A Context-Based Approach to Detecting Miscreant Behavior and Collusion in Open Multiagent Systems

  • Larry Whitsel
  • Roy Turner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6967)

Abstract

Most multiagent systems (MAS) either assume cooperation on the part of the agents or assume that the agents are completely self-interested, for example, in the case of bidding and other market-based approaches. However, an interesting class of MAS is one that is fundamentally cooperative, yet open, and in which one or more of the agents may be self-interested. In such systems, there is the potential for agents to misbehave, i.e., to be miscreants. Detecting this is tricky and context-dependent. Even more difficult is the problem of detecting collusion between agents.

In this paper, we report on a project that is beginning to address this problem using a context-based approach. Features of the MAS’ situation are used by a subset of the agents to identify it as an instance of one or more known contexts. Knowledge the agent(s) have about those contexts can then be used to directly detect miscreant behavior or collusion or to select the appropriate technique for the context with which to do so. The work is based on context-mediated behavior (CoMB), and it develops a new form of collusion detection called society-level analysis of motives (SLAM).

Keywords

Multiagent System Social Trust Reputation System Covert Channel Context Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Durfee, E.H.: Distributed problem solving and planning. In: Weiss, G. (ed.) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge (1999)Google Scholar
  2. 2.
    Falcone, R., Castelfranchi, C.: Social trust: A cognitive approach. In: Castelfranchi, C., Tan, Y. (eds.) Trust and Deception in Virtual Societies, pp. 55–90. Kluwer Academic Publishers, Dordrecht (2001)CrossRefGoogle Scholar
  3. 3.
    Iszuierdo, L., Izquierdo, S.: Dynamics of the Bush–Mosteller learning algorithm in 2x2 games. In: Weber, C., Elshaw, M., Mayer, N. (eds.) Reinforcement Learning: Theory and Applications, p. 424. I-Tech Education and Publishing, Vienna (2008)Google Scholar
  4. 4.
    Miller, R.A., Pople, H.E., Myers, J.D.: INTERNIST–1, an experimental computer-based diagnostic consultant for general internal medicine. New England Journal of Medicine 307, 468–476 (1982)CrossRefGoogle Scholar
  5. 5.
    Moskowitz, I., Kang, M.: Covert channels-here to stay? In: Reggio, G., Astesiano, E., Tarlecki, A. (eds.) Abstract Data Types 1994 and COMPASS 1994. LNCS, vol. 906, pp. 235–243. Springer, Heidelberg (1995)Google Scholar
  6. 6.
    Mui, L., Mohtashemi, M., Halberstadt, A.: Notions of reputation in multi-agents systems: a review. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2002, Part 1, pp. 280–287. ACM, New York (2002), http://doi.acm.org/10.1145/544741.544807
  7. 7.
    Sandholm, T.: Distributed rational decision making. In: Weiss, G. (ed.) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge (1999)Google Scholar
  8. 8.
    Turner, R.M.: Adaptive Reasoning for Real-World Problems: A Schema-Based Approach. Lawrence Erlbaum Associates, Hillsdale (1994)Google Scholar
  9. 9.
    Turner, R.M.: Context-mediated behavior for intelligent agents. International Journal of Human–Computer Studies 48(3), 307–330 (1998)CrossRefGoogle Scholar
  10. 10.
    Whitsel, L.T.: A Simulator for Rule-based Agent Trust Decisions. Master’s thesis, University of Maine, Department of Computer Science, Orono, Maine (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Larry Whitsel
    • 1
  • Roy Turner
    • 1
  1. 1.School of Computing and Information ScienceUniversity of MaineOronoUSA

Personalised recommendations