Multi-object auctions: sequential vs. simultaneous sales
Management Science
Market-based control: a paradigm for distributed resource allocation
Market-based control: a paradigm for distributed resource allocation
Online learning about other agents in a dynamic multiagent system
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Computationally Manageable Combinational Auctions
Management Science
An Algorithm for Optimal Winner Determination in Combinatorial Auctions
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Deliberation Levels in Theoretic-Decision Approaches for Task Allocation in Resource-Bounded Agents
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
A Dynamic Programming Model for Algorithm Design in Simultaneous Auctions
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
An Autonomous Bidding Agent for Simultaneous Auctions
CIA '01 Proceedings of the 5th International Workshop on Cooperative Information Agents V
A Comparison among Bidding Algorithms for Multiple Auctions
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Performance Analysis about Parallel Greedy Approximation on Combinatorial Auctions
PRIMA '08 Proceedings of the 11th Pacific Rim International Conference on Multi-Agents: Intelligent Agents and Multi-Agent Systems
Bid evaluation in combinatorial auctions: optimization and learning
Software—Practice & Experience
On the efficiency of sequential auctions for spectrum sharing
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
Bidding efficiently in repeated auctions with entry and observation costs
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
Continuous value function approximation for sequential bidding policies
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Sequential auctions and externalities
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
A markov model for inventory level optimization in supply-chain management
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Repeated auctions with complementarities
AMEC'05 Proceedings of the 2005 international conference on Agent-Mediated Electronic Commerce: designing Trading Agents and Mechanisms
An options-based method to solve the composability problem in sequential auctions
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Using Priced Options to Solve the Exposure Problem in Sequential Auctions
ACM Transactions on Internet Technology (TOIT)
Power Allocation for Two-Way Relay System Based on Sequential Second Price Auction
Wireless Personal Communications: An International Journal
Efficient bidding strategies for Cliff-Edge problems
Autonomous Agents and Multi-Agent Systems
AdSCHE: DESIGN OF AN AUCTION-BASED FRAMEWORK FOR DECENTRALIZED SCHEDULING
Journal of Integrated Design & Process Science
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Market-based mechanisms such as auctions are being studied as an appropriate means for resource allocation in distributed and inultiagenl decision problems. When agents value resources in combination rather than in isolation, one generally relies on combinatorial auctions where agents bid tor resource bundles. or simultaneous auctions for all resources. We develop a different model, where agents bid for required reources sequentially. This model has the advantage that it can be applied in settings where combinatorial and simultaneous models are infeasible (e.g.. when resources are made available at different points in time by different parties), as well as certain benefits in settings where combinatorial models are applicable. We develop a dynamic programming model tor agents to compute bidding policies based on estimated distributions over prices. We also describe how these distributions are updated to provide a learning model for bidding behavior.