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

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Book 1990aces 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 increasing

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Introduction,al chapters develop the basic components of the system at the node and network level, with the general goal of efficient category learning and representation. The later chapters are more concerned with the problems of assembling sequences of actions in order to achieve a given goal state.

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Improving on Perceptron Training,is considered, and three techniques are developed which can significantly improve performance. These are variable associability, adaptive origin placement, and short-term weight modification. Each addresses a different limiting aspect of perceptron training.

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Turing Machines with Sublogarithmic Spaceal chapters develop the basic components of the system at the node and network level, with the general goal of efficient category learning and representation. The later chapters are more concerned with the problems of assembling sequences of actions in order to achieve a given goal state.

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Turing Machines with Sublogarithmic Space. In this chapter, two S-S models of “planning” by parallel search are developed. The first searches backward from the goal state looking for a sequence of states that connect the goal state with the current state. The second searches forward from the current state looking for a path to the goal state.

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