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Proceedings Paper

Time-varying environment-based machine learning technique for autonomous agent shortest-path planning
Author(s): Dalila B. Megherbi; A. Teirelbar; A. J. Boulenouar
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Paper Abstract

Autonomous agent path planning is a main problem in the fields of machine learning and artificial intelligence. Reactive execution is often used in order to provide best decision for the agent's reactions. Although this problem is important in the stationary environment, most interesting environments are time varying. This paper is based on our previous work focusing on combining the potential field model with reinforcement learning to solve the stationary path problem. In this work we deal with the case of dynamic environment. In the dynamic environment, the motion of the obstacles provides for different problems and challenges, which our proposed algorithm in this paper encounters and addresses.

Paper Details

Date Published: 20 September 2001
PDF: 10 pages
Proc. SPIE 4364, Unmanned Ground Vehicle Technology III, (20 September 2001); doi: 10.1117/12.440003
Show Author Affiliations
Dalila B. Megherbi, Univ. of Massachusetts/Lowell (United States)
A. Teirelbar, Univ. of Massachusetts/Lowell (United States)
A. J. Boulenouar, Univ. of Massachusetts/Lowell (United States)


Published in SPIE Proceedings Vol. 4364:
Unmanned Ground Vehicle Technology III
Grant R. Gerhart; Chuck M. Shoemaker, Editor(s)

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