Predicting the Movements of Robot Teams Using Generative Models

  • Simon Butler
  • Yiannis Demiris

Abstract

When a robot plans its actions within an environment containing multiple robots, it is often necessary to take into account the actions and movements of the other robots to either avoid, counter, or cooperate with them, depending on the scenario. Our predictive system is based on the biologically-inspired, simulationtheoretic approach that uses internal generative models in single-robot applications. Here, we move beyond the single-robot case to illustrate how these generative models can predict the movements of the opponent’s robots, when applied to an adversarial scenario involving two robot teams. The system is able to recognise whether the robots are attacking or defending, and the formation they are moving in. It can then predict their future movements based on the recognised model. The results confirm that the speed of recognition and the accuracy of prediction depend on how well the models match the robots’ observed behaviour.

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References

  1. Beetz, M., Kirchlechner, B.: Computerized real-time analysis of football games. IEEE pervasive computing 4(3) (2005)Google Scholar
  2. Bui, H., Venkatesh, S., West, G.: Policy recognition in the abstract hidden markov models. Journal of Artificial Intelligence Research 17, 451–499 (2002)MATHMathSciNetGoogle Scholar
  3. Darken, R., Mcdowell, P., Johnson, E.: The Delta3D open source game engine. IEEE computer graphics and applications 25(3) (2005)Google Scholar
  4. Demiris, Y.: Prediction of intent in robotics and multi-agent systems. Cognitive Processing 8(3), 151–158 (2007)CrossRefGoogle Scholar
  5. Demiris, Y., Khadhouri, B.: Hierarchical attentive multiple models for execution and recognition of actions. Robotics and autonomous systems 54(5) (2006)Google Scholar
  6. Devaney, M., Ram, A.: Needles in a haystack: Plan recognition in large spatial domains involving multiple agents. In: National Conference on Artificial Intelligence (1998)Google Scholar
  7. Hesslow, G.: Conscious thought as simulation of behaviour and perception. Trends in Cognitive Sciences 6(6), 242–247 (2002)CrossRefGoogle Scholar
  8. Ji, E.M.: Distributed coordination control of multiagent systems while preserving connectedness. IEEE Transactions on Robotics 23(4), 693–703 (2007)CrossRefGoogle Scholar
  9. Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawai, E., Matsubara, H.: Robocup: A challenge problem for AI and robotics. In: Kitano, H. (ed.) RoboCup 1997. LNCS, vol. 1395, pp. 1–19. Springer, Heidelberg (1998)Google Scholar
  10. Sukthankar, G., Sycara, K.: Robust recognition of physical team behaviors using spatio-temporal models. In: AAMAS 2006: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems, pp. 638–645. ACM, New York (2006)CrossRefGoogle Scholar
  11. Takács, B., Butler, S., Demiris, Y.: Multi-agent behaviour segmentation via spectral clustering. In: Proceedings of the AAAI-2007, PAIR Workshop, pp. 74–81. AAAI Press, Menlo Park (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Simon Butler
    • 1
  • Yiannis Demiris
    • 1
  1. 1.EEE Dept.Imperial College LondonUK

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