Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs
Abstract
Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Traditional I-POMDPs model this dependence on the actions of other agents using joint action and model spaces. Therefore, the solution complexity grows exponentially with the number of agents thereby complicating scalability. In this paper, we model and extend anonymity and context-specific independence -- problem structures often present in agent populations -- for computational gain. We empirically demonstrate the efficiency from exploiting these problem structures by solving a new multiagent problem involving more than 1,000 agents.
Cite
@article{arxiv.1503.07220,
title = {Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs},
author = {Ekhlas Sonu and Yingke Chen and Prashant Doshi},
journal= {arXiv preprint arXiv:1503.07220},
year = {2015}
}
Comments
8 page article plus two page appendix containing proofs in Proceedings of 25th International Conference on Autonomous Planning and Scheduling, 2015