable Selection for Decision Making. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/63808

Chicago Manual of Style (16th Edition):

Gunter, Lacey L. “Variable Selection for Decision Making.” 2009. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/63808.

MLA Handbook (7th Edition):

Gunter, Lacey L. “Variable Selection for Decision Making.” 2009. Web. 11 Aug 2020.

Vancouver:

Gunter LL. Variable Selection for Decision Making. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/63808.

Council of Science Editors:

Gunter LL. Variable Selection for Decision Making. [Doctoral Dissertation]. University of Michigan; 2009. Available from: http://hdl.handle.net/2027.42/63808

18. Wolfe, Britton D. Modeling Dynamical Systems with Structured Predictive State Representations.

Degree: PhD, Computer Science & Engineering, 2009, University of Michigan

 Predictive state representations (PSRs) are a class of models that represent the state of a dynamical system as a set of predictions about future events.… (more)

Subjects/Keywords: Predictive State Representations; Artificial Intelligence; Modeling Dynamical Systems; Computer Science; Engineering

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APA (6th Edition):

Wolfe, B. D. (2009). Modeling Dynamical Systems with Structured Predictive State Representations. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/64601

Chicago Manual of Style (16th Edition):

Wolfe, Britton D. “Modeling Dynamical Systems with Structured Predictive State Representations.” 2009. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/64601.

MLA Handbook (7th Edition):

Wolfe, Britton D. “Modeling Dynamical Systems with Structured Predictive State Representations.” 2009. Web. 11 Aug 2020.

Vancouver:

Wolfe BD. Modeling Dynamical Systems with Structured Predictive State Representations. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/64601.

Council of Science Editors:

Wolfe BD. Modeling Dynamical Systems with Structured Predictive State Representations. [Doctoral Dissertation]. University of Michigan; 2009. Available from: http://hdl.handle.net/2027.42/64601

19. Rudary, Matthew R. On Predictive Linear Gaussian Models.

Degree: PhD, Computer Science & Engineering, 2009, University of Michigan

 Models are used by artificial agents to make predictions about the future; agents then use these predictions to modify their behavior. In many cases, these… (more)

Subjects/Keywords: Reinforcement Learning; Predictive State Representations; Artificial Intelligence; Statistical Models; Linear Dynamical Systems; Computer Science; Engineering

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APA (6th Edition):

Rudary, M. R. (2009). On Predictive Linear Gaussian Models. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/62307

Chicago Manual of Style (16th Edition):

Rudary, Matthew R. “On Predictive Linear Gaussian Models.” 2009. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/62307.

MLA Handbook (7th Edition):

Rudary, Matthew R. “On Predictive Linear Gaussian Models.” 2009. Web. 11 Aug 2020.

Vancouver:

Rudary MR. On Predictive Linear Gaussian Models. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/62307.

Council of Science Editors:

Rudary MR. On Predictive Linear Gaussian Models. [Doctoral Dissertation]. University of Michigan; 2009. Available from: http://hdl.handle.net/2027.42/62307

20. Vorobeychik, Yevgeniy. Mechanism Design and Analysis Using Simulation-Based Game Models.

Degree: PhD, Computer Science & Engineering, 2008, University of Michigan

 As agent technology matures, it becomes easier to envision electronic marketplaces teeming with autonomous agents. Since agents are explicitly programmed to (nearly) optimally compete in… (more)

Subjects/Keywords: Simulation-based Game Theory; Computational Game Theory; Multiagent Systems; Computer Science; Engineering

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APA (6th Edition):

Vorobeychik, Y. (2008). Mechanism Design and Analysis Using Simulation-Based Game Models. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/60786

Chicago Manual of Style (16th Edition):

Vorobeychik, Yevgeniy. “Mechanism Design and Analysis Using Simulation-Based Game Models.” 2008. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/60786.

MLA Handbook (7th Edition):

Vorobeychik, Yevgeniy. “Mechanism Design and Analysis Using Simulation-Based Game Models.” 2008. Web. 11 Aug 2020.

