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

Ekhlas Sonu, Y. (Yingke) Chen, P. (Prashant) Doshi

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    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.
    Original languageEnglish
    Publication statusPublished - 8 Apr 2015
    Event25th International Conference on Automated Planning and Scheduling - Jerusalem, Israel
    Duration: 7 Jun 201511 Jun 2015

    Conference

    Conference25th International Conference on Automated Planning and Scheduling
    CountryIsrael
    CityJerusalem
    Period7/06/1511/06/15

    Bibliographical note

    Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF Copyright IAAA. For full details see https://www.aaai.org/ocs/index.php/ICAPS/ICAPS15/about/submissions#copyrightNotice [Accessed 23/02/2018]

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  • Cite this

    Sonu, E., Chen, Y. Y., & Doshi, P. P. (2015). Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs. Paper presented at 25th International Conference on Automated Planning and Scheduling, Jerusalem, Israel. https://www.aaai.org/ocs/index.php/ICAPS/ICAPS15/paper/view/10601