This paper addresses the agent-based bidding mechanism under trading actions from supplier sites to demand sites. Bidding includes unit price, amount, and storage cost. This trading environment assumes to be completely competitive, which means an agent cannot detect the competitive agent information. To increase the success rate of bidding, the agent must learn its bidding strategy from the past trading log. Our agent estimates the appropriate bidding price from the past "success bids" and "failure bids" by using statistical analysis. Experimental results shows an agent with such learning functionality increases its rate of "success bids" by 45.6%, compared to the agent without such functionality.
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