Spawn: A Distributed Computational Economy
IEEE Transactions on Software Engineering
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
Adaptive agents in a persistent shout double auction
Proceedings of the first international conference on Information and computation economies
An adaptive agent bidding strategy based on stochastic modeling
Proceedings of the third annual conference on Autonomous Agents
Conjectural Equilibrium in Multiagent Learning
Machine Learning
Economic dynamics of agents in multiple auctions
Proceedings of the fifth international conference on Autonomous agents
High-performance bidding agents for the continuous double auction
Proceedings of the 3rd ACM conference on Electronic Commerce
Strategic sequential bidding in auctions using dynamic programming
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Autonomous Bidding Agents in the Trading Agent Competition
IEEE Internet Computing
Market Performance of Adaptive Trading Agents in Synchronous Double Auctions
PRIMA 2001 Proceedings of the 4th Pacific Rim International Workshop on Multi-Agents, Intelligent Agents: Specification, Modeling, and Applications
Antisocial Agents and Vickrey Auctions
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Developing a bidding agent for multiple heterogeneous auctions
ACM Transactions on Internet Technology (TOIT)
SouthamptonTAC: An adaptive autonomous trading agent
ACM Transactions on Internet Technology (TOIT)
On Agent-Mediated Electronic Commerce
IEEE Transactions on Knowledge and Data Engineering
A Fuzzy-Logic Based Bidding Strategy for Autonomous Agents in Continuous Double Auctions
IEEE Transactions on Knowledge and Data Engineering
Speculation Agents for Dynamic Multi-Period Continuous Double Auctions in B2B Exchanges
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 7 - Volume 7
On cheating in sealed-bid auctions
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Walverine: a Walrasian trading agent
Decision Support Systems - Special issue: Decision theory and game theory in agent design
ATTac-2000: an adaptive autonomous bidding agent
Journal of Artificial Intelligence Research
Designing a successful trading agent: A fuzzy set approach
IEEE Transactions on Fuzzy Systems
Evolutionary optimization of ZIP60: a controlled explosion in hyperspace
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An adaptive attitude bidding strategy for agents in continuous double auctions
Electronic Commerce Research and Applications
Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
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
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In developing open, heterogeneous and distributed multi-agent systems researchers often face a problem of facilitating negotiation and bargaining amongst agents. It is increasingly common to use auction mechanisms for negotiation in multi-agent systems. The choice of auction mechanism and the bidding strategy of an agent are of central importance to the success of the agent model. Our aim is to determine the best agent learning algorithm for bidding in a variety of single seller auction structures in both static environments where a known optimal strategy exists and in complex environments where the optimal strategy may be constantly changing. In this paper we present a model of single seller auctions and describe three adaptive agent algorithms to learn strategies through repeated competition. We experiment in a range of auction environments of increasing complexity to determine how well each agent performs, in relation to an optimal strategy in cases where one can be deduced, or in relation to each other in other cases. We find that, with a uniform value distribution, a purely reactive agent based on Cliff's ZIP algorithm for continuous double auctions (CDA) performs well, although is outperformed in some cases by a memory based agent based on the Gjerstad Dickhaut agent for CDA.