A Particle Filtering Algorithm for Interactive POMDPs
Prashant Doshi, Piotr J. Gmytrasiewicz · 2004
Interactive POMDP (I-POMDP) is a stochastic optimization framework for sequential planning in multiagent settings. It represents a direct generalization of POMDPs to multiagent cases. Expectedly, I-POMDPs also suffer from a high computational complexity, thereby motivating approximation schemes. In this paper, we propose using a particle filtering algorithm for approximating the I-POMDP belief update process. Since the belief update is a key step in solving I-POMDPs, approximating it will reduce the time its takes to compute the solution.