How to Design Good Rules for Multiple Learning Agents in Scheduling Problems?
This paper explores how to design good rules for multiple learning agents in scheduling problems and investigates what kind of factors are required to find good solutions with small computational costs. Through intensive simulations of crew task scheduling in a space shuttle/station, the following experimental results have been obtained: (1) an integration of (a) a solution improvement factor, (b) an exploitation factor, and (c) an exploration factor contributes to finding good solutions with small computational costs; and (2) the condition part of rules, which includes flags indicating overlapping, constraints, and same situation conditions, supports the contribution of the above three factors.
Keywordsrule design scheduling problem multiple learning agents organizational learning learning classifier system
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