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Autonomous Bidding Agents in the Trading Agents Competition

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Autonomous Bidding Agents in the Trading Agents Competition Autonomous Bidding Virtual Marketplaces Agents in the Trading Agent Competition Designing agents that can bid in online simultaneous auctions is a complex task.The authors describe task-specific details and strategies of agents in a trading agent competition. Amy Greenwald natural offshoot of the growing a companion article, the tournament orga- Brown University prevalence of online auctions is nizers present the design and operation of Peter Stone A the creation of autonomous bid- the competition.1 AT&T Labs–Research ding agents that monitor and participate This article describes the task-specific in these auctions. It is straightforward to details of, and the general motivations write a bidding agent to participate in an behind, the four top-scoring agents. First, online auction for a single good, particu- we discuss general strategies used by most larly when the value of that good is fixed of the participating agents. We then report ahead of time: the agent can bid slightly on the strategies of the four top-placing over the ask price until the auction closes agents. We conclude with suggestions for or the price exceeds the value. In simulta- improving the design of future trading neous auctions offering complementary agent competitions. and substitutable goods, however, agent deployment is a much more complex General Game Strategies endeavor. A TAC game instance lasts 15 minutes and The first trading agent competition pits eight autonomous bidding agents (TAC), held in Boston, Massachusetts, on 8 against one another. Each TAC agent is a July 2000, challenged participants to simulated travel agent with eight clients, design a trading agent capable of bidding each of whom would like to travel from in online simultaneous auctions for com- TACtown to Boston and home again dur- plimentary and substitutable goods. TAC ing a five-day period. Each client is char- was organized by a group of researchers acterized by a random set of preferences and developers led by Michael Wellman of for arrival and departure dates, hotel the University of Michigan and Peter Wur- rooms, and entertainment tickets. A TAC man of North Carolina State University. In agent’s objective is to maximize the total 52 MARCH • APRIL 2001 http://computer.org/internet/ 1089-7801/01/$10.00 ©2001 IEEE IEEE INTERNET COMPUTING Bidding Agents (A) REPEAT utility (a monetary measure of the value of goods 1. Get market prices from server to clients) minus total expenses. 2. Decide on what goods to bid Agents have two basic activities: bidding and 3. Decide at what prices to bid allocating. An agent bids, or offers payment, for 4. Decide for how many to bid goods to gain utility; an agent allocates purchased 5. Decide at what time to bid resources to clients to maximize total utility, both UNTIL game over during and at the end of the game. To obtain util- (B) Allocate goods to clients ity for a client, a TAC agent constructs a travel package for that client by placing winning bids in Figure 1. High-level overview of a TAC agent’s bidding simultaneous auctions for hotel reservations and decisions.TAC agents run an inner bidding loop, getting flights. An agent can obtain additional utility by price updates from the server and making bidding deci- buying and selling entertainment tickets. After the sions. Note that the order in which these decisions are auctions close, agents have four minutes to report made varies according to the agent’s strategy. their final allocations of goods to clients. A TAC agent’s score is the difference between its clients’ utilities and the agent’s expenditures; a higher Treating all current holdings of flights and score indicates better performance. For full details, entertainment tickets as sunk costs, the marginal see http://tac.eecs.umich.edu. utility of an as-yet-unsecured hotel room reserva- tion is precisely the utility of the package itself. Bidding Strategies (Note that this observation holds only when the Figure 1 lists the basic decisions in the agents’ length of stay is exactly one inner bidding loops. Most decisions—what to buy, night; for longer stays, it how many to buy, and how much to bid—showed requires the further assumption The top-scoring several noteworthy differences. Only the timing of that all other hotel rooms in the bids showed common features across agents. package are secured.) Through- agents were out the preliminary competition, those that not Hotel auctions. In TAC auctions, the supply of few agents bid their marginal only bid aggres- flights is infinite and airline ticket prices are pre- utilities on hotel rooms. Those dictable, but the supply of hotel rooms is finite that did, however, generally sively,but also and hotel prices are unpredictable. Given the risks dominated their competitors. incorporated risk associated with the hotel reservation auctions, Such agents were high bidders, and portfolio together with their importance in securing feasi- bidding