Results 211 to 220 of about 13,931 (229)

Optimizing fixed-size stochastic controllers for POMDPs and decentralized POMDPs

Autonomous Agents and Multi-Agent Systems, 2009
POMDPs and their decentralized multiagent counterparts, DEC-POMDPs, offer a rich framework for sequential decision making under uncertainty. Their high computational complexity, however, presents an important research challenge. One way to address the intractable memory requirements of current algorithms is based on representing agent policies as ...
Amato, C, Bernstein, DS, Zilberstein, S
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POMDP Filter: Pruning POMDP Value Functions with the Kaczmarz Iterative Method

2010
In recent years, there has been significant interest in developing techniques for finding policies for Partially Observable Markov Decision Problems (POMDPs). This paper introduces a new POMDP filtering technique that is based on Incremental Pruning [1], but relies on geometries of hyperplane arrangements to compute for optimal policy.
Eddy C. Borera, Larry D. Pyeatt, Arisoa S. Randrianasolo, Madhi Naser-Moghadasi   +3 more
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The Decentralized POMDP Framework

2016
In this chapter we formally define the Dec-POMDP model. It is a member of the family of discrete-time planning frameworks that are derived from the single-agent Markov decision process.
Frans A. Oliehoek, Christopher Amato
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αPOMDP: POMDP-based user-adaptive decision-making for social robots

Pattern Recognition Letters, 2019
Abstract In this work we present αPOMDP: a User-Adaptive Decision-Making technique for social robots. This technique is based on the classical POMDP formulation which we extend with novel aspects inspired by Reward Shaping and Model-Based Reinforcement Learning.
Martins, Gonçalo S., Al Tair, Hend, Santos, Luís, Dias, Jorge   +3 more
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Prediction-Directed Compression of POMDPs

2008 Seventh International Conference on Machine Learning and Applications, 2008
High dimensionality of belief space in partially observable Markov decision processes (POMDPs) is one of the major causes that severely restricts the applicability of this model. Previous studies have demonstrated that the dimensionality of a POMDP can eventually be reduced by transforming it into an equivalent predictive state representation (PSR). In
Boularias, A., Izadi, M., Chaib-Draa, B.
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Finite-Horizon Dec-POMDPs

2016
In this chapter, we discuss issues that are specific to finite-horizon Dec-POMDPs. First, we formalize the goal of planning for Dec-POMDPs by introducing optimality criteria and policy representations that are applicable in the finite-horizon case.
Frans A. Oliehoek, Christopher Amato
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Affective Dialogue Management Using Factored POMDPs

2010
Partially Observable Markov Decision Processes (POMDPs) have been demonstrated empirically to be good models for robust spoken dialogue design. This chapter shows that such models are also very appropriate for designing affective dialogue systems. We describe how to model affective dialogue systems using POMDPs and propose a novel approach to develop ...
Bui Huu Trung, B.H.T., Zwiers, Jakob, Poel, Mannes, Nijholt, Antinus   +3 more
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Task-Based Decomposition of Factored POMDPs

IEEE Transactions on Cybernetics, 2014
Recently, partially observable Markov decision processes (POMDP) solvers have shown the ability to scale up significantly using domain structure, such as factored representations. In many domains, the agent is required to complete a set of independent tasks.
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Grasping POMDPs

Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007
Kaijen Hsiao, Leslie Pack Kaelbling, Tomas Lozano-Perez   +2 more
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POMDP Planning at Roundabouts

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 2021
Henrik Bey, Moritz Sackmann, Alexander Lange, Jorn Thielecke   +3 more
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