Expert systems handbook
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue on clustering and learning
Strategic negotiation in multiagent environments
Strategic negotiation in multiagent environments
The Future of Emarkets: Multi-Dimensional Market Mechanisms
The Future of Emarkets: Multi-Dimensional Market Mechanisms
Decision procedures for multiple auctions
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Designing the Market Game for a Trading Agent Competition
IEEE Internet Computing
Autonomous Bidding Agents in the Trading Agent Competition
IEEE Internet Computing
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On Fuzzy e-Negotiation Agents: Autonomous Negotiation with Incomplete and Imprecise Information
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Developing a bidding agent for multiple heterogeneous auctions
ACM Transactions on Internet Technology (TOIT)
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
A heuristic bidding strategy for buying multiple goods in multiple english auctions
ACM Transactions on Internet Technology (TOIT)
ATTac-2000: an adaptive autonomous bidding agent
Journal of Artificial Intelligence Research
Incremental learning optimization on knowledge discovery in dynamic business intelligent systems
Journal of Global Optimization
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Online auctions are one of the most effective ways of negotiation of salable goods over the internet. Software agents are increasingly being used to represent humans in online auctions. These agents can systematically monitor a wide variety of auctions and can make rapid decisions about what bids to place in what auctions. To be successful in open multi-agent environments, agents must be capable of adapting different strategies and tactics to their prevailing circumstances. This paper presents a software test-bed for studying autonomous bidding strategies in simulated auctions for procuring goods. It shows that agents' bidding strategy explore the attitudes and behaviors that help agents to manage dynamic assessment of prices of goods given the different criteria and scenario conditions. Our agent also uses fuzzy techniques for the decision making: to make decisions about the outcome of auctions, and to alter the agent's bidding strategy in response to the different criteria and market conditions.