Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Commodity trading using an agent-based iterated double auction
Proceedings of the third annual conference on Autonomous Agents
Conjectural Equilibrium in Multiagent Learning
Machine Learning
High-performance bidding agents for the continuous double auction
Proceedings of the 3rd ACM conference on Electronic Commerce
Emergent Properties of a Market-based Digital Library with Strategic Agents
Autonomous Agents and Multi-Agent Systems
Rational Coordination in Multi-Agent Environments
Autonomous Agents and Multi-Agent Systems
Predicting the Expected Behavior of Agents that Learn About Agents: The CLRI Framework
Autonomous Agents and Multi-Agent Systems
ISADS '01 Proceedings of the Fifth International Symposium on Autonomous Decentralized Systems
SouthamptonTAC: An adaptive autonomous trading agent
ACM Transactions on Internet Technology (TOIT)
A principled study of the design tradeoffs for autonomous trading agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
On artificial agents for negotiation in electronic commerce
On artificial agents for negotiation in electronic commerce
The p-strategy: an adaptive agent bidding strategy based on stochastic modeling for continuous double auctions
A Fuzzy-Logic Based Bidding Strategy for Autonomous Agents in Continuous Double Auctions
IEEE Transactions on Knowledge and Data Engineering
Trading Agents Competing: Performance, Progress, and Market Effectiveness
IEEE Intelligent Systems
Agent-human interactions in the continuous double auction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Evolutionary optimization of ZIP60: a controlled explosion in hyperspace
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Managing parallel inquiries in agents' two-sided search
Artificial Intelligence
Evolutionary Prediction of Online Keywords Bidding
EC-Web '08 Proceedings of the 9th international conference on E-Commerce and Web Technologies
Stronger CDA strategies through empirical game-theoretic analysis and reinforcement learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Evolutionary optimization of ZIP60: a controlled explosion in hyperspace
TADA/AMEC'06 Proceedings of the 2006 AAMAS workshop and TADA/AMEC 2006 conference on Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets
Prediction of keyword auction using Bayesian network
EC-Web'07 Proceedings of the 8th international conference on E-commerce and web technologies
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As computational agents are developed for increasingly complicated e-commerce applications, the complexity of the decisions they face demands advances in artificial intelligence techniques. For example, an agent representing a seller in an auction should try to maximize the seller's profit by reasoning about a variety of possibly uncertain pieces of information, such as the maximum prices various buyers might be willing to pay, the possible prices being offered by competing sellers, the rules by which the auction operates, the dynamic arrival and matching of offers to buy and sell, and so on. A naïve application of multiagent reasoning techniques would require the seller's agent to explicitly model all of the other agents through an extended time horizon, rendering the problem intractable for many realistically-sized problems. We have instead devised a new strategy that an agent can use to determine its bid price based on a more tractable Markov chain model of the auction process. We have experimentally identified the conditions under which our new strategy works well, as well as how well it works in comparison to the optimal performance the agent could have achieved had it known the future. Our results show that our new strategy in general performs well, outperforming other tractable heuristic strategies in a majority of experiments, and is particularly effective in a "seller's market," where many buy offers are available.