Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Communication in reactive multiagent robotic systems
Autonomous Robots
Scheduling Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Meta-Heuristics: Theory and Applications
Meta-Heuristics: Theory and Applications
Making Organizational Learning Operational: Implications from Learning Classifier Systems
Computational & Mathematical Organization Theory
ISADS '99 Proceedings of the The Fourth International Symposium on Autonomous Decentralized Systems
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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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.