AIJan 23, 2013
Approximate Planning for Factored POMDPs using Belief State Simplification
arXiv:1301.6719v194 citations
Originality Synthesis-oriented
AI Analysis
This is an incremental improvement for researchers in AI planning and POMDPs.
The paper tackles planning for factored POMDPs by providing an algorithm that exploits the accuracy-efficiency tradeoff in belief state simplification, building on prior work by Kearns, Mansour, Ng, Boyen, and Koller.
We are interested in the problem of planning for factored POMDPs. Building on the recent results of Kearns, Mansour and Ng, we provide a planning algorithm for factored POMDPs that exploits the accuracy-efficiency tradeoff in the belief state simplification introduced by Boyen and Koller.