Artificial Intelligence Review

, Volume 16, Issue 3, pp 225–253 | Cite as

Goal-Seeking Behavior in a Connectionist Model

  • Thomas E. Portegys


Goal-seeking behavior in a connectionist modelis demonstrated using the examples of foragingby a simulated ant and cooperativenest-building by a pair of simulated birds. Themodel, a control neural network, translatesneeds into responses. The purpose of this workis to produce lifelike behavior with agoal-seeking artificial neural network. Theforaging ant example illustrates theintermediation of neurons to guide the ant to agoal in a semi-predictable environment. In thenest-building example, both birds, executinggender-specific networks, exhibit socialnesting and feeding behavior directed towardmultiple goals.

computational neuroethology connectionism goal-seeking neural networks 


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  1. Anderson, C., Blackwell, P. G. & Cannings, C. (1997). Stochastic Simulation of Ants that Forage by Expectation. In Husbands P. & Harvey I. (eds.) Fourth European Conference on Artificial Life. Cambridge, MA: MIT Press.Google Scholar
  2. Albus, J. S. (1979). Mechanisms of Planning and Problem Solving in the Brain. Math. Biosci. 45: 247-293.Google Scholar
  3. Bonabeau, E., Dorigo, M. & Théraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press.Google Scholar
  4. Bonabeau, E. & Théraulaz, G. (2000). Swarm Smarts. Scientific American 282(3): 72-79.Google Scholar
  5. Drogoul, A. & Ferber, J. (1993). From Tom Thumb to the Dockers: Some Experiments with Foraging Robots. In Meyer J-A., Roitblat H. L. and Wilson S. W. (eds.) From Animals to Animats II: Proceedings of the Second International Conference on Simulation of Adaptive Behavior. Cambridge, MA: MIT Press.Google Scholar
  6. Dudai, Y. (1989). The Neurobiology of Memory. New York: Oxford University Press.Google Scholar
  7. Fu, L. (1994). Neural Networks in Computer Intelligence. McGraw-Hill, Inc.Google Scholar
  8. Goss, S. & Deneubourg, J.L. (1992). Harvesting by a Group of Robots. In Varela F.J. and Bourgine P. (eds.) Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life. Cambridge, MA: MIT Press.Google Scholar
  9. Hampson, S. E. (1990). Connectionist Problem Solving: Computational Aspects of Biological Learning. Birkhäuser Boston.Google Scholar
  10. Holmes, W. & Rall, W. (1992) Electrotonic Models of Neuronal Dendrites and Single Neuron Computation. In McKenna T. et al. (eds.) Single Neuron Computation. San Diego, CA: Academic Press.Google Scholar
  11. Hopfield, J. & Tank, D. (1986). Computing with Neural Circuits: A Model. Science 233: 625-633.Google Scholar
  12. Kodjabachian, J. & Meyer, J-A. (1998). Evolution and Development of Neural Controllers for Locomotion, Gradient-Following, and Obstacle-Avoidance in Artificial Insects. IEEE Transactions on Neural Networks 9(5): 796-812.Google Scholar
  13. Mataric, M. (1995). Designing and Understanding Adaptive Group Behavior. Adaptive Behavior 4(1): 51-80.Google Scholar
  14. McClelland, D. (1987). Human Motivation. Cambridge: Cambridge University Press.Google Scholar
  15. Munakata, T. (1998). Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms. New York: Springer-Verlag Inc.Google Scholar
  16. Murciano, A. & Millán, J. (1996). Learning Signaling Behaviors and Specialization in Cooperative Agents. Adaptive Behavior 5(1): 5-28.Google Scholar
  17. Nolfi, S. & Parisi, D. (1996). Learning to Adapt to Changing Environments in Evolving Neural Networks. Adaptive Behavior 5(1): 75-98.Google Scholar
  18. Parten, C. R. (1990). Handbook of Neural Computing Applications. Academic Press, Inc.Google Scholar
  19. Pfaff, D.W. (1982). Motivational Concepts: Definitions and Distinctions. In Pfaff E. (ed.) The Physiological Mechanisms of Motivation. New York: Springer-Verlag Inc.Google Scholar
  20. Portegys, T. (1986). GIL-an Experiment in Goal-directed Inductive Learning. Ph.D. dissertation, Northwestern University, Evanston (Available from UMI at Scholar
  21. Portegys, T. (1999). A Connectionist Model of Motivation. Proceedings of the International Joint Conference on Neural Networks (IJCNN'99). IEEE Catalog Number: 99CH36339C.Google Scholar
  22. Reynolds, C. W. (1987). Flocks, Herds, and Schools: A Distributed Behavior Model. Computer Graphics 21(4): 25-34.Google Scholar
  23. Roy, A. (1997) Panel Discussion at ICNN97 on Connectionist Learning. In Levine D. (ed.) Neural Networks 2(2).Google Scholar
  24. Schank, R. C., & Childers, P. G. (1984). The Cognitive Computer; On Language, Learning, and Artificial Intelligence. Addison-Wesley Publishing Company, Inc.Google Scholar
  25. Skinner, B. F. (1957). Verbal Behavior. New York: Appleton-Century-Crofts.Google Scholar
  26. Thompson, R., Berger, T. & Berry S. (1980). Brain Anatomy and Function. In Wittrock M. (ed.) The Brain and Psychology. Academic Press.Google Scholar
  27. Waltz, D. (1999). The Importance of Importance. AI Magazine 20(3): 18-35.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Thomas E. Portegys
    • 1
  1. 1.Lucent TechnologiesNapervilleUSA

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