Abstract
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.