Practical and Flexible Decision-Making Using Compilation in Time-Critical Environments

시간 제약적인 환경에서 컴파일 기법을 사용한 실질적이며 유연한 의사결정 방법

  • 노상욱 (가톨릭대학교 컴퓨터정보공학부)
  • Published : 2003.12.01

Abstract

To perform rational decision-making, autonomous agents need considerable computational resources. When other agents are present in the environment, these demands are even more severe. In these settings, it may be difficult for the agent to decide what to do in an acceptable time in multiagent situations that involve many agents. These problems motivate us to investigate ways in which the agents can be equipped with flexible decision-making procedures that enable them to function in a variety of situations in which decision-making time is important. The flexible decision-making methods explicitly consider a tradeoff between decision quality and computation time. Our framework limits resources used for agent deliberation and produces results that are not necessarily optimal, but provide autonomous agents with the best decision under time pressure. We validate our framework with experiments in a simulated anti-air defense domain. The experiments show that compiled rules reduce computation time while offering good performance.

여러 에이전트가 존재하는(multiagent) 환경에서 자율적인 에이전트들은 복잡하고 불확실한 환경뿐만 아니라 다른 에이전트들도 고려하여 자신의 결정을 수행하여야 하기 때문에 제한된 시간 내에 의사결정(decision-making)을 완료한다는 것은 실질적으로 불가능하다. 이러한 문제점을 극복하고 긴급한 상황에서 최적의 행동을 수행하기 위하여 자율적인 에이전트들에게 다양하고 유연한 의사결정 방법들을 제공한다. 이 방법들은 의사결정의 질적인 수준과 의사결정 소요시간을 고려하여 실질적인 에이전트의 의사결정을 가능하도록 한다. 유연한 의사결정 방법의 하나로 컴파일된 규칙의 사용을 제안하며, 자율적인 에이전트는 복잡한 실시간 환경에서 가능한 행동의 범위를 제한하기 위하여 조건-행동 규칙을 사용한다. 지대공 방어 환경에서 주어진 상황의 긴박한 정도에 따라 이에 적절한 행동을 자율적으로 수행하는 유연한 에이전트를 실험적으로 보인다.

