From: AAAI Technical Report SS-01-03. Compilation copyright © 2001, AAAI (www.aaai.org). All rights reserved.
Toward Flexible
Trading Agents
Peter R. Wurman
North Carolina State University
Suite 110, Venture1; Centennial Campus
Raleigh, NC27695-7535 USA
wurman@csc.ncsu.edu
Abstract
Themajorityof studies of agent decisionmakingin environments
with multiple auctionsassumehomogeneity
in the
rules of the auctions. However,
online marketsare much
morefragmented,
and productsare often for sale in a variety of venueswithdifferent rules. Wepresenta conceptual
approachto designinge xible trading agents that is broad
enoughto encompass
both multipletypes of auctionsand a
variety of user preferences.Thegeneralizationsdescribed
lead to natural representationsof the agent’sdecisiontask
as either a Markov
decisionprocessesor an extensiveform
game,dependinguponthe formof the marketmodel.
1 Introduction
Electronic auctions have rapidly becomeone of the most
vibrant and widespread applications in e-commerce.Each
day, consumerslist millions of items in electronic auction
sites suchas ebay, and it is dif cult to nd an industrythat
is not launching a vertical B2B"exchange".This profusion
of auctions poses newchallengesfor the participants in the
marketplace. Currently, if one does a search for a common
product, such as a PalmPilot, one will nd there are hundreds of active listings hostedon a dozendifferent Websites
each with potentially different auction "types". In addition,
each auction has a unique closing time, and can have other
attributes whichaffect the buyersinterest level, suchas text
describing the quality of the product or a measureof the
seller’s reputation.
For a variety of reasons, the actual Internet marketplace
has evolvedinto a fragmentedmarketin whichrelated products are sold in various auction formatswith subtle but very
importantdifferences in rules. Seeminglyminordifferences
can induce radically different bidding behavior on the part
of the bidders. Take, for example,the different strategies
induced by the ascendingauctions that end after a period
of inactivity in whichno newbids are received (eg. Amazon.com),and those that end at a xed time (eg. ebay).
latter set of rules inducesparticipants to "snipe",that is, to
hold off bidding until the nal moments,
and then try to submit a highbid just before the auctioncloses. Thisstrategy is
ineffective in auctions withinactivity-basedclosing rules.
Copyright©2001, American
Associationfor Arti cial Intelligence(www.aaai.org).
All rights reserved.
134
Nolicense: PDFproducedby PStill (e) F. Siegert - http://www.this.net/~frank/pstill.html
Makingbidding decisions in fragmentedmarketplaces is
complex;to our knowledge,people handle this problemby
simplyrestricting their attention to a small subsetof the auctions of potential interest. Clearly this can lead to suboptimal outcomesfor both the buyers and the sellers. Software
agents havethe potential to greatly enhancean individual’s
ability to effectively participate in these environments.We
call such software programstrading agents.
In somesituations, such as consumerpurchasing on the
Internet, the conditionsthat a real agent will face will vary
from episode to episode. Evenwhenmarket conditions are
more consistent, as might be found in a B2Bexchange, we
need to be able to quickly develop and deploy agents in
each specialized environment.Adoptionof agent technologies will be limited if an agent’s bidding strategy must be
hand-coded for each marketplace, and then recoded when
the rules of the market change. Thus, we argue that trading agents mustbe designed to be exible, that is, capable
of perceiving both the context and the dynamicstate of the
marketplacein order to makerational decisions.
Consider the decision problem facing an agent whenit
desires to purchasetwounits of a particular object for sale.
The agent searches the web and nds the following three
1auctionslisting the desiredobject.
¯ AuctionA lists a single unit. The auction rules are those
of a typical ascending auction with axed closing time,
designatedta.
¯ AuctionB lists a single unit. The auction is also an ascendingauction, howeverit will close after time tB when
~Btime passes without any newbids.
¯ AuctionC lists ve units. It uses a type of multi-unit auction in whicheach bidder pays the price of the lowest accepted bid. Indivisible bids are accepted. This auction
closes after time tc when6c time passes without any new
bids.
