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PRIMA
2007
Springer
14 years 1 months ago
Multiagent Planning with Trembling-Hand Perfect Equilibrium in Multiagent POMDPs
Multiagent Partially Observable Markov Decision Processes are a popular model of multiagent systems with uncertainty. Since the computational cost for finding an optimal joint pol...
Yuichi Yabu, Makoto Yokoo, Atsushi Iwasaki
ICML
1990
IEEE
13 years 11 months ago
Explanations of Empirically Derived Reactive Plans
Given an adequate simulation model of the task environment and payoff function that measures the quality of partially successful plans, competition-based heuristics such as geneti...
Diana F. Gordon, John J. Grefenstette
ICML
2008
IEEE
14 years 8 months ago
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
Partially Observable Markov Decision Processes (POMDPs) have succeeded in planning domains that require balancing actions that increase an agent's knowledge and actions that ...
Finale Doshi, Joelle Pineau, Nicholas Roy
ICRA
2010
IEEE
163views Robotics» more  ICRA 2010»
13 years 6 months ago
Exploiting domain knowledge in planning for uncertain robot systems modeled as POMDPs
Abstract— We propose a planning algorithm that allows usersupplied domain knowledge to be exploited in the synthesis of information feedback policies for systems modeled as parti...
Salvatore Candido, James C. Davidson, Seth Hutchin...
AGENTS
1998
Springer
13 years 11 months ago
Learning Situation-Dependent Costs: Improving Planning from Probabilistic Robot Execution
Physical domains are notoriously hard to model completely and correctly, especially to capture the dynamics of the environment. Moreover, since environments change, it is even mor...
Karen Zita Haigh, Manuela M. Veloso