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Solving the motion planning problem using learning experience through case-based reasoning and machine learning algorithms

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AIN SHAMS ENGINEERING JOURNAL 2020年第1期11卷 133-142页

作者： Abdelwahed, Mustafa F. Mohamed, Amr E. Saleh, Mohamed Aly Helwan Univ Fac Engn Dept Elect Commun & Comp 1 Sherif St Cairo 11792 Egypt

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://***/licenses/by-nc-nd/4.0/).

关键词： Sampling-based algorithms experience-based algorithms Case-based reasoning Artificial intelligence Motion planning

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Improving Solution Quality for experience-based Framework Through Clustering algorithms

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IEEE ACCESS 2019年 7卷 106720-106725页

作者： Abdelwahed, Mustafa F. Mohamed, Amr E. Saleh, Mohamed Aly Helwan Univ Fac Engn Dept Elect Commun & Comp Cairo 11792 Egypt

This paper presents an extension for the current developed experience-based frameworks. The current experience-based scheme depends on executing two parallel threads;one tries to solve the problem using traditional approaches, while the other thread uses experience from past solutions for solving it. Once one of these threads solves the problem, its solution is promoted to be the problem's solution, yet disregarding the solution quality. However, our extension to experience-based frameworks uses clustering to decides which thread will solve the problem while maintaining solution quality. We have used experience-based motion planners for benchmarking our approach, where the presented results demonstrate that this approach works with different experience representations while maintaining better path quality, experience utilization, and reduced computational cost.

关键词： Artificial intelligence case-based reasoning experience-based algorithms motion planning sampling-based algorithms

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Speeding up single-query sampling-based algorithms using case-based reasoning

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 114卷 524-531页

作者： Abdelwahed, Mustafa F. Saleh, Mohamed Mohamed, Amr E. Helwan Univ Fac Engn Dept Elect Commun & Comp 1 Sherif St Cairo 11792 Egypt

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.

关键词： Sampling-based algorithms experience-based algorithms Case-based reasoning Artificial intelligence

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