Reasoning about knowledge
Chernoff-Hoeffding Bounds for Applications with Limited Independence
SIAM Journal on Discrete Mathematics
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Value-Directed Sampling Methods for POMDPs
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Sampling Methods for Action Selection in Influence Diagrams
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Approximating state estimation in multiagent settings using particle filters
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A framework for sequential planning in multi-agent settings
Journal of Artificial Intelligence Research
Exact solutions of interactive POMDPs using behavioral equivalence
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Approximate state estimation in multiagent settings with continuous or large discrete state spaces
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Graphical models for interactive POMDPs: representations and solutions
Autonomous Agents and Multi-Agent Systems
Approximate solutions of interactive dynamic influence diagrams using model clustering
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Generalized point based value iteration for interactive POMDPs
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Monte Carlo sampling methods for approximating interactive POMDPs
Journal of Artificial Intelligence Research
Memory-bounded dynamic programming for DEC-POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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POMDPs provide a principled framework for sequential planning in single agent settings. An extension of POMDPs to multi agent settings, called interactive POMDPs (I-POMDPs), replaces POMDP belief spaces with interactive hierarchical belief systems which represent an agent's belief about the physical world, about beliefs of the other agent(s), about their beliefs about others' beliefs, and so on. This modification makes the difficulties of obtaining solutions due to complexity of the belief and policy spaces even more acute. We describe a method for obtaining approximate solutions to IPOMDPs based on particle filtering (PF). We utilize the interactive PF which descends the levels of interactive belief hierarchies and samples and propagates beliefs at each level. The interactive PF is able to deal with the belief space complexity, but it does not address the policy space complexity. We provide experimental results and chart future work.