Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs

Ekhlas Sonu, Yingke Chen, Prashant Doshi

Research output: Contribution to journalConference articlepeer-review

11 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)202-210
Number of pages9
JournalProceedings of the International Conference on Automated Planning and Scheduling
Publication statusPublished - 8 Apr 2015
Externally publishedYes


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