By Hiroyuki Iizuka, Ezequiel A. Di Paolo (auth.), Minoru Asada, John C. T. Hallam, Jean-Arcady Meyer, Jun Tani (eds.)
This booklet constitutes the refereed court cases of the tenth overseas convention on Simulation of Adaptive habit, SAB 2008, held in Osaka, Japan in July 2008.
The 30 revised complete papers and 21 revised poster papers awarded have been conscientiously reviewed and chosen from a hundred and ten submissions. The papers are equipped in topical sections at the animat method of adaptive behaviour, evolution, navigation and inner international types, belief and regulate, studying and model, cognition, emotion and behavior, collective and social behaviours, adaptive behaviour in language and communique, and utilized adaptive behaviour.
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Proc. 6th Int. Conf. Development and Learning, pp. E1–6. Imperial College, London (2006) 5. : Valency for adaptive homeostatic agents: Relating evolution and learning. , Timmis, J. ) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 936–945. Springer, Berlin (2005) 6. : Evolving a sense of valency. MSc thesis, School of Informatics, University of Edinburgh (2007) 7. : Modeling motivations and emotions as a basis for intelligent behavior. In: Proc. Int. Conf. Autonomous Agents, pp. 148–155 (1997) 8. : Modeling emotion-based decision making.
Generalization and scaling in reinforcement learning. S. ) Adv. Neural Information Processing Systems, vol. 2. Morgan Kaufmann, San Mateo (1990) 14. : Learning to predict by the method of temporal differences. Machine Learning 3, 9–44 (1988) 15. : Landscapes, learning costs and genetic assimilation. Evolutionary Computation 4(3), 213–234 (1996b) 16. : Learning and evolution: a quantitative genetics approach. J. Theor. Biol. 175, 89–101 (1995) 17. : A model of how the basal ganglia generates and uses neural signals that predict reinforcement.
We have not ruled out transient effects for the situation described for NC#9, however. The pattern of sensor activation and of neural noise may induce bifurcations that are asymptotically non-functional (would not produce phototaxis in the long term) but their change keeps the neural and agent state in a functional transient when A=0 during test after evolution. 4 Conclusion Experiments with neural noise have been presented here from an evolutionary and sensorimotor perspective. The simulation model in itself is minimal but results suggest that, at least in the experimental situations, evolution relies on mechanisms that maintain functional dynamics in transients, as shown for NC#9.