Vancouver:

Vorobeychik Y. Mechanism Design and Analysis Using Simulation-Based Game Models. [Internet] [Doctoral dissertation]. University of Michigan; 2008. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/60786.

Council of Science Editors:

Vorobeychik Y. Mechanism Design and Analysis Using Simulation-Based Game Models. [Doctoral Dissertation]. University of Michigan; 2008. Available from: http://hdl.handle.net/2027.42/60786

21. Gorski, Nicholas A. Learning To Use Memory.

Degree: PhD, Computer Science & Engineering, 2012, University of Michigan

 This thesis is a comprehensive empirical exploration of using reinforcement learning to learn to use simple forms of working memory. Learning to use memory involves… (more)

Subjects/Keywords: Reinforcement Learning; Memory; Artificial Intelligence; Sequential Decision Making; Computer Science; Engineering

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APA (6th Edition):

Gorski, N. A. (2012). Learning To Use Memory. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/91491

Chicago Manual of Style (16th Edition):

Gorski, Nicholas A. “Learning To Use Memory.” 2012. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/91491.

MLA Handbook (7th Edition):

Gorski, Nicholas A. “Learning To Use Memory.” 2012. Web. 11 Aug 2020.

Vancouver:

Gorski NA. Learning To Use Memory. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/91491.

Council of Science Editors:

Gorski NA. Learning To Use Memory. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/91491

22. Witwicki, Stefan J. Abstracting Influences for Efficient Multiagent Coordination Under Uncertainty.

Degree: PhD, Computer Science & Engineering, 2011, University of Michigan

 When planning optimal decisions for teams of agents acting in uncertain domains, conventional methods explicitly coordinate all joint policy decisions and, in doing so, are… (more)

Subjects/Keywords: Multiagent Coordination; Optimal Planning; Sequential Decision Making; Agent Interaction Structure; Decentralized Partially Observable Markov Decision Process; Influence Based Policy Abstraction; Computer Science; Engineering

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APA (6th Edition):

Witwicki, S. J. (2011). Abstracting Influences for Efficient Multiagent Coordination Under Uncertainty. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/84614

Chicago Manual of Style (16th Edition):

Witwicki, Stefan J. “Abstracting Influences for Efficient Multiagent Coordination Under Uncertainty.” 2011. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/84614.

MLA Handbook (7th Edition):

Witwicki, Stefan J. “Abstracting Influences for Efficient Multiagent Coordination Under Uncertainty.” 2011. Web. 11 Aug 2020.

Vancouver:

Witwicki SJ. Abstracting Influences for Efficient Multiagent Coordination Under Uncertainty. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/84614.

Council of Science Editors:

Witwicki SJ. Abstracting Influences for Efficient Multiagent Coordination Under Uncertainty. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/84614

23. Duong, Quang A. Graphical Multiagent Models.

Degree: PhD, Computer Science and Engineering, 2012, University of Michigan

 I introduce Graphical Multiagent Models (GMMs): probabilistic graphical models that capture agent interactions in factored representations for efficient inference about agent behaviors. The graphical model… (more)

Subjects/Keywords: Multiagent Reasoning; Graphical Models; Dynamics Behavior; Structure Learning; Computer Science; Engineering; Science

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APA (6th Edition):

Duong, Q. A. (2012). Graphical Multiagent Models. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/95999

Chicago Manual of Style (16th Edition):

Duong, Quang A. “Graphical Multiagent Models.” 2012. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/95999.

MLA Handbook (7th Edition):

Duong, Quang A. “Graphical Multiagent Models.” 2012. Web. 11 Aug 2020.

Vancouver:

Duong QA. Graphical Multiagent Models. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/95999.

