A Particle Filtering Based Approach to Approximating Interactive POMDPs

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 20

Markov Decision Processes and Uncertainty

POMDPs provide a principled framework for sequential planning in single agent settings. An extension of POMDPs to multiagent 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 I-POMDPs based on particle filtering (PF). We utilize the