opponent modeling was precluded by the system dynamics  Nonetheless  ATTac-2000 s adaptivity is one of the keys to its success particularly in avoiding skyrocketing hotels The nd and rd place agents both used a different strategy to prepare for the possibility of skyrocketing hotels Rather than avoiding popular hotels entirely by tracking closing prices across game instances they both discouraged their agents from bidding for too many of any particular hotel room  thus spreading their demand across the rooms  Greenwald Stone While such a strategy is safer in the limit  ie   it continues to work even if everyone uses it it has a greater potential to cost the agent in the event that hotel prices do not skyrocket  since the agent will still distribute its demand to the less desirable rooms On the other hand ATTac-200 would notice that the prices are not skyrocketing and thus bid for the optimal travel packages given current prices

## 6. Conclusion And Future Work

was the first autonomous bidding agent competition While it was a very success TA C
ful event  some minor improvements would increase its interest from a multiagent learning perspective

- Currently there is no incentive to buy airline tickets until the end of the game  Were the price of flights to tend to increase  or were supply limited  agents would have to balance the advantage of keeping their options open against the savings of committing to travel packages earlier®
- The inform ation structure of the TAC setup was such that it was impossible to observe the bidding patterns of individual agents during games  Nonetheless the strategic behavior of individual agents often profoundly affected market dynamics    particularly in the hotel auctions It seems that it would be beneficial to be able to directly observe the behavior of each individual agent Were there to be information available regarding the bidding behavior of the agents during the game  such that other agents could infer clients preferences and therefore market supply demand and prices  TAC agents would potentially be able to learn to predict market behavior as a game proceeds
With or without these modifications we hope to be able to participate in future TACs with the goal of adding additional adaptive elements to ATTac-2000 Another direction of future research is to apply the lessons learned from TAC to real simultaneous interacting auctions  It is straightforward to write bidding agents to partic ipate in on line auctions for a single good if the value to the client is fixed ahead of time the agent can bid slightly over the ask price until the auction closes or the price exceeds the value   However  when the values of multiple goods interact  such as is the case in  TAC agent deployment is not nearly so straightforward One such real application is the Federal Communications Commission s auctioning off of radio spectrum  Weber Cramton Especially for companies that are trying to achieve national coverage the values of the different licenses interact in complex ways Perhaps autonomous bidding agents will be able to affect bidding strategies in such future 6. This change has been adopted in the specification of TAC-01.