Technical Note: \cal Q-Learning
Machine Learning
Market-based control: a paradigm for distributed resource allocation
Market-based control: a paradigm for distributed resource allocation
Computationally Manageable Combinational Auctions
Management Science
Truth revelation in approximately efficient combinatorial auctions
Proceedings of the 1st ACM conference on Electronic commerce
Computationally feasible VCG mechanisms
Proceedings of the 2nd ACM conference on Electronic commerce
An efficient approximate allocation algorithm for combinatorial auctions
Proceedings of the 3rd ACM conference on Electronic Commerce
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An approximate truthful mechanism for combinatorial auctions with single parameter agents
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Taming the Computational Complexity of Combinatorial Auctions: Optimal and Approximate Approaches
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Solving Combinatorial Auctions Using Stochastic Local Search
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Iterative Combinatorial Auctions: Theory and Practice
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Cooperative negotiation for soft real-time distributed resource allocation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Dynamic Programming
Combinatorial Auctions: A Survey
INFORMS Journal on Computing
Combinatorial Auctions
False-name-proof combinatorial auction protocol: Groves Mechanism with SubModular Approximation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
An Approximate Algorithm for Resource Allocation Using Combinatorial Auctions
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
An Adaptive Bidding Strategy in Multi-round Combinatorial Auctions for Resource Allocation
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Stepwise randomized combinatorial auctions achieve revenue monotonicity
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Truthful risk-managed combinatorial auctions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Agents' bidding strategies in a combinatorial auction controlled grid environment
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
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Combinatorial auctions, where bidders are allowed to put bids on bundle of items, are the subject of increasing research in recent years. Combinatorial auctions can lead to better social efficiencies than tractional auctions in the resource allocation problem when bidders have complementarities and substitutabilities among items. Although many works have been conducted on combinatorial auctions, most of them focus on the winner determination problem and the auction design. A large unexplored area of research in combinatorial auctions is the bidding strategies. In this paper, we propose a Q-learning based adaptive bidding strategy for combinatorial auctions in static markets. The bidder employing this strategy can transit among different states, gradually converge to the optimal one, and obtain a high utility in the long-term run. Experiment results show that the Q-learning based adaptive strategy performs fairly well when compared to the optimal strategy and outperforms the random strategy and our previous adaptive strategy in different market environments, even without any prior knowledge.