Abstract
We have developed a new efficient neural network-based algorithm for Alife application in a competitive world whereby the effects of interactions between organisms are evaluated in a weak form by exploiting the position of nearest food elements into consideration but not the positions of the other competing organisms. Two online learning algorithms, an instructive ASL (adaptive supervised learning) and an evaluative feedback-oriented RL (reinforcement learning) algorithm developed have been tested in simulating Alife environments with various neural network algorithms. Adopting an adaptively selected best sequence of feedback action period Δα which we have found to be a decisive parameter in improving the network efficiency, the ASL-guided FuzGa had an improved performance as compared with ASL-guided CasCor and RL-guided FuzGa. We confirm that the present solution successfully evaluates the effect of interactions at a larger F A(food availability), reducing to an isolated solution at a lower value of F A.
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References
F. Dellaert and R. Beer, “Toward an Evolvable Model of Development for Autonomous Agent Synthesis”, Artificial Life IV, The MIT Press, pp.246–257, 1996.
B. Fullmer and R. Miikkulainen, “Using Marker-Based Genetic Encoding of Neural Networks to Evolve Finite-State Behaviour”, Proc. 1st European Conference on Artificial Life, Paris, 1991.
L. Kaelbling, M. Littman and A. Moore, “Reinforcement learning: A survey”, Journal of Artificial Intelligence Research, Vol.4, pp.237–285, 1996.
H. Lund, “Specialization under social conditions in competitive environments”, Proc. 3rd European Conference on Artificial Life, Spain, 1995.
S. Nolfi and D. Parisi, “Auto-teaching: networks that develop their own teaching input”, Proc. 2nd European Conference on Artificial Life, Brussels, 1993.
J. Yan, N. Tokuda and J. Miyamichi, “A New Constructive Compound Neural Networks Using Fuzzy Logic and Genetic Algorithm: 1 Application to Artificial Life”, IEICE Transactions on Information and Systems, Vol. E81-D, No.12, pp. 1507–1516, Dec. 1998.
S. Fahlman and C. Libiere, “The Cascade-Correlation Learning Architecture”, D. Touretzky, ed., Advances in Neural Information Processing Systems II, Morgan Kaufmann, San Mateo, Calif., pp. 524–532, 1990.
V. Vysniauskas, F. Groen and B. Krose, “The optimal number of learning samples and hidden units in function approximation with a feedforward network”, Technical Report CS-93-15, University of Amsterdam, 1993.
C. Langton, ed., Artificial Life II, Addison-Wesley, MA, USA, 1992.
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© 2000 Springer-Verlag Berlin Heidelberg
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Yan, J., Tokuda, N., Miyamichi, J. (2000). Simulating Competing Alife Organisms by Constructive Compound Neural Networks. 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_22
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DOI: https://doi.org/10.1007/3-540-45486-1_22
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