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Optimizing Multi-Agent Strategies for Trading Agent Competition

Explore the strategies used in the Trading Agent Competition (TAC), including background on commodities, scoring, and general strategies. Learn about Living Agents' strategy and how it enhances UMBCTac's approach. Delve into the design and advantages of the MASTAC strategy. Conclude with performance benefits and potential real-life implementation.

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Optimizing Multi-Agent Strategies for Trading Agent Competition

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  1. Multi-Agents SystemCMSC 691BGunjan KalraPeter DSouza

  2. Outline • Introduction • Overview of TAC • Background • Strategy used by LARS • Strategy used by UMBCTAC • MASTAC strategy • Design • Conclusion

  3. Introduction • TAC - trading agent competition held annually • Strategies used involve single agent per competitor • Winners (Living Agents) used a multi-agent strategy that proved to be most effective • MASTAC : Enhancing UMBCTac’s strategy by incorporating Living Agents’ architecture

  4. Trading Agent Competition • 12 minute game - 8 competitors per game • Agents communicate with AuctionBot server • Each competitor has 8 clients • 3 commodities - flight tickets, hotel reservations and entertainment tickets • Objective - maximize the total satisfaction of the clients • Learning patterns developed in qualifying rounds

  5. Commodities • Flights • One flight both ways per day (no in-flights on last day, no out-flights on first day) • Single seller auctions • Prices set by AuctionBot agent according to stochastic function - Perturbations induced in price periodically • Unlimited supply of seats

  6. Commodities • Hotel Reservations • high priced (Tampa Towers) • low priced (Shoreline Shanty) • Ascending auctions • 16 rooms auctioned off each day • 16 highest bids accepted • Cost price for all winners = price of 16th highest bid = Ask price of next set of bids • Only hotels can sell rooms

  7. Commodities • Entertainment • Alligator wrestling, amusement park and museum • Each agent receives allotment of 12 tickets • 8 tickets per event type allotted • Agents exchange bids continuously through double auctions • One auction for each event-night combination

  8. Scoring • Penalty of 200 assessed for each ticket owed (sell tickets the agent does not own) • Allocation done by TAC scorer • Value = sum of individual client utilities • Final score=(value of allocation) - (travel agent’s expenses) - (penalty for negative entertainment balances) • Optimal allocation done

  9. Background • General strategies used • Strategies based on obtaining hotel rooms • Price prediction algorithm • Hotel auction models • Concentrating on individual preferences vs. aggregate purchases • Linear programming solution to find an optimal purchase • Greedy algorithm used in some cases

  10. Background • Risk analysis strategies prior to purchase • lengthen travel if customer preferences showed higher score for attending entertainment • shorten travel if cost of hotel exceeded penalty • Analysis of average response time to overcome network delay and server performance • Allocation provided by agents themselves in some cases

  11. Living Agents’ Strategy • 20 agents of 5 different types used • 1 TACManager • 5 TACDataGrabber agents • 8 TACClient agents • 5 TACAuctioneer agents • 1 ResultGrabber (offline agent) • Strategy (never changes) - offer high prices as soon as possible, win bids • No bids withdrawn or changed

  12. Living Agents’ Strategy • Flight and hotel auctioneers bid for needs of the clients only • Entertainment auctioneers place higher bids for buying and lower bids for selling • Best journey calculated based on client-preferences, flight prices and average hotel prices

  13. UMBCTac strategy • An adaptive best plan for every client • Computes TAC value by contrasting every possible plan against client preferences • Larger the TAC value, better the match • Best plans may change every time new TAC data is obtained • Concentrate on only three clients per cycle due to huge computation time

  14. UMBCTac strategy • Sleeps for a minute to compute new bids • Flights - tickets bought early in the game • Hotels - offer higher price initially itself • Entertainment - first find one ticket that had maximum entertainment and assign it to a customer who is willing to pay for it

  15. Design • Strategy • one agent works toward obtaining one client’s best travel plan • one MASTACmgr agent acts as an interface between 8 client agents and TAC server • computes next set of bids based on best bid estimates received from each client agent

  16. Design • Flights • Collect tickets as soon as possible • Hotels • Same as that of UMBCTac • Decision made on per-customer basis • Entertainment • Adjust entertainment tickets between agents to optimize distribution of tickets

  17. Advantages • Huge search space distributed amongst 8 client agents • Less computation time per agent • Additional capability to bid for more goods than a single agent can • Better allocation possible due to better travel plans for every client

  18. Conclusion • Performance benefits observed in real-life scenarios with longer time duration • Implementation results still to be obtained • Less overhead in communication time as compared to Living Agents’ strategy • Higher throughput as compared to UMBCTac strategy

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