Titlebook: Connectionistic Problem Solving; Computational Aspect Steven E. Hampson Book 1990 Birkh?user Boston 1990 Extension.artificial intelligence.

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Turing Machines with Sublogarithmic Spacectness of the preceding action (sometimes referred to as “l(fā)earning with a critic” (Widrow et al., 1973; Barto et al., 1981)). A specialized evaluation system is developed in this chapter and used to train the operators.

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Lecture Notes in Computer Sciencereme, since the S-S model alone is sufficient for adaptive behavior, it has been suggested that the S-R model is dispensable (Bolles, 1972; Bindra, 1976). This chapter completes the development of the S-S model by considering some of the biological evidence for and against such mechanisms.

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Languages acceptable with logarithmic space,ing that they had not learned a particular motor pattern, but the more general goal of traversing the maze to reach the goal box. The evidence for “place learning” as opposed to “response learning” which was discussed in the previous chapter is also evidence for some form of goal setting.

pacifist

Node Structure and Training, extent. Both the node representation and the training algorithm are sufficiently simple to permit a significant amount of analysis. Variations on the basic model are considered and possible extensions to deal with continuous input values are explored.

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Stimulus-Stimulus Discussion,reme, since the S-S model alone is sufficient for adaptive behavior, it has been suggested that the S-R model is dispensable (Bolles, 1972; Bindra, 1976). This chapter completes the development of the S-S model by considering some of the biological evidence for and against such mechanisms.

Limited

Stimulus-Goal Associations,ing that they had not learned a particular motor pattern, but the more general goal of traversing the maze to reach the goal box. The evidence for “place learning” as opposed to “response learning” which was discussed in the previous chapter is also evidence for some form of goal setting.

prolate

ucture, traces a path from relatively low-level neural/connectionistic structures and processes to relatively high-level animal/artificial intelligence behaviors. Incremental extension of this initial path permits increasingly sophisticated representation and processing strategies, and consequently

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https://doi.org/10.1007/978-1-4684-6770-3Extension; artificial intelligence; behavior; intelligence; learning; memory; modeling; problem solving; pro