Council of Science Editors:

Duong QA. Graphical Multiagent Models. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/95999

24. Bloch, Mitchell. Computationally Efficient Relational Reinforcement Learning.

Degree: PhD, Computer Science & Engineering, 2018, University of Michigan

 Relational Reinforcement Learning (RRL) is a technique that enables Reinforcement Learning (RL) agents to generalize from their experience, allowing them to learn over large or… (more)

Subjects/Keywords: Relational Reinforcement Learning; Rete; Adaptive Tile Coding; Online Learning; Sequential Decision Making; Computer Science; Engineering

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APA (6th Edition):

Bloch, M. (2018). Computationally Efficient Relational Reinforcement Learning. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/145859

Chicago Manual of Style (16th Edition):

Bloch, Mitchell. “Computationally Efficient Relational Reinforcement Learning.” 2018. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/145859.

MLA Handbook (7th Edition):

Bloch, Mitchell. “Computationally Efficient Relational Reinforcement Learning.” 2018. Web. 11 Aug 2020.

Vancouver:

Bloch M. Computationally Efficient Relational Reinforcement Learning. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/145859.

Council of Science Editors:

Bloch M. Computationally Efficient Relational Reinforcement Learning. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/145859

25. Oh, Junhyuk. Efficient Deep Reinforcement Learning via Planning, Generalization, and Improved Exploration.

Degree: PhD, Computer Science & Engineering, 2018, University of Michigan

 Reinforcement learning (RL) is a general-purpose machine learning framework, which considers an agent that makes sequential decisions in an environment to maximize its reward. Deep… (more)

Subjects/Keywords: Deep Reinforcement Learning; Computer Science; Engineering

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APA (6th Edition):

Oh, J. (2018). Efficient Deep Reinforcement Learning via Planning, Generalization, and Improved Exploration. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/145829

Chicago Manual of Style (16th Edition):

Oh, Junhyuk. “Efficient Deep Reinforcement Learning via Planning, Generalization, and Improved Exploration.” 2018. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/145829.

MLA Handbook (7th Edition):

Oh, Junhyuk. “Efficient Deep Reinforcement Learning via Planning, Generalization, and Improved Exploration.” 2018. Web. 11 Aug 2020.

Vancouver:

Oh J. Efficient Deep Reinforcement Learning via Planning, Generalization, and Improved Exploration. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/145829.

Council of Science Editors:

Oh J. Efficient Deep Reinforcement Learning via Planning, Generalization, and Improved Exploration. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/145829

26. Lee, Chansoo. Analysis of Perturbation Techniques in Online Learning.

Degree: PhD, Computer Science & Engineering, 2018, University of Michigan

 The most commonly used regularization technique in machine learning is to directly add a penalty function to the optimization objective. For example, L2 regularization is… (more)

Subjects/Keywords: online learning; machine learning; differential privacy; Mathematics; Science

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APA (6th Edition):

Lee, C. (2018). Analysis of Perturbation Techniques in Online Learning. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/143968

Chicago Manual of Style (16th Edition):

Lee, Chansoo. “Analysis of Perturbation Techniques in Online Learning.” 2018. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/143968.

MLA Handbook (7th Edition):

Lee, Chansoo. “Analysis of Perturbation Techniques in Online Learning.” 2018. Web. 11 Aug 2020.

Vancouver:

Lee C. Analysis of Perturbation Techniques in Online Learning. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/143968.

Council of Science Editors:

Lee C. Analysis of Perturbation Techniques in Online Learning. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/143968

27. Jiang, Nan. A Theory of Model Selection in Reinforcement Learning.

Degree: PhD, Computer Science & Engineering, 2017, University of Michigan

 Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to accomplish sequential decision-making tasks from experience. Applications of RL are found in… (more)

Subjects/Keywords: reinforcement learning; Computer Science; Engineering

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APA (6th Edition):

Jiang, N. (2017). A Theory of Model Selection in Reinforcement Learning. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138518

Chicago Manual of Style (16th Edition):

Jiang, Nan. “A Theory of Model Selection in Reinforcement Learning.” 2017. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/138518.

MLA Handbook (7th Edition):

Jiang, Nan. “A Theory of Model Selection in Reinforcement Learning.” 2017. Web. 11 Aug 2020.

