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Continuous case-based reasoning
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DOI：10.1016/S0004-3702(96)00037-9.png)
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摘要
En 中文
Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as on-line sensorimotor interaction with the environment, and continuous adaptation and learning during the performance task. This article introduces a new method for continuous case-based reasoning, and discusses its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (self-improving navigation system). The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. The article concludes with a general discussion of case-based reasoning issues addressed by this research.
Keyword:
case-based reasoning
machine learning
reinforcement learning
robot navigation
reactive control
motor schema-based navigation
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