Accession Number : ADA294080

Title :   An Evolutionary Approach to Learning in Robots.

Descriptive Note : Progress rept.,

Corporate Author : NAVAL RESEARCH LAB WASHINGTON DC

Personal Author(s) : Grefenstette, John ; Schultz, Alan

PDF Url : ADA294080

Report Date : 1994

Pagination or Media Count : 8

Abstract : Evolutionary learning methods have been found to be useful in several areas in the development of intelligent robots. In the approach described here, evolutionary algorithms are used to explore alternative robot behaviors within a simulation model as a way of reducing the overall knowledge engineering effort. This paper presents some initial results of applying the SAMUEL genetic learning system to a collision avoidance and navigation task for mobile robots. (AN)

Descriptors :   *ROBOTS, *LEARNING MACHINES, ALGORITHMS, COMPUTERIZED SIMULATION, EXPERIMENTAL DATA, OPTIMIZATION, INTELLIGENCE, DECISION MAKING, REASONING, RULE BASED SYSTEMS, NAVIGATION, MOBILE, HEURISTIC METHODS, KNOWLEDGE BASED SYSTEMS, SYSTEMS ANALYSIS, CONTROL THEORY, COLLISION AVOIDANCE, HIGH LEVEL LANGUAGES, GAME THEORY.

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE