Results 201 to 210 of about 13,931 (229)
Towards AI-based precision rehabilitation via contextual model-based reinforcement learning. [PDF]
J Neuroeng RehabilYe D, Luo H, Winstein C, Schweighofer N.
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Beliefs, compulsive behavior and reduced confidence in control. [PDF]
PLoS Comput BiolRigoux L, Stephan KE, Petzschner FH.
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Improved Intention Discovery with Classified Emotions in A Modified POMDP
, 2012 Sivaraman Sriram
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Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions. [PDF]
Health Care Manag SciWu Q, Han J, Yan Y, Kuo YH, Shen ZM.
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Faster Teaching via POMDP Planning
Cognitive Science, 2015AbstractHuman and automated tutors attempt to choose pedagogical activities that will maximize student learning, informed by their estimates of the student's current knowledge. There has been substantial research on tracking and modeling student learning, but significantly less attention on how to plan teaching actions and how the assumed student model
Anna N, Rafferty +3 more
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Scaling up POMDPs for Dialog Management: The ``Summary POMDP'' Method
IEEE Workshop on Automatic Speech Recognition and Understanding, 2005., 2005Partially observable Markov decision processes (POMDPs) have been shown to be a promising framework for dialog management in spoken dialog systems. However, to date, POMDPs have been limited to artificially small tasks. In this work, we present a novel method called a "summary POMDP" for scaling slot-filling POMDP-based dialog managers to cope with ...
J.D. Williams, S. Young
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Bayes-Adaptive Interactive POMDPs
Proceedings of the AAAI Conference on Artificial Intelligence, 2021We introduce the Bayes-Adaptive Interactive Partially Observable Markov Decision Process (BA-IPOMDP), the first multiagent decision model that explicitly incorporates model learning. As in I-POMDPs, the BA-IPOMDP agent maintains beliefs over interactive states, which include the physical states as well as the other agents’ models.
Brenda Ng +3 more
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2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), 2017
Many real-world multi-agent applications such as rescue operations require to dynamically assemble or disassemble teams to provide a service exploiting this flexibility. While Dec-POMDPs capture real-world uncertainty in multi-agent systems, they fail to use the team flexibility. This paper introduces a new model, called the Open Dec-POMDP, allowing us
Jonathan Cohen +2 more
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Many real-world multi-agent applications such as rescue operations require to dynamically assemble or disassemble teams to provide a service exploiting this flexibility. While Dec-POMDPs capture real-world uncertainty in multi-agent systems, they fail to use the team flexibility. This paper introduces a new model, called the Open Dec-POMDP, allowing us
Jonathan Cohen +2 more
openaire +1 more source