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Surviving in a Hostile Multi-agent Environment: How Simple Affective States Can Aid in the Competition for Resources

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Advances in Artificial Intelligence (Canadian AI 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1822))

Abstract

In this paper, I will argue that agents with simple affective inner states (that can be interpreted as “hunger” and “mood”) can have an advantage over agents without such states if these states are used to modulate the agents’ behavior in specific ways. The claim will be confirmed using results from experiments done in a simulation of a multi-agent environment, in which agents have to compete for resources in order to survive.

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References

  1. Arkin, R. C.: Behavior-Based Robotics. MIT Press, Cambridge, MA (1998)

    Google Scholar 

  2. Arkin, R. C.: Motor Schema-Based Mobile Robot Navigation. International Journal of Robotic Research, Vol. 8, No. 4, (1989) 92–112

    Article  Google Scholar 

  3. Breland, K., and Breland, M.: The misbehavior of organisms. American Psychologist, 16, (1961) 681–684

    Article  Google Scholar 

  4. Braitenberg, V.: Vehicle: Experiments in Synthetic Psychology. MIT Press, Cambridge, MA (1984)

    Google Scholar 

  5. Brooks, R.: Intelligence Without Reason, A.I. Memo No. 1227, MIT AI Laboratory, April (1990)

    Google Scholar 

  6. Brooks, R.: A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation, Vol. RA-2,1, (1986) 14–23

    Google Scholar 

  7. Domjan, M.: Formulation of a behavior system for sexual conditioning. Psychonomic Bulletin, 1(4), (1994) 439–450

    Google Scholar 

  8. Fanselow, M.S.: Neural organization of the defensive behavior system responsible for fear. Psychonomic Bulletin, 1(4), (1994) 429–438

    Google Scholar 

  9. Gardner, R.A. and Gardner, B.T.: Feedforward versus feedback: An ethological alternative to the law of effect. Behavioral Brain Sciences, 11, (1988) 429–447

    Article  Google Scholar 

  10. Lorenz, K.: The foundations of ethology. New York: Springer Verlag (1981)

    Google Scholar 

  11. Lorenz, K. and Leyhausen, P.: Motivation and Animal Behavior: An Ethological View. Van Nostrand Co., New York (1973)

    Google Scholar 

  12. Mochida, T., Ishiguro, A., Aoki, T., and Uchikawa, Y.: Behavior Arbitration for Autonomous Mobile Robots Using Emotion Mechanism. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’ 95), Pittsburgh, PA, (1995) 516–21

    Google Scholar 

  13. Moravec, H.: Mind Children: The Future of Robot and Human Intelligence. Harvard University Press, Cambridge, MA (1988)

    Google Scholar 

  14. Pfeiffer, R. and Scheier, Ch.: From Perception to Action: The Right Direction. In: Gaussier, P. and Nicoud, J.-D. (eds.): From Perception to Action, IEEE Computer Society Press, Los Alamos, CA, (1994) 1–11

    Chapter  Google Scholar 

  15. Salomon, R.: Self-organizing Neural Network Controllers for Mobile Robots (1996) (unpublished)

    Google Scholar 

  16. Sloman, A.: Architectural Requirements for Human-like Agents Both Natural and Artificial. (1999) (to appear in Human Cognition and Social Agent Technology, Ed. Kerstin Dautenhahn)

    Google Scholar 

  17. Timberlake, W.D. and Lucas, G.A.: Behavior systems and learning: From Misbehavior to general principles. In: Klein & Mowrer (eds.): Contemporary Learning Theories: Instrumental Conditioning and the Impact of Biological Constraints on Learning, Lawrence Earlbaum Assoc., Hillsdale, NJ (1989)

    Google Scholar 

  18. Tinbergen, N.: The study of instinct. Oxford: Oxford University Press, Clarendon Press (1951)

    Google Scholar 

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© 2000 Springer-Verlag Berlin Heidelberg

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Scheutz, M. (2000). Surviving in a Hostile Multi-agent Environment: How Simple Affective States Can Aid in the Competition for Resources. In: Hamilton, H.J. (eds) Advances in Artificial Intelligence. Canadian AI 2000. Lecture Notes in Computer Science(), vol 1822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45486-1_33

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  • DOI: https://doi.org/10.1007/3-540-45486-1_33

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67557-0

  • Online ISBN: 978-3-540-45486-1

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