approximately $1,000, ble travel packages, hotels were the most hotly always winning the hotels on management contested items during the TAC competition. The which they bid, but paying a into their timing of bidding in hotel auctions is particular- price far below their bid. Most strategy. ly intriguing. agents adopted this high-bidding Hotel room prices have no set maximum value. strategy during the actual com- Instead, TAC hotel auctions are ascending (English), petition. The result: many negative scores, since mth-price, multiunit auctions and are subject to there were often greater than m high bids on a random closing times given sufficient levels of inac- hotel room. In the final competition, the top-scor- tivity. (In a multiunit auction, m units of a good are ing TAC agents were those that not only bid available; in an mth price auction, the bidders with aggressively on hotels, but also incorporated risk the m highest prices win the m units, all at the mth and portfolio management into their strategy to highest price.) Most TAC agents refrain from bid- reduce the likelihood of buying highly demanded, ding for hotels early in the game unless the ask overpriced hotel rooms. price has not changed recently, implying that the auction might close early, or the ask price is very Flight auctions. Unlike hotel prices, prices for low. Ultimately, the most aggressive hotel bidding flights are predictable, with a maximum value of takes place at the “witching hour”—in the final $600. In particular, expected future prices equal moments of the game—although precisely when is current prices. Since airline prices periodically determined individually by each agent. More often increase or decrease by a random amount chosen than not, TAC hotel auctions reduce to mth price from the set {–10, –9, . , 9, 10} with equal prob- sealed-bid auctions, resulting in unpredictable final ability, the expected change in price for each air- hotel prices that were often out of bounds. line auction is 0. (Indeed, it can be shown that if IEEE INTERNET COMPUTING http://computer.org/internet/ MARCH • APRIL 2001 53 Virtual Marketplaces Table 1. The agents’ effectiveness in optimizing final allocations during the complete packages, others make bidding decisions competition. on travel packages alone (that is, flights and hotel rooms) without regard for entertainment packages, Agent Strategy Aggregate Minimum Number essentially breaking the TAC problem down into two ATTac Optimal 100.0% 100.0% 13/13 subproblems and then solving greedily. The greedy RoxyBot Optimal 100.0% 100.0% 13/13 approach, however, is not optimal. For example, if a client does not already have a ticket to an event, Aster Heuristic 99.6% 98.0% 9/13 then it is preferable to extend the client’s stay when- UmbcTac Greedy 99.4% 94.5% 7/13 ever the utility obtained by assigning that client a ticket to the event exceeds the cost of the ticket and Note:Aggregate is the percentage of the optimal utility (ignoring expenditures) achieved an additional night at the hotel plus any travel with the reported allocation, aggregated over the 13 games each of the top four agents penalties incurred. Similarly, it is sometimes prefer- played. Minimum is the minimum among these aggregated values. Number is the able to sell entertainment tickets and shorten a number of times the agent reported an optimal allocation.This information was client’s stay accordingly. provided by the TAC organizing team. Allocation Strategies When allocating goods to their clients, most of the the airline auctions are considered in isolation, agents greedily focus on satisfying each of their waiting until the end of the game to purchase tick- clients in turn. Only the top two teams’ bidding ets is an optimal strategy, except in the rare case strategies incorporated global decision-making in where the price hits the lower bound on its value.) which the interests of all their clients were consid- Thus, the auction design offers no incentive to bid ered simultaneously. (The third-scoring agent used on airline tickets before the witching hour, since by a heuristically based intermediate approach.) This waiting there is some chance of obtaining infor- aspect of an agent’s design also affects its final mation about hotel acquisitions. allocation algorithm. The percentage of optimal There are, however, substantial risks associated allocations reported by each agent during the com- with delaying bid submission. These risks arise petition is listed in Table 1. from unpredictable network and server delays, and Although simpler, the greedy strategy is not can cause bids placed during a game to be received always optimal. For example, consider two clients, after the game is over. To cope with these risks, A and B, with identical travel preferences and the most agents dynamically computed the length of following entertainment preferences: A values the their bidding cycles and then placed their flight symphony at $90, the theater at $80, and baseball bids some calculated amount of time before the end at $70; B values the symphony at $175, the theater of a game.
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