Cost, Precision, and Task Structure in Aggression-Based Arbitration for Minimalist Robot Cooperation

  • Tanushree Mitra
  • Dylan A. Shell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7426)


This paper reexamines a multi-robot transportation task, introduced and studied by Vaughan and his collaborators, in which constrained space induces inter-agent interference. Previous research demonstrated the effectiveness of an arbitration mechanism inspired by biological signaling where the level of aggression displayed by each agent effectively prioritizes the limited resources. This paper shows that apart from determining the correct fitness of an individual several other factors, such as signaling cost, precision of the outcome and properties of the resource and task are key to determine an effective arbitration technique. Based on these factors we present a taxonomy of the arbitration mechanisms.

The large signalling costs incurred by our simple robots using minimal set of sensors permit us to identify scenarios in which a dominance hierarchy outperforms, not only to no arbitration, but also aggression-based mechanisms. We identify how memory of past interactions can be used to the advantage of an agent, albeit with a trade-off between cost and outcome accuracy. We also show that the importance of a particular aggressive interaction to long-term task performance is not trivial to determine and depends on the task structure. Results help us identify instances where agents may manipulate interactions to alter the frequency and duration of aggressive encounters, affecting the overall task performance.


Dominance Hierarchy Aggressive Interaction Task Structure Aggressive Encounter Physical Robot 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brown, S., Zuluaga, M., Zhang, Y., Vaughan, R.T.: Rational aggressive behaviour reduced interferrence in a mobile robot team. In: Proc. International Conference on Advanced Robotics, Seattle, WA, U.S.A. (July 2005)Google Scholar
  2. 2.
    Enquist, M.: Communication during aggressive interactions with particular reference to variation in choice of behaviour. Animal Behav. 33(4), 1152 (1985)CrossRefGoogle Scholar
  3. 3.
    Goldberg, D., Matarić, M.J.: Interference as a Tool for Designing and Evaluating Multi-Robot Controllers. In: Proc. AAAI National Conference on Artificial Intelligence, Providence, RI, U.S.A., pp. 637–642 (July 1997)Google Scholar
  4. 4.
    Stuart-Fox, D.: Testing game theory models: fighting ability and decision rules in chameleon contests. Proc. Rol. Soc. B: Biological Sciences 273(1593), 1555 (2006)CrossRefGoogle Scholar
  5. 5.
    Vaughan, R.T., Støy, K., Sukhatme, G.S., Matarić, M.J.: Go ahead, make my day: Robot conflict resolution by aggressive competition. In: From Animals to Animats: Proc. Simulation of Adaptive Behavior, Paris, France, pp. 491–500 (August 2000)Google Scholar
  6. 6.
    Zuluaga, M., Vaughan, R.T.: Reducing spatial interference in robot teams by local-investment aggression. In: Proc. IEEE International Conference on Intelligent Robots and Systems, Edmonton, Alberta (August 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tanushree Mitra
    • 1
  • Dylan A. Shell
    • 1
  1. 1.Department of Computer Science and EngineeringTexas A&M UniversityUSA

Personalised recommendations