skip to main content
10.1145/1329125.1329213acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaamasConference Proceedingsconference-collections
research-article

Exploiting factored representations for decentralized execution in multiagent teams

Published: 14 May 2007 Publication History

Abstract

In many cooperative multiagent domains, there exist some states in which the agents can act independently and others in which they need to coordinate with their teammates. In this paper, we explore how factored representations of state can be used to generate factored policies that can, with minimal communication, be executed distributedly by a multiagent team. The factored policies indicate those portions of the state where no coordination is necessary, automatically alert the agents when they reach a state in which they do need to coordinate, and determine what the agents should communicate in order to achieve this coordination. We evaluate the success of our approach experimentally by comparing the amount of communication needed by a team executing a factored policy to a team that needs to communicate in every timestep.

References

[1]
D. S. Bernstein, R. Given, N. Immerman, and S. Zilberstein. The complexity of centralized control of Markov decision processes. Mathematics of Operations Research, 2002.
[2]
C. Boutilier, R. Dearden, and M. Goldszmidt. Stochastic dynamic programming with factored representations. Artificial Intelligence, 2000.
[3]
C. Boutilier, N. Friedman, M. Goldszmidt, and D. Koller. Context-specific independence in Bayesian networks. In Uncertainty in Artificial Intelligence, 1996.
[4]
T. Dean and K. Kanazawa. A model for reasoning about persistence and causation. Computational Intelligence Journal, 1989.
[5]
C. V. Goldman and S. Zilberstein. Optimizing information exchange in cooperative multi-agent systems. In International Joint Conferences on Autonomous Agents and multi-Agent Systems, 2003.
[6]
C. V. Goldman and S. Zilberstein. Decentralized control of cooperative systems: Categorization and complexity analysis. Journal of AI Research, 2004.
[7]
C. Guestrin and G. Gordon. Distributed planning in hierarchical factored MDPs. In Uncertainty in Artificial Intelligence, 2002.
[8]
C. Guestrin, S. Venkataraman, and D. Koller. Context specific multiagent coordination and planning with factored MDPs. In AAAI Spring Symposium, 2002.
[9]
E. Hansen and Z. Feng. Dynamic programming for POMDPs using a factored state representation. In International Conference on AI Planning Systems, 2000.
[10]
J. Hoey, R. St-Aubin, A. Hu, and C. Boutilier. SPUDD: Stochastic planning using decision diagrams. In Uncertainty in Artificial Intelligence, 1999.
[11]
R. Nair, M. Roth, M. Yokoo, and M. Tambe. Communication for improving policy computation in distributed POMDPs. In International Joint Conferences on Autonomous Agents and multi-Agent Systems, 2004.
[12]
D. V. Pynadath and M. Tambe. The communicative Multiagent Team Decision Problem: Analyzing teamwork theories and models. Journal of AI Research, 2002.
[13]
M. Roth, R. Simmons, and M. Veloso. Reasoning about joint beliefs for execution-time communication decisions. In International Joint Conferences on Autonomous Agents and multi-Agent Systems, 2005.

Cited By

View all
  • (2022)Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) SystemsAerospace10.3390/aerospace90904889:9(488)Online publication date: 31-Aug-2022
  • (2015)Multi-robot task acquisition through sparse coordination2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2015.7353765(2823-2828)Online publication date: Sep-2015
  • (2015)An extended version of opportunity cost algorithm for communication decisionsEvolving Systems10.1007/s12530-015-9138-07:1(41-60)Online publication date: 23-Sep-2015
  • Show More Cited By

Index Terms

  1. Exploiting factored representations for decentralized execution in multiagent teams

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    AAMAS '07: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
    May 2007
    1585 pages
    ISBN:9788190426275
    DOI:10.1145/1329125
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    • IFAAMAS

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 14 May 2007

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. communication
    2. decentralized execution
    3. multiagent MDP

    Qualifiers

    • Research-article

    Funding Sources

    Conference

    AAMAS07
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 20 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) SystemsAerospace10.3390/aerospace90904889:9(488)Online publication date: 31-Aug-2022
    • (2015)Multi-robot task acquisition through sparse coordination2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2015.7353765(2823-2828)Online publication date: Sep-2015
    • (2015)An extended version of opportunity cost algorithm for communication decisionsEvolving Systems10.1007/s12530-015-9138-07:1(41-60)Online publication date: 23-Sep-2015
    • (2013)Modeling information exchange opportunities for effective human-computer teamworkArtificial Intelligence10.1016/j.artint.2012.11.007195(528-550)Online publication date: 1-Feb-2013
    • (2013)Heuristic Planning for Decentralized MDPs with Sparse InteractionsDistributed Autonomous Robotic Systems10.1007/978-3-642-32723-0_24(329-343)Online publication date: 2013
    • (2012)The social life of robotsCommunications of the ACM10.1145/2076450.207645755:2(19-21)Online publication date: 1-Feb-2012
    • (2012)ACTION FAILURE RECOVERY VIA MODEL‐BASED DIAGNOSIS AND CONFORMANT PLANNINGComputational Intelligence10.1111/j.1467-8640.2012.00444.x29:2(233-280)Online publication date: 4-Jul-2012
    • (2012)A novel collaboration and communication decision based on multi-agent in wireless sensor network2012 IEEE 14th International Conference on Communication Technology10.1109/ICCT.2012.6511262(459-463)Online publication date: Nov-2012
    • (2012)Multiagent decision by partial evaluationProceedings of the 25th Canadian conference on Advances in Artificial Intelligence10.1007/978-3-642-30353-1_21(242-254)Online publication date: 28-May-2012
    • (2012)Decentralized POMDPsReinforcement Learning10.1007/978-3-642-27645-3_15(471-503)Online publication date: 2012
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media