C4.5: programs for machine learning
C4.5: programs for machine learning
The simulated trading heuristic for solving vehicle routing problems
Discrete Applied Mathematics - Special volume: first international colloquium on graphs and optimization (GOI), 1992
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Fundamenta Informaticae - Special issue on theory and applications of soft computing (TASC04)
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Learning to Evaluate Conditional Partial Plans
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Traffic Prediction for Agent Route Planning
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Combining Rule Induction and Reinforcement Learning: An Agent-based Vehicle Routing
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
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In this paper we propose an agent architecture with learning capabilities and its application to a transportation problem. The agent consists of the several modules (control, execution, communication, task evaluation, planning and social) and knowledge bases to store information and learned knowledge. The proposed solution is tested on the PDPTW. Agents using supervised and reinforcement learning algorithms generate knowledge to evaluate arriving requests. Experimental results show that learning increases agent performance.