Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Online learning about other agents in a dynamic multiagent system
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Commodity trading using an agent-based iterated double auction
Proceedings of the third annual conference on Autonomous Agents
An adaptive agent bidding strategy based on stochastic modeling
Proceedings of the third annual conference on Autonomous Agents
Flexible double auctions for electionic commerce: theory and implementation
Decision Support Systems - Special issue on economics of electronic commerce
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Designing Bidding Strategies for Trading Agents in Electronic Auctions
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions
Autonomous Agents and Multi-Agent Systems
Utility-based double auction mechanism using genetic algorithms
Expert Systems with Applications: An International Journal
Evolutionary mechanism design: a review
Autonomous Agents and Multi-Agent Systems
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We are concerned with the issues on designing adaptive trading agents to learn bidding strategies in electronic market places. The synchronous double auction is used as a simulation testbed. We implemented agents with neural-network-based reinforcement learning called Q-learning agents (QLA) to learn bidding strategies in the double auctions. In order to compare the performances of QLAs in the electronic market places, we also implemented many kinds of non-adaptive trading agents such as simple random bidding agents (SRBA), gradient-based greedy agent (GBGA), and truth telling agent (TTA). Instead of learning to model other trading agents that is computational intractable, we designed learning agents to model the market environment as a whole instead. Our experimental results showed that in terms of global market efficiency, QLAs could outperform TTAs and GBGAs but could not outperform SRBAs in the market of homogeneous type of agents. In terms of individual performance, QLAs could outperform all three non-adaptive trading agents when the opponents they are dealing with in the market place are a purely homogeneous type of non-adaptive trading agents. However, QLAs could only outperform TTAs and GBGAs and could not outperform SRBAs in the market of heterogeneous types of agents.