Title: A learning system based on genetic adaptive algorithms
Abstract:A learning system is a system that improves its performance with respect to a given task domain over time through its interactions with the task environment. The mechanisms by which such a system mani...A learning system is a system that improves its performance with respect to a given task domain over time through its interactions with the task environment. The mechanisms by which such a system manipulates its knowledge about the task environment in response to these interactions constitute the system's of learning. In constructing an artificial learning system, the particular methods employed determine, to a large extent, the ultimate generality of the system. A learning system capable of functioning in a variety of task domains necessarily requires the presence of domain independent methods of learning.
This thesis is concerned with investigating the feasibility of constructing a general purpose learning system around a particular class of domain independent methods called genetic algorithms. To this end, a specific learning system organization, LS-1, is proposed. In further specifying the design, a production system language amenable to manipulation by a genetic algorithm is defined as the system's representation of knowledge, organized as a domain independent framework into which task specific primitives can be injected. The classical genetic algorithms are then modified to suit the specific characteristics of the defined knowledge structure representation. A formal analysis of the search conducted by such a revised genetic algorithm through the space of possible production system programs is performed, demonstrating that it possesses properties analogous to those exhibited by classical genetic algorithms and establishing a sound theoretical foundation for LS-1. Finally, a critic to judge the relative worth of a production system program as a potential solution to the task at hand is specified, incorporating both domain independent and task specific sources of judgmental information.
As a demonstration of the feasibility of the design an LS-1 implementation is tested in two distinct and unrelated task domains, each the domain of a related effort in learning system construction. Specifically, the system is faced with (1) a simple maze walk problem and (2) the problem of making the bet decision in draw poker. Initialized in each test with randomly generated production system programs, the LS-1 implementation is shown to rapidly converge on high performance knowledge structures in both task domains, providing empirical evidence of the effectiveness of a genetic algorithm as a general purpose learning mechanism.Read More
Publication Year: 1980
Publication Date: 1980-01-01
Language: en
Type: article
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Cited By Count: 535
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