Abstract
This paper introduces a novel study on the sense of valency as a vital process for achieving adaptation in agents through evolution and developmental learning. Unlike previous studies, we hypothesise that behaviour-related information must be underspecified in the genes and that additional mechanisms such as valency modulate final behavioural responses. These processes endow the agent with the ability to adapt to dynamic environments. We have tested this hypothesis with an ad hoc designed model, also introduced in this paper. Experiments have been performed in static and dynamic environments to illustrate these effects. The results demonstrate the necessity of valency and of both learning and evolution as complementary processes for adaptation to the environment.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ackley, D., Littman, M.: Interactions between learning and evolution. In: Langton, C., Taylor, C., Farmer, D., Rasmussen, S. (eds.) Proceedings of the Second Conference on Artificial Life. Addison-Wesley, California (1991)
Ashby, W.R.: Design for a Brain: The Origin of Adaptive Behaviour. Chapman & Hall, London (1965)
Baldwin, J.M.: A new factor in evolution. The American Naturalist 30, 441–451, 536–553 (1896)
Batali, J., Grundy, W.N.: Modelling the evolution of motivation. Evolutionary Computation 4(3), 235–270 (1996)
Cañamero, D.: A hormonal model of emotions for behavior control. In: VUB AIMemo 97U06, Free University of Brussels, Belgium. Presented as poster at the Fourth European Conference on Artificial Life. ECAL 1997, Brighton, UK, July 28-3 (1997)
Cos-Aguilera, I., Cañamero, D., Hayes, G.: Motivation-driven learning of object affordances: First experiments using a simulated khepera robot. In: Detje, F., Dörner, D., Schaub, H. (eds.) The Logic of Cognitive Systems. Proceedings of the Fifth International Conference on Cognitive Modelling, ICCM, April 2003, pp. 57–62. Universitäts-Verlag, Bamberg (2003)
Damoulas, T.: Evolving a sense of Valency. Master’s thesis, School of Informatics, Edinburgh University (2004)
Harvey, I.: Is there another new factor in evolution? Evolutionary Computa- tion 4(3), 313–329 (1996)
Hinton, G.E., Nowlan, S.J.: How learning can guide evolution. Complex Systems 1, 495–502 (1987)
Hull, C.: Principles of Behaviour: an Introduction to Behaviour Theory. D. Appleton-Century Company, Inc. (1943)
Mclean, C.B.: Design, evaluation and comparison of evolution and reinforcement learning models. Master’s thesis, Department of Computer Science, Rhodes University (2001)
Mitchell, M.: An Introduction To Genetic Algorithms. A Bradford Book, The MIT Press, Cambridge (1998)
Nolfi, S.: Learning and evolution in neural networks. Adaptive Behavior 1(3), 5–28 (1994)
Sasaki, T., Tokoro, M.: Adaptation toward changing environments: Why darwinian in nature? In: Husband, P., Harvey, I. (eds.) Proceedings of the Fourth European Conference on Artificial Life, pp. 378–387. MIT Press, Cambridge (1997)
Spier, E., McFarland, D.: Possibly optimal decision-making under self-sufficiency and autonomy. Journal of Theoretical Biology (189), 317–331 (1997)
Sutton, R.S.: Learning to predict by the method of temporal difference. Machine Learning 3(1), 9–44 (1988)
Sutton, R.S., Barto, A.G.: Reinforcement Learning. MIT Press, Cambridge (1998)
Suzuki, R., Arita, T.: The baldwin effect revisited: Three steps characterized by the quantitative evolution of phenotypic plasticity. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 395–404. Springer, Heidelberg (2003)
Toates, F., Jensen, P.: Ethological and psychological models of motivation - towards a synthesis. In: Meyer, J.-A., Wilson, S.W. (eds.) Proceedings of the First International Converence on Simulation of Adaptive Behaviour, A Bradford Book, pp. 194–205. The MIT Press, Cambridge (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Damoulas, T., Cos-Aguilera, I., Hayes, G.M., Taylor, T. (2005). Valency for Adaptive Homeostatic Agents: Relating Evolution and Learning. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science(), vol 3630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553090_94
Download citation
DOI: https://doi.org/10.1007/11553090_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28848-0
Online ISBN: 978-3-540-31816-3
eBook Packages: Computer ScienceComputer Science (R0)