A Principled Study of Design Tradeoffs for Autonomous Trading Agents Ioannis A. Vetsikas Bart Selman...
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Transcript of A Principled Study of Design Tradeoffs for Autonomous Trading Agents Ioannis A. Vetsikas Bart Selman...
A Principled Study of Design Tradeoffs for Autonomous Trading
AgentsIoannis A. Vetsikas Bart Selman
Cornell University
Agents’ Preferences
• Bidders have preferences for bundles of items– Complementarities
• Combination of goods is valued more than sum of values of individual goods : V({a,b})>V({a})+V({b})
• e.g. having a VCR and a TV together
– Substitutability• Combination of goods is valued less than sum of
values of individual goods : V({a,b})<V({a})+V({b})• e.g. having a Dell or a Gateway computer
• One formulation: Combinatorial Auctions
Bidding in Simultaneous Auctions
• Goods are traded independently• Different rules for each auction (potentially)
• Main issue: Participants need to speculate on behavior of other agents
• How aggressively does one bid, when and what for?
• Having a plan flexible enough to handle contingencies
• Best solution is relative to other players strategies
Trading Agent Competition (TAC)
• General problem capturing several issues of bidding in simultaneous auctions
• Provides a universal testbed for researchers
• Travel agents– Working on behalf of 8 customers each– Arranging for a trip to Tampa
• round-trip flight tickets• hotel accommodations• entertainment tickets
– GOAL: Maximize profit
White Bear General Architecture
Follows the SMPA architecture (loosely)
While (not end of game){ Get price quotes Calculate estimates & statistics Planner (Formulate desired plan) Bidder (Bid to implement plan)}Plan : how many goods of each type it is desirable to
allocate to each customer
Decomposing the Problem
Optimizer / Planner
AuctionType 1
PartialBidding
Strategy 1
AuctionType 2
PartialBidding
Strategy 2
AuctionType k
PartialBidding
Strategy k
Agent
Agent Components
• OPTIMIZER– INPUT: Price information from bidders
(and client preferences from original game data)– OUTPUT: Quantities of each good to be bought– METHOD: Solve optimization problem
• BIDDERS (for each auction type)– INPUT: Quantities to be bought and
pricing information from auctions
– OUTPUT: Bid Price and Bid Placement Time– METHOD: Determine strategies and experiment
to find “best” strategy profile
Determining Partial Strategies
• Determine “boundary strategies”– E.g. minimum and maximum price for the
bid, if bid price is the issue
• Determine “intermediate strategies”– By modifying boundary strategies– By combining boundary strategies– By using a strategy that constitutes an
equilibrium for a simpler but similar game
Bidding Strategies – HotelsISSUE: Bid Price
Dilemma:• If not aggressive, could get outbid and lose
rooms needed– will get outbid by other agents and lose utility
for not implementing the plan and for unused resources
• If too aggressive, prices will skyrocket and the agent’s score will get hurt more than other agents’ scores– All agents’ scores are hurt– But this hurts the agent more, since rooms it
desires will have an increased price
Bidding Strategies – Hotels (cont.)
1. Low aggressiveness : (boundary str.) Bids higher than the current ask price by an
increment2. High aggressiveness : (boundary str.)
Bids for all rooms progressively closer to the marginal utility
3. Medium aggressiveness : (intermediate str.) Combines two previous strategies For critical rooms (rooms with high marginal
utility) the bid is close to the marginal utility For all other rooms it bids an increment above
the current price (the increment increases as time passes)
Bidding – Plane TicketsISSUE: Time of Bid Placement
Dilemma:– To bid early in order to get the cheapest tickets– Or to bid later in order not to limit its options
Solution:• Bid for some of the tickets at the beginning• Bids for the rest after some hotel room auctions have
closed• Strategies: Which tickets are bought at the beginning
Bidding – Plane Tickets (cont.)
1. Late Bidder: (boundary str.) Buy at the beginning only tickets that are
“certain” to be used Buying nothing at the beginning is a clearly
inefficient strategy in this setting, so it is not used as a boundary strategy
2. Early Bidder: (boundary str.) Buy all tickets at the beginning
3. Strategic Bidder : (intermediate str.) Modifies “Early Bidder” boundary strategy Uses “Strategic Demand Reduction” [Weber
’97] Buy all tickets at the beginning, except the
ones that are “highly likely not to be used”
Exploring Strategy Space• Determine the best partial strategy for one
particular auction type– Keep all other partial strategies fixed– Use a fixed number of agents using intermediate
strategies– Vary the mixture of agents using boundary
strategies
• Explore strategy space systematically– Use several experiments to evaluate the
strategies for different auction types– Use the best partial strategies found in the
previous experiments as the strategies that are kept fixed in each experiment
– Stop when experiments “converge”
Experiment 1
0
500
1000
1500
2000
2500
3000
Less Aggressive More Aggressive Agents
Av
g S
co
re
Low Aggressiveness
Medium Aggressiveness
High Aggressiveness
A mildly aggressive agent usually performs better than agents with high or low aggressiveness
Experiment 2
1800
1900
2000
2100
2200
2300
2400
2500
0 2 4 6 Numbe r of Early-bidding age nts
Av
g. S
co
re
s
Late-bidding(med. aggr)
Late-bidding(high aggr)
Strategic(med. aggr)
Strategic(high aggr)
Early-bidding(med. aggr)
Early-bidding(high aggr)
The strategically bidding agents perform best overall
Experiment 3
2300
2350
2400
2450
2500
2550
2600
2 4 6 Num be r of Aggre s s ive Age nts
Avg
. Sco
re
M ed. A ggr(Strategic)
M ed. A ggr(Early B idder)
High A ggr(Strategic)
High A ggr(Early B idder)
The medium aggressiveness agent performs best overall
However the difference is not always significant
Some Comments
Overall the medium and high aggressiveness versions perform the best– But the medium aggressiveness agent is
more consistent in general Overall the strategic agent versions
perform the best– The early bidder is significantly better than
the late bidder In general you win when you are “going
against the tide”, i.e. being aggressive when most other agents are not
White Bear General Observations
• Planner is adaptive, versatile, fast and robust• Agent uses both principled methods and
approaches guided by the knowledge acquired by observing the behavior of the games and combines both seamlessly
• The agent used in TAC was the strategic agent with medium aggressiveness
• Agent White Bear always ranks in the top three agents in all the competition rounds of the Trading Agent Competition
TAC 2002 Final Scores
# Agent Score
1 WhiteBear 3556
2 Southampton 3492
3 Thalis 3351
4 UMBCTAC 3321
5 Walverine 3316
6 livingagents 3310
7 kavayaH 3250
8 cuhk 3248
• 19 institutions in the preliminaries
• 16 in the semi-finals
• 8 in the finals
• White Bear was 1st in the final
Related Work
• Examined the behavior of agents bidding for N similar items in an Nth price auction to find Bayes-Nash equilibria for the bid prices
• Examined the effect that better price prediction has on the performance of the agent– Using historical price information definitely improves
performance– More intelligent price prediction showed minimal
improvement
• Examined ways to reduce the number of games per experiment needed in order to derive accurate conclusions