The agent could satisfy its goals by purchasingtwo items
in auction C, one in C and one in A or B, or one in each A
and B. The bidding strategy that accomplishesthe selected
goal is dependentuponthe expectednal prices in each auction, the rules of eachauction, and the relative closing times
IAll threeof the auctionrules describedare widelyusedonthe
Intemet.
of each auction. Moreover,this decision is not static--the
agent’s expectationsof the nal prices dependsuponthe current price quotesand the actions of the other participants.
The research to date, both in economicsand computerscience, viewsthe multi-unit or multi-object allocation problems from one of two perspectives. Onebodyof work takes
the perspective of the designer of the mechanismwhose
goal is to select a set of rules that maximizessomeobjective. The approachesstudied include both auctions for
sets of identical objects (Demange,Gale, & Sotomayor
1986; Gale 1990; Rodr’guez et al. 1997) and combinatorial auctions (Lehmann, O’Callaghan, & Shoham1999;
Parkes 1999; Wellmanet al. to appear; Wurman&Wellman2000)Thesecondperspective is that of the participant.
This bodyof workfocuses on decision makingfor scenarios in whichthe auctions are homogeneous
(Boutilier, Goldszmidt, & Sabata 1999b; Hu &Wellman1998; Park 1999;
Rust, Miller, &Palmer1993). Weare interested in decision
makingfor heterogeneousmarketplaces.
This paper presents a conceptual approachtowardbuilding exible trading agents. In Section 2, we describe the
architectural elements of a exible trading agent and suggest someuseful formalisms. Section 3 describes someof
the issues related to gametheoretic and decision theoretic
strategy selection.
2 Components of a Trading Agent
Weuse the term marketplaceto refer to the combinationof
the user’s agent (called the principal agent), the auctions,
and the other bidders in the system.
Similarly, the decision makingpolicy ofa exible trading
agent can be viewedas a function with three inputs:
1. A modelof the user’s preferences.
2. Alist of auctionsthat are relevant to the task and a method
of determiningthe particular rules of each auction.
3. Amodelof the other bidders in the auctions.
Fromthese inputs, the agent mustderive a bidding strategy that whenexecuted (ideally) maximizesthe user’s expected payoff. TheStrategy GenerationEngine(SGE)is responsiblefor derivinga biddingstrategy. Figure1 illustrates
howthese componentst together. The following discussion identi es someissues associated with each of the four
components.
2.1 User Preferences
Eliciting the user’s economicpreferencesin a mannerthat is
not overly burdensometo the user and yet acquires enough
information to be of value is a challenging problem. For
example, the agent should not ask the user howmuchshe
values every make/modelof computermonitor that is currently being sold online. Instead, wewouldlike the agent
to ask a fewquestionsfromwhichit can infer a suf ciently
completepreference structure.
Althoughpreference elicitation is an interesting area of
research, for the purposesof this paper we assumethat we
havethe users’ preferencesin the formof a utility function.
Weadmita variety of preference structures, including both
135
Nolicense: PDFproducedby PStill (c) F. Siegen- http://www.this.nel/~frank/pstill.html
U set
Pref~renGes
A uctbn
Rules
M azket
Model
st~at~y
G en~t:bn
Eng~he
]
~ s~gy
Figure1: Anarchitecture for trading agents.
substitutable and complementary
preferences, and both independentprivate values and af liated common
value problems.
2.2 Auction Rules
In previous work (Wurman,Wellman,& Walshto appear),
we have described a parametrization of auction rules that
encompassesthe majority of common
auction formats, including those used by most Intemet auction sites. Moreover,
the parametrizedrules are instantiated in an online auction
server called the Michigan Intemet AuctionBot (Wurman,
Wellman,& Walsh 1998).
The parameters are organized according to whether they
governthe admittanceof bids into the system,the revelation
of informationby the auction, or the policies and timing of
eventsthat clear the auction.
For example,rules associated with bid admittancedeterminewhois allowed to bid, the format of bids, and whether
and in what mannerbids must "improve".Rulesthat control
clearing specify the eventsthat trigger a clear (e.g. at axed
time or after no bids are received for z minutes), and the
policies used to computethe prices, quantities, and trading
partners involvedin the resulting exchanges.