Keywords

References

  1. Brooks, R.A., A robust layered control system for a mobile robot, IEEE Journal on Robotics and Automation, vol. RA-2, no. 1, pp. 14-23, Mar. 1986
  2. Dean, T., Decision-theoretic control of inference for time-critical applications, Artificial Intelligence, pp. 1-28, Nov. 1990
  3. Good, I.J., Twenty-seven principles of rationality, Foundations of Statistical Inference, V.P. Godambe and D.A. Sprott, Eds., pp. 108-141. Holt, Rinehart, and Winston, 1971
  4. Horvitz, E.J., Reasoning about beliefs and actions under computational resource constraints, In Proceedings of the 1987 Workshop on Uncertainty in Artificial Intelligence, 1987
  5. Horvitz, E.J., Cooper, G.F., and Heckerrnan, D.E., Reflection and action under scarce resources: Theoretical principles and theoretical study, In Proceedings of the 11th International Joint Conference on Artificial Intelligence, Detroit, Michigan, Aug. 1989, pp. 1121-1127
  6. Kortenkamp, D., Schreckenghost, D., and Bonasso, R.P., Real-time autonomous control of space habitats, AAAI Spring Symposium, March 2000, pp. 38-45
  7. Russell, S. J. and Wefald. E. H., Principles of Metareasoning, Journal of AI, Volume 49. pp. 361-395, 1991 https://doi.org/10.1016/0004-3702(91)90015-C
  8. Russell, S.J. and Subramanian, D., Provably bounded-optimal agents, Journal of Artificial Intelligence Research, Volume 2, pp. 575-609, 1995
  9. Russell, S.J., Rationality and Intelligence, Artificial Intelligence, Volume 94, pp. 57-77, 1997 https://doi.org/10.1016/S0004-3702(97)00026-X
  10. Simon, H.A., The Sciences of the Artificial, MIT Press, 1969
  11. Zilberstein, S. and Russell, S.J., Optimal composition of real-time systems, Artificial Intelligence, vol. 82, no. 1, pp. 181-213, 1996 https://doi.org/10.1016/0004-3702(94)00074-3
  12. Gmytrasiewicz, P.J. and Durfee, E.H., Rational coordination in multi-agent environments, Autonomous Agents and Multiagent Systems Journal, vol. 3, pp. 319-350, 2000 https://doi.org/10.1023/A:1010028119149
  13. Gmvtrasiewicz, P.J., Noh, S., and Kellogg, T., Bayesian update of recursive agent models, User Modeling and User-Adapted Interaction: An International Journal, vol. 8, no. 1/2, pp. 49-69, 1998 https://doi.org/10.1023/A:1008269427670
  14. Noh, S. and Gmytrasiewicz, P.J., Uncertain Knowledge Representation and Communicative Behavior in Coordinated Defense, Lecture Notes in Artificial Intelligence 1916, Issues in Agent Communication' pp. 281-300, Springer, 2000 https://doi.org/10.1007/10722777_19
  15. Noh, S. and Gmytrasiewicz, P.J., Towards Flexible Multi-Agent Decision-Making Under Time Pressure, In Proceedings of the Sixteenth Inter national Joint Conference on Artificial Intelligence, pp. 492-498, Stockholm, Sweden, August 1999
  16. Noh, S. and Gmytrasiewicz, P.J., Rational communicative behavior in anti-air defense, In Proceedings of the Third International Conference on Multi-Agent Systems, pp. 214-221, July 1998 https://doi.org/10.1109/ICMAS.1998.699052
  17. Noh, S. and Gmytrasiewicz, P.J., Coordination and Belief Update in a Distributed Anti-Air Environment, In Proceedings of the 31st Hawaii International Conference on System Sciences, Vol. V, pp. 142-151, Hawaii, January 1998 https://doi.org/10.1109/HICSS.1998.648307
  18. Agre, P.E. and Chapman, D., Pengi: An implementation of a theory of activity, In Proceedings of the National Conference on Artificial Intelligence, Seattle, Washington, pp, 268-272, 1987
  19. Fox, J. and Krause, P., Symbolic decision theory and autonomous systems, In Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence, CCLA, California, pp. 103-110, July 1991
  20. Bratman, M.E., Israel, D.J. and Pollack, M.E., Plans and resource-bounded practical reasoning, Journal of Computational Intelligence, vol. 4, pp. 349-355, 1988 https://doi.org/10.1111/j.1467-8640.1988.tb00284.x
  21. Rao, A.S. and Georgeff, M.P., An abstract architecture for rational agents, In Proceedings of the Knowledge Representation and Reasoning, pp. 439-449, 1992
  22. Rao, A.S. and Georgeff, M.P., BDI agents: From theory to practice, In Proceedings of the 1st International Conference on Multiagent Systems, pp. 312-319, July 1995
  23. Lesser, V.R, Reflections on the nature of multi-agent coordination and its implications for an agent architecture, Autonomous Agents and Multi-Agent Systems, vol. 1, no. 1, pp. 89-111, 1998 https://doi.org/10.1023/A:1010046623013
  24. Durfee, E.H., Practically coordinating, AI Magazine, vol. 20, no. 1, pp. 99-116, 1999
  25. Macfadzean, R.H.M., Surface-Based Air Defense System Analysis, Artech House, 1992
  26. Durfee, E.H. and Montgomery, T.A., MICE: A flexible testbed for intelligent coordination experiments' In Proceedings of the 1989 Distributed AI Workshop, pp. 25-40, Sept. 1989
  27. Clark, P. and Niblett, T., The CN2 Induction Algorithm, Machine Learning Journal, Vol. 3, No. 4, pp. 261-283, 1989 https://doi.org/10.1007/BF00116835
  28. Quinlan, J.R., C4.5 Programs for Machine Learning, Morgan Kaufmann, 1988
  29. Cameron-Jones, R.M. and Quinlan, J.R., Efficient top-down induction of logic programs, SIGART Bulletin, vol. 5, no. 1, pp, 33-42, Jan. 1994 https://doi.org/10.1145/181668.181676