Abstract
We introduce information-theoretic tools that can be used in an autonomous agent for constructing an internal predictive model based on event anticipation. This model relies on two different kinds of predictive relationships: time-delay relationships, where two events are related by a nearly constant time-delay between their occurrences; and contingency relationships, where proximity in time is the main property. We propose an anticipation architecture based on these tools that allows the construction of a relevant internal model of the environment through experience. Its design takes into account the problem of handling different time scales. We illustrate the effectiveness of the tools proposed with preliminary results about their ability to identify relevant relationships in different conditions. We describe how these principles can be embedded in a more complex architecture that allows action-decision making according to reward expectation, and handling of more complex relationships. We conclude by discussing issues that were not addressed yet and some axis for future investigations.
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References
Butz, M.V., Sigaud, O., Gerard, P.: Internal models and anticipations in adaptive learning systems. In: Butz, M.V., Sigaud, O., Gérard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. LNCS, vol. 2684, pp. 86–109. Springer, Heidelberg (2003)
Cover, T., Thomas, J.: Elements of Information Theory. John Wiley and Sons, New York (1991)
Gallistel, C.R.: Frequency, Contingency and the Information Processing Theory of Conditioning. In: Sedlmeier, P., Betsch, T. (eds.) Frequency Processing and Cognition, pp. 153–171. Oxford University Press, Oxford, UK (2002)
Gallistel, C.R.: Conditioning from an Information Processing Perspective. Behavioural Processes 61(3), 1234 1–13 (2003)
Klyubin, A.S., Polani, D., Nehaniv, C.L.: Tracking Information Flow through the Environment: Simple Cases of Stigmergy. In: Artificial Life IX. Proceedings of the 9th International Conference on the Simulation and Synthesis of Living Systems, pp. 563–568. The MIT Press, Cambridge (2004)
Kording, K.P., Wolpert, D.M.: Bayesian integration in sensorimotor learning. Nature 427, 244–247 (2004)
Tolman, E.C.: Principles of purposive behavior. In: Koch, S. (ed.) Psychology: A Study of Science, pp. 92–157. McGraw-Hill, New York (1959)
Waddel, J., Dzakpasu, R., Booth, V., Riley, B.T., Reasor, J.D., Poe, G.R., Zochowski, M.: Causal Entropies- A measure for determining changes in the temporal organization of neural systems. Journal of Neuroscience Methods (In Press, 2007)
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Capdepuy, P., Polani, D., Nehaniv, C.L. (2007). Construction of an Internal Predictive Model by Event Anticipation. In: Butz, M.V., Sigaud, O., Pezzulo, G., Baldassarre, G. (eds) Anticipatory Behavior in Adaptive Learning Systems. ABiALS 2006. Lecture Notes in Computer Science(), vol 4520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74262-3_12
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DOI: https://doi.org/10.1007/978-3-540-74262-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74261-6
Online ISBN: 978-3-540-74262-3
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