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![Page 1: A Trading Agent for Real-Time Procurement of Bundles of Complementary Goods on Multiple Simultaneous Internet Auctions and Exchanges Erik Aurell, Mats.](https://reader035.fdocuments.us/reader035/viewer/2022081002/56649d1f5503460f949f3303/html5/thumbnails/1.jpg)
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 LaboratorySwedish Institute of Computer Science (SICS)
http://www.sics.se/isl/
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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– ...
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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
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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
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8 agents
8 clients
006
28 auctions
LivingAgents
ATTac
RoxyBot
Harami
Arc2k
Whitebear
Urlaub
flightmarkets
hotelauctions
event ticketexchanges
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Communicationwith trading
agentsMarketserver
Informationdatabase
Publish datavia web and
applet
TAC ServerTrading Agents
Game Spectators
LivingAgents
ATTac
006
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TAC-01 Game Monitor (Game 5716)
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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
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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
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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
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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
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006 Strategy
Initialize
Estimate prices
Flight
Event
Hotel
Endowment
Client prefs
Find optimalholdings
Compute target holdings
Inform auctionhandlers
Optimizer(“The Solver”)
marginal costsand prefs
target holdingsand price
e.g. Hotel auction handler
Compute new bid fromcurrent holdings, old bidand target holdings. Bid.
Monitor bid.Increase if necessary.
If transaction, auctionclose, or price > max
costthen initiate replanning.
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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
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Communication andScheduling
GameHandler
TAC Server(Michigan Auction Bot)
FlightHandler
Auction Handler
HotelHandler
Auction Handler
EntertainmentHandler
Auction Handler
”The Solver”TAC
Optimizer
Flight StrategyFlight Strategy Flight StrategyHotel Strategy Flight StrategyEnter. Strategy
006 Architecture & Implementation
• SICStus Prolog
• Explicit task scheduling
• Optimizer in separate process
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TAC-01 Scores (Semifinals)
# Agent Affiliation Score Std dev
Heat
1 livingagents Living Systems AG 3660.2 893.8 1
2 Southampton TAC University of Southampton 3614.5 747.3 1
3 Urlaub01 Penn State University 3484.8 924.1 1
4 whitebear Cornell University 3469.7 1043.0 1
5 Retsina Carnegie Mellon University 3293.5 630.9 2
6 ATTac AT&T Labs 3249.2 407.9 2
7 006 SICS 3240.8 1108.1 1
8 CaiserSose University of Essex 3038.1 640.9 2
9 TacsMan Stanford University 2966.1 595.2 2
10
PainInNEC NEC Research 2905.9 540.6 2
11
polimi_bot Politecnico di Milano 2834.7 1102.1 2
12
umbctac University of Maryland, BC 2772.9 813.5 2
13
RoxyBot Brown University 2112.4 1478.7 2
14
arc-2k Chinese U of Hong Kong 1746.3 1948.7 1
15
jboadw McGill University, CA 1716.7 1281.3 1
16
harami Bogazici University, Istanbul
94.4 2537.2 1
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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]
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Trading Agent Competition 2002
• Hosted by SICS
• Info/registration:http://www.sics.se/tac/
• New open source game and market server software