Vancouver:

Jiang N. A Theory of Model Selection in Reinforcement Learning. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/138518.

Council of Science Editors:

Jiang N. A Theory of Model Selection in Reinforcement Learning. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138518

28. Li, Ning Hui. The Goal Re-activation Problem in Cognitive Architectures.

Degree: PhD, Computer Science and Engineering, 2016, University of Michigan

 Intelligent agents in the real world have to manage multiple goals. However, the pursuit of some goals may only be possible under specific conditions which,… (more)

Subjects/Keywords: cognitive architecture; prospective memory; Computer Science; Engineering

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APA (6th Edition):

Li, N. H. (2016). The Goal Re-activation Problem in Cognitive Architectures. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/120676

Chicago Manual of Style (16th Edition):

Li, Ning Hui. “The Goal Re-activation Problem in Cognitive Architectures.” 2016. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/120676.

MLA Handbook (7th Edition):

Li, Ning Hui. “The Goal Re-activation Problem in Cognitive Architectures.” 2016. Web. 11 Aug 2020.

Vancouver:

Li NH. The Goal Re-activation Problem in Cognitive Architectures. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/120676.

Council of Science Editors:

Li NH. The Goal Re-activation Problem in Cognitive Architectures. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/120676

29. Shvartsman, Michael. Adaptive Eye Movement Control in a Simple Linguistic Task.

Degree: PhD, Psychology, 2014, University of Michigan

 This dissertation pursues a computationally rational analysis of eye movements in a simple list-reading task. The strength of the computationally rational approach is in the… (more)

Subjects/Keywords: eye movements; eyetracking; computationally rational analysis; computational modeling; lexical decision; sequential sampling; Psychology; Social Sciences

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APA (6th Edition):

Shvartsman, M. (2014). Adaptive Eye Movement Control in a Simple Linguistic Task. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/110380

Chicago Manual of Style (16th Edition):

Shvartsman, Michael. “Adaptive Eye Movement Control in a Simple Linguistic Task.” 2014. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/110380.

MLA Handbook (7th Edition):

Shvartsman, Michael. “Adaptive Eye Movement Control in a Simple Linguistic Task.” 2014. Web. 11 Aug 2020.

Vancouver:

Shvartsman M. Adaptive Eye Movement Control in a Simple Linguistic Task. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/110380.

Council of Science Editors:

Shvartsman M. Adaptive Eye Movement Control in a Simple Linguistic Task. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/110380

30. Xu, Joseph Zhen Ying. Learning Integrated Relational and Continuous Action Models for Continuous Domains.

Degree: PhD, Computer Science & Engineering, 2013, University of Michigan

 Long-living autonomous agents must be able to learn to perform competently in novel environments. One important aspect of competence is the ability to plan, which… (more)

Subjects/Keywords: Action Modeling; Learning; Combined Symbolic/Continuous Representation; Computer Science; Engineering

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APA (6th Edition):

Xu, J. Z. Y. (2013). Learning Integrated Relational and Continuous Action Models for Continuous Domains. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/102383

Chicago Manual of Style (16th Edition):

Xu, Joseph Zhen Ying. “Learning Integrated Relational and Continuous Action Models for Continuous Domains.” 2013. Doctoral Dissertation, University of Michigan. Accessed August 11, 2020. http://hdl.handle.net/2027.42/102383.

MLA Handbook (7th Edition):

Xu, Joseph Zhen Ying. “Learning Integrated Relational and Continuous Action Models for Continuous Domains.” 2013. Web. 11 Aug 2020.

Vancouver:

Xu JZY. Learning Integrated Relational and Continuous Action Models for Continuous Domains. [Internet] [Doctoral dissertation]. University of Michigan; 2013. [cited 2020 Aug 11]. Available from: http://hdl.handle.net/2027.42/102383.

Council of Science Editors:

Xu JZY. Learning Integrated Relational and Continuous Action Models for Continuous Domains. [Doctoral Dissertation]. University of Michigan; 2013. Available from: http://hdl.handle.net/2027.42/102383

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