Multi-Agents System CMSC 691B Gunjan Kalra Peter DSouza

Outline n Introduction n Overview of TAC n Background – Strategy used by LARS – Strategy used by UMBCTAC – MASTAC strategy n Design n Conclusion

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

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

Commodities n 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

Commodities n Hotel Reservations n high priced (Tampa Towers) n 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

Commodities n 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

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

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

Background n 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 n Analysis of average response time to overcome network delay and server performance n Allocation provided by agents themselves in some cases

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1 ResultGrabber (offline agent) n Strategy (never changes) - offer high prices as soon as possible, win bids n No bids withdrawn or changed

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

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

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

Design n 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

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

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

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