Electronic markets and electronic hierarchies
Communications of the ACM
Proceedings of the 1st ACM conference on Electronic commerce
Pricing in Agent Economies Using Multi-Agent Q-Learning
Autonomous Agents and Multi-Agent Systems
Learning Curve: A Simulation-Based Approach to Dynamic Pricing
Electronic Commerce Research
Optimal Policies for a Multi-Echelon Inventory Problem
Management Science
Flood decision support system on agent grid: method and implementation
Enterprise Information Systems
Electronic marketplace definition and classification: literature review and clarifications
Enterprise Information Systems
A multi-agent-based model for a negotiation support system in electronic commerce
Enterprise Information Systems
Information Systems Frontiers
Distributed data mining for e-business
Information Technology and Management
Effects of promotion cost sharing policy with the sales learning curve on supply chain coordination
Computers and Operations Research
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This paper examines effectiveness of Q-learning as a tool for specifying agent attributes and behaviours in agent-based supply network models. Agent-based modelling (ABM) has been increasingly employed to study supply chain and supply network problems. A challenging task in building agent-based supply network models is to properly specify agent attributes and behaviours. Machine learning techniques, such as Q-learning, can be a useful tool for this purpose. Q-learning is a reinforcement learning technique that has been shown to be an effective adaptation and searching mechanism in distributed settings. In this study, Q-learning is employed by supply network agents to search for 'optimal' values for a parameter in their operating policies simultaneously and independently. Methods are designed to identify the 'optimal' parameter values against which effectiveness of the learning is evaluated. Robustness of the learning's effectiveness is also examined through consideration of different model settings and scenarios. Results show that Q-learning is very effective in finding the 'optimal' parameter values in all model settings and scenarios considered.