The rules are, to a large extent, orthogonal, enabling a
combinatorial numberof different auction types. Not only
can the vast majority of auctions available on the Internet
can be de ned using the parametrizedrules, a large number
of newand potentially useful auctions can be identi ed.
The AuctionBotserver also includes an Agent ProgrammingInterface (API)whichpermits software agents to interact with the server. Throughthe APIthe agent can request
the speci c rules of each auction. Currently, the AuctionBot returns a text string specifying the auction rules. Work
is currently underwayto convert the API to an XML
based
interaction.
2.3 Market Models
Howmuchand what type of knowledgeof the other participants in the marketdoes an agent need to perform effectively? This is one of the fundamentalresearch questionsin
the design of trading agents. Amongthe choices of market
modelsare:
I. A modelof the evolutionof prices that ignores the behavior of individual non-principalparticipants. This essentially treats the external marketas a single entity wecall
a representative agent, with (potentially uncertain) reactions to the agent’sbids.
A modelof the individual participants in the auctions. In
.
general there are a variety of issues that comeup when
modelingother participants.
¯ Are the other agents’ preferences known?
. Are the other agents’ strategies known?
¯ Whatinformation do the other agents have about our
agent, and howis that informationused?
The model used by Boutilier, Goldszmidt, and Sabata
(1999b) falls into the rst category, while the modelexplored by Hu and Wellman(1998) explores multiagent
learning in the secondcontext. Theseexampleshelp illustrate the spectrumof potential models.At one end are gross
level modelsin whichthe behaviorof the other agents is encapsulatedin a single representative agent. At the other end
of the spectrum we have gametheoretic models which assumeperfectly rational, self-interested agents with common
knowledge
of each other’s utility functionsand capabilities.
Theapplicability of the potential representations is also
affected by features of the particular market.For instance,
consider a marketplacewith several sequential auctions for
individual, substitutable objects and axed numberof bidders. As the marketplaceprogresses and someof the objects
are allocated, the numberof agents competingdecreases.
Moreimportantly, the past behaviorof the remainingagents,
and the fact that they haven’t yet purchasedan object, provides information to the principal agent regarding the remainingagent’s preferences. A purely gross level representation maynot properly capture this valuableinformation.
However,
in practice it is quite rare to haveas muchinformationas the gametheoretic modelassumes.Eventually,
we wouldlike our market modelsto be based on the types
of real data that is accessibleto the agent. Manysites, such
as ebay, provide identifying informationthat can be used to
track the behaviorof individual participants. Weare writing softwareto collect this type of informationfroma small
set of auctionsin a particular productarea andwill use it to
explore the automatedconstruction of marketmodels.
2.4 Strategy Generation Engine (SGE)
Theprimaryfocus of this project is to build a strategy generation engine. At an abstract level, we canseparate strategy
generation from strategy implementation,that is, we assume
we can generate a completepolicy a priori that species the
agent’s best action for every state it mayencounter.Froma
practical point of view, it is often not desirable to precompute a policy for all possible states, and therefore somereplanningis desirable.
Thetask of the strategy generation engine is to compute
a strategy that maximizesthe agent’s utility. The methodology that is employedin this task dependsuponthe internal
136
No license: PDFproduced by PStill (c) F. Siegert - http://www.this.net/~frank/pstill.html
A gent
A uctbn
com pu ~
A u ctSon
corn putts
new sl~l~
new stmt~
responds
Figure 2: Thecycle of actions.
modelused to represent the inputs to the SGE.For example, if individual agents are modeled,then a gametheoretic
representation maybe appropriate. Onthe other hand, if
the marketis modeledas a stochastic state machine, then
a MarkovDecision Process maybe a useful frameworkfor
makingdecisions. In this section, we describe the SGEat an
abstract level in the framework
presentedby Boutilier, Dean,
and Hanks(Boutilier, Dean, &Hanks1999).
Weassumea discrete time environmentin whichthe agent
modelsthe worldas the cycle of actions depictedin