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Solving the motion planning problem using learning experience through case-based reasoning and machine learning algorithms
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DOI：10.1016/j.asej.2019.10.007.png)
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摘要
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This article presents two novel methodologies for solving the motion planning problem through retained experience. Both approaches employ AI's case-based reasoning (CBR) technique. Case-based reasoning is an expert system development methodology which reuses past solutions to solve new problems. The first approach uses CBR to retain K similar cases to solve the motion planning problem by merging those solutions into a set. Afterwards, it picks from this set based on a heuristic function to assemble a final solution. Regarding the second approach, it employs the retained K similar cases differently. It uses those solution to build a graph which can be queried using traditional graph search algorithms. Results prove the success of such approaches concerning solution quality and success rate compared to different experience-based algorithms. Such utilization for CBR systems develops new research directions for building systems that can solve NP problems based on retained experiences exclusively. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword:
Sampling-based algorithms
Experience-based algorithms
Case-based reasoning
Artificial intelligence
Motion planning
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