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Cooperative Behavior of Agents Based on Potential Field

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3690))

Abstract

In non-communicative environment, it is important for agents to assess the situation prevailing in the system, especially to anticipate other agents’ intentions. In this paper, we argue in favor of cooperation among agents and propose a new method to utilize potential field as a tool for estimation of the environment. In our method, potential of environment gives agents some criteria to assess environmental situations from their own perspective. The potential of each object represents its influence on the environment and the environmental potential, i.e.,summation of each object’s potential, represents global situation of the environment. Agents’ decision of their behavior will be done by refining the policy obtained from potential. We use a trash collecting problem as an example to show the effectiveness of our method by some sets of experiments of the trash collecting problem. We also discuss the applicability of our method to hybrid systems or environments where agent’s range of vision are limited.

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References

  1. Shehory, O., Kraus, S., Yadgar, O.: Emergent cooperative goalsatisfaction in large-scale automated-agent systems. Artificial Intelligence 110, 1–55 (1999)

    Article  MATH  Google Scholar 

  2. Smith, R.G.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers C-29(12), 1104–1113 (1980)

    Google Scholar 

  3. Conry, S.E., Kuwabara, K., Lesser, V.R., Meyer, R.A.: Multistage negotiation for distributed constraint satisfaction. IEEE Transaction on Systems, Man, and Cybernetics 21(6), 1462–1477 (1991)

    Article  MATH  Google Scholar 

  4. Shehory, O., Kraus, S.: Methods for task allocation via agent coalition formation. Artificial Intelligence 101, 165–200 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  5. Hogg, L.M.J., Jennings, N.R.: Socially intelligent reasoning for autonomous agenst. IEEE Transactions on System, Man, and Cybernetics–Part A: Systems and Humans 31(5), 381–393 (2001)

    Article  Google Scholar 

  6. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

    Google Scholar 

  7. Sun, R., Sessions, C.: Multi-agent reinforcement learning with bidding for segmenting action sequences. In: From Animals to Animals: Proceedings of the International conference of Simulation of Adaptive Behavior (SAB 2000). MIT Press, Cambridge (2000)

    Google Scholar 

  8. Arai, S., Sycara, K., Payne, T.R.: Experience-based reinforcement learning to acquire effective behavior in a multiagent domain. In: Mizoguchi, R., Slaney, J.K. (eds.) PRICAI 2000. LNCS, vol. 1886, pp. 125–135. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  9. Wiering, M., Schmidhuber, J.: HQ-learning. Adaptive Behavior 6(2), 219–246 (1997)

    Article  Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Katoh, T., Hoshi, K., Shiratori, N. (2005). Cooperative Behavior of Agents Based on Potential Field. In: Pěchouček, M., Petta, P., Varga, L.Z. (eds) Multi-Agent Systems and Applications IV. CEEMAS 2005. Lecture Notes in Computer Science(), vol 3690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559221_24

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  • DOI: https://doi.org/10.1007/11559221_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29046-9

  • Online ISBN: 978-3-540-31731-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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