slide1 n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
006 PowerPoint Presentation
Download Presentation
006

Loading in 2 Seconds...

play fullscreen
1 / 17

006 - PowerPoint PPT Presentation


  • 127 Views
  • Uploaded on

006. 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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about '006' - dai


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

006

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/

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

...

Combinations of Goods/Resources
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

Combinatorial Markets and Trading
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

Trading Agent Competition (TAC)
slide5

8 agents

28 auctions

flight

markets

White

bear

ATTac

8 clients

Living

Agents

hotel

auctions

006

Roxy

Bot

Harami

event ticket

exchanges

Arc2k

Urlaub

slide6

Trading Agents

TAC Server

006

ATTac

Communication

with trading

agents

Market

server

Living

Agents

Information

database

Publish data

via web and

applet

Game Spectators

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

Travel Packages, Goods and Feasibility
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

Client Preferences and Utility
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

Auction/Market Types
trading agent problems
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
006 strategy
006 Strategy

Optimizer

(“The Solver”)

Endowment

Initialize

Client prefs

Flight

Hotel

Estimate prices

e.g. Hotel auction handler

Event

Compute new bid from

current holdings, old bidand target holdings. Bid.

marginal costs

and prefs

Find optimal

holdings

Monitor bid.

Increase if necessary.

Compute target holdingsInform auctionhandlers

If transaction, auction

close, or price > max cost

then initiate replanning.

target holdings

and price

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

006 Optimizer (”The Solver”)
006 architecture implementation
SICStus Prolog

Explicit task scheduling

Optimizer in separate process

Flight Strategy

Flight Strategy

Flight Strategy

Enter. Strategy

Hotel Strategy

Flight Strategy

Entertainment

Handler

Hotel

Handler

Flight

Handler

Auction Handler

Auction Handler

Auction Handler

Game

Handler

”The Solver”

TAC

Optimizer

Communication and

Scheduling

TAC Server

(Michigan Auction Bot)

006 Architecture & Implementation
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]

006 Problem: Unstable Solver Output
trading agent competition 2002
Trading Agent Competition 2002
  • Hosted by SICS
  • Info/registration:http://www.sics.se/tac/
  • New open source game and market server software