Accession Number : ADA294062

Title :   Improving Tactical Plans with Genetic Algorithms,

Corporate Author : NAVAL RESEARCH LAB WASHINGTON DC

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

PDF Url : ADA294062

Report Date : 1990

Pagination or Media Count : 7

Abstract : The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical plans from a simple flight simulator where a plane must avoid a missile. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. In the research presented here, the use of available heuristic domain knowledge to initialize the population to produce better plans is investigated. (AN)

Descriptors :   *ALGORITHMS, *RULE BASED SYSTEMS, COMPUTERIZED SIMULATION, OPTIMIZATION, DECISION MAKING, COMPARISON, LEARNING MACHINES, PROBLEM SOLVING, SEQUENTIAL ANALYSIS, DECISION THEORY, SEARCHING, DATA ACQUISITION, HEURISTIC METHODS, KNOWLEDGE BASED SYSTEMS, TACTICAL ANALYSIS, SYSTEMS ANALYSIS, MANEUVERS, FLIGHT SIMULATORS, HIGH LEVEL LANGUAGES.

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE