1. A Trading Agent for Real-Time Procurement of Bundles of Complementary Goods on Multiple Simultaneous Internet Auctions and Exchanges Erik Aurell, Mats Carlsson, Joakim Eriksson, Niclas Finne, Sverker Janson, Lars Rasmusson, Magnus Boman, Per Kreuger Intelligent Systems Laboratory Swedish Institute of Computer Science (SICS) http://www.sics.se/isl/

2. Combinations of Goods/Resources Complementary goods –Demand in one decreases when price of the other increases –”both needed” Substitutable goods –Demand in one increases when price of the other increases –”one or the other” Buyer combinations –Flight and hotel nights –Project resources –VPN links –... Seller combinations –Match production facilities –Economy of scale –Byproducts –...

3. Combinatorial Markets and Trading How make the best possible global exchange of goods/resources? How buy/sell the best possible combinations at the best possible prices? Combinatorial markets –All goods on one market –Global optimization of combinatorial bids –See, e.g., Trade Extensions (tradeextensions.se) Trading agents –Goods on multiple markets –Optimized trading (online decision problem) for one or more clients

4. Trading Agent Competition (TAC) Trading Agent Competition –International annual event –Aim: stimulate research into automated combinatorial trading Model problem –Goods in travel domain: flights, hotel nights, event tickets –Agents represent clients with different preferences –Goal: buy best possible combinations at the lowest possible price A game instance –8 competing agents, each representing 8 clients –28 markets, auctions, exchanges –12 minutes The TAC-01 competition –25 participating academic and industrial research groups –Winner, livingagents by Living Systems AG, determined by thousands of games over several weeks

5. 8 agents 8 clients 006 28 auctions Living Agents ATTac Roxy Bot Harami Arc2k White bear Urlaub flight markets hotel auctions event ticket exchanges

7. TAC-01 Game Monitor (Game 5716)

8. Travel Packages, Goods and Feasibility Inflight tickets Ii –i in 1.. 4 Outflight tickets Oi –i in 2.. 5 Hotel nights Hij –i in 1.. 2, j in 1.. 4 Event tickets Eij –i in 1.. 3, j in 1.. 4 Flights, in preceding out Hotel nights in-date to out-date – 1, same hotel Up to three different events on different hotel nights E.g., I1, O3, H11, H12, E21, E32

9. Client Preferences and Utility Preferred arrival and departure date –PA in 1.. 4, PD in 2..5 Bonus for H1 –BH1 in 50.. 150 Bonus for E1, E2, E3 –BE1, BE2, BE3 in 0.. 200 E.g., PA = 1, PD = 4, BH1 = 63, BE1 = 120, BE2 = 23, BE3 = 184 Utility = 1000 – TravelPenalty + HotelBonus + EventBonus TravelPenalty = 100 * (|AA–PA| + |AD–PD|) E.g., 1000 – 100*(|1-1| + |3-4|) + 63 + 23 + 184 = 1170 Max 1750, min 400

10. Auction/Market Types Flight tickets –”Over-the-counter” –Unlimited supply –Prices in $150.. $800 –Start in $250.. $400 –Updated every ~30 seconds by -10.. X(t) Event tickets –Continuous double auction –8 tickets / event / day –12 endowed / agent Hotel nights –Ascending multi-unit Nth price auctions –16 rooms / hotel / night –Price = 16th highest bid –Price updates once a minute –Auctions close randomly, one every minute from 4th minute

11. Trading Agent Problems Strategy problem –Buy which packages? –Which packages demanded by others? –Modeling opponents? –Price expectations? –Uncertainty/risk? (Binding bids.) Optimization problem –Combine goods into travel packages for clients –Analogous to combinatorial auctioneer problem

12. 006 Strategy Initialize Estimate prices Flight Event Hotel Endowment Client prefs Find optimal holdings Compute target holdings Inform auction handlers Optimizer (“The Solver”) marginal costs and prefs target holdings and price e.g. Hotel auction handler Compute new bid from current holdings, old bid and target holdings. Bid. Monitor bid. Increase if necessary. If transaction, auction close, or price > max cost then initiate replanning.

13. 006 Optimizer (”The Solver”) Constraint programming –Finite domain constraints –Global constraints cumulatives(Ts, Ms) –task(S, D, E, H, M) –machine(M, L) Limited discrepancy search –Limit allowed backtracks –Anytime Branch-and-Bound –Bound = best so far Variable order –Arrivals, departures –Hotels –Events –Order by max utility of pertaining client Value order –Descending estimated value of X = v –I.e. average of upper and lower bound –Arrival, departure ordered pairwise

14. Communication and Scheduling Game Handler TAC Server (Michigan Auction Bot) Flight Handler Auction Handler Hotel Handler Auction Handler Entertainment Handler Auction Handler ”The Solver” TAC Optimizer Flight Strategy Hotel Strategy Flight Strategy Enter. Strategy 006 Architecture & Implementation SICStus Prolog Explicit task scheduling Optimizer in separate process

15. TAC-01 Scores (Semifinals) #AgentAffiliationScoreStd devHeat 1livingagentsLiving Systems AG3660.2893.81 2Southampton TACUniversity of Southampton3614.5747.31 3Urlaub01Penn State University3484.8924.11 4whitebearCornell University3469.71043.01 5RetsinaCarnegie Mellon University3293.5630.92 6ATTacAT&T Labs3249.2407.92 7006SICS3240.81108.11 8CaiserSoseUniversity of Essex3038.1640.92 9TacsManStanford University2966.1595.22 10PainInNECNEC Research2905.9540.62 11polimi_botPolitecnico di Milano2834.71102.12 12umbctacUniversity of Maryland, BC2772.9813.52 13RoxyBotBrown University2112.41478.72 14arc-2kChinese U of Hong Kong1746.31948.71 15jboadwMcGill University, CA1716.71281.31 16haramiBogazici University, Istanbul94.42537.21

16. 006 Problem: Unstable Solver Output Time –260 ms Utility –2736 Flight allocation –[4,4,4,4,4,4,3,4] –[5,5,5,5,5,5,4,5] Hotel allocation –[0,0,0,0,0,0,0,0] –[1,1,1,1,1,1,1,1] Event allocation –[0,4,0,4,0,0,3,0] –[0,0,0,0,0,0,0,0] –[4,0,0,0,4,4,0,0] Time –540 ms Utility –2978 Flight allocation –[3,1,3,1,2,2,3,3] –[5,5,5,4,3,5,4,5] Hotel allocation –[1,1,1,1,0,1,1,1] –[0,0,0,0,1,0,0,0] Event allocation –[0,0,3,3,0,0,3,0] –[0,0,0,0,0,0,0,0] –[4,4,0,0,2,4,0,0]

17. Trading Agent Competition 2002 Hosted by SICS Info/registration: http://www.sics.se/tac/ New open source game and market server software