Prioritized Shaping of Models for Solving DEC-POMDPs
Abstract
An interesting class of multi-agent POMDP planning problems can be solved by having agents iteratively solve individual POMDPs, find interactions with other individual plans, shape their transition and reward functions to encourage good interactions and discourage bad ones and then recompute a new plan. D-TREMOR showed that this approach can allow distributed planning for hundreds of agents. However, the quality and speed of the planning process depends on the prioritization scheme used. Lower priority agents shape their models with respect to the models of higher priority agents. In this paper, we introduce a new prioritization scheme that is guaranteed to converge and is empirically better, in terms of solution quality and planning time, than the existing prioritization scheme for some problems.
BibTeX
@conference{Varakantham-2012-7512,author = {Pradeep R. Varakantham and William Yeoh and Prasanna Velagapudi and Katia Sycara and Paul Scerri},
title = {Prioritized Shaping of Models for Solving DEC-POMDPs},
booktitle = {Proceedings of 11th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '12)},
year = {2012},
month = {June},
volume = {3},
pages = {1269 - 1270},
}