Abstract
We propose a generalized framework Schema-based learning (SBL) for the design of complete and integrated adaptive autonomous agents incorporating general principles of adaptive organization e.g., bootstrap coherence and coherence maximization principles. A schema is an evolutionarily or experience-based constructed recurrent pattern of interaction or expectation (perceptual, motor, reactive, and predictive schemas) with the environment, and coherence is a measure of the congruence between the result of an interaction with the environment and the expectations the agent has for that interaction. SBL attempts to provide a general and formal framework independent of the particularities of implementation, thus allowing the design and analysis of a wide variety of agents. SBL allows the growth of increasingly complex patterns of interaction between the agent and its environment from an initially restricted stock of schemas while allowing for efficient learning by confining statistical estimation to a narrow credit assignment space.
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References
Arbib, M. A. (1987). Levels of modeling of visually guided behavior. Behav. Brain Sci. 10, 407–465.
Arbib, M. A. (1992). Schema Theory. In: The Encyclopedia of Artificial Intelligence. (Shapiro, S., ed.) 2nd Edn pp. 1427–1443. New York, NY: Wiley Interscience.
Arkin, R. (1989). Motor Schema based mobile robot navigation. Int. J. Robotics. Res. 8(4), 92–112.
Brooks, R. A. (1986). A robust layered control system for a mobile robot. IEEE Trans. Rob. Automation, 2: 14–23.
Carbonell, J. G. & Gil, Y. (1990). Learning by experimentation: The Operator Refinement Method. (In Machine Learning: An Artificial Intelligence Approach. Vol. III. Y. Kodratoff & Ryszard Michalski Eds.). Morgan Kaufmann: San Mateo.
Collett, T. (1982). Do toads plan routes? A study of detour behavior of B. viridis. J. Comp. Physiol. A, 146:261–271.
Corbacho, F. & Arbib, M. A. (1995). Learning to Detour. Adaptive Behavior, 3(4), 419–468.
Corbacho, F. & Arbib, M. A. (1996a). Learning to Detour and Schema-based Learning. Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior.
Corbacho, F. & Arbib, M. A. (1996b). Schema-based Learning. (in preparation).
Drescher, G. L. (1991). Made-Up Minds. Cambridge: MIT Press.
Duhamel, J. R., Colby, C. L., & Goldberg, M. E. (1992). The Updating of the Representation of Visual Space in Parietal Cortex by Intended Eye Movements. Science, 255: 90–92.
Foner, L. N. & Maes. P. (1994) Paying attention to what's important: using focus of attention to improve unsupervised learning. Proceedings of the Third International Conference on Simulation of Adaptive Behavior.
Hummel, R. A., & Zucker, S. W. (1983) On the Foundations of Relaxation Labelling Processes, IEEE Trans. Pattern Analysis and Machine Intelligence, 5:267–287.
Maes, P. & Brooks, R. A. (1990). Learning to coordinate behaviors. AAAI-90, Boston, 796–802.
Piaget, J. (1954). The Construction of Reality in the Child. New York: Ballantine.
Rumelhart, D. E., Hinton, G. E., and Williams. R. J. (1986). Learning internal representations by error propagation. In Parallel distributed processing: Explorations in the microstructure of cognition (D. E. Rumelhart, and J. McClelland, eds.), vol. 1, pp. 318–362. The MIT Press/Bradford Books.
Shen, W-M. (1994). Autonomous Learning from the Environment. New York: W. H. Freeman and Company.
Thrun, S. (1996). Explanation-based Neural Network Learning: A lifelong learning problem. Kluwer Academic Publishers: MA.
von der Malsburg, C. (1986). Am I Thinking Assemblies? In Brain Theory, G. Palm & A. Aertsen (Eds.). Springer-Berlag, Berlin.
von der Malsburg, C. (1994). The Correlation Theory of Brain Function, (Reprint) In Models of Neural Networks II (E. Domany, J. L. van Hemmen, and K. Schulten, eds.), Ch. 2. pp. 95–119. Springer Verlag.
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Corbacho, F.J., Arbib, M.A. (1997). Schema-based learning: Biologically inspired principles of dynamic organization. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032528
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DOI: https://doi.org/10.1007/BFb0032528
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