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Speeding up single-query sampling-based algorithms using case-based reasoning
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DOI：10.1016/j.eswa.2018.08.035.png)
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Abstract
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We present an extension to the single-query sampling-based algorithm for improving its response time using Case-Based Reasoning (CBR) technique. Unlike traditional experience-based planners, CBR depends on a single thread execution which reduces the required computation power. Additionally, it is always biased towards exploration rather than exploitation to overcome experience-based algorithms drawbacks. Results indicate that CBR extension has significantly improved sampling-based response time for similar served queries. (C) 2018 Elsevier Ltd. All rights reserved.
Keywords:
Sampling-based algorithms
Experience-based algorithms
Case-Based reasoning
Artificial intelligence
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