Adaptive agents in a persistent shout double auction
Proceedings of the first international conference on Information and computation economies
Economic dynamics of agents in multiple auctions
Proceedings of the fifth international conference on Autonomous agents
Strategic sequential bidding in auctions using dynamic programming
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Decision procedures for multiple auctions
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
A Dynamic Programming Model for Algorithm Design in Simultaneous Auctions
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
Algorithm Design for Agents which Participate in Multiple Simultaneous Auctions
Agent-Mediated Electronic Commerce III, Current Issues in Agent-Based Electronic Commerce Systems (includes revised papers from AMEC 2000 Workshop)
Sequential optimality and coordination in multiagent systems
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Sequential auctions for the allocation of resources with complementarities
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Continuous value function approximation for sequential bidding policies
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Bidding in sealed-bid and English multi-attribute auctions
Decision Support Systems
Bidding agents for online auctions with hidden bids
Machine Learning
Resource trading using cognitive agents: A hybrid perspective and its simulation
Future Generation Computer Systems
Web Intelligence and Agent Systems
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We study simulations of populations of agents participating in sequences of overlapping English auctions, using three different bidding algorithms. We measure various parameters of the agents' success, to determine qualities of the underlying bidding algorithms. In particular, we show that a Dynamic Programming approach, in which beliefs regarding the opposition the agent is likely to face are built up on-the-fly, is robust enough with respect to the inaccuracy of its beliefs to outperform a greedy approach right from the moment they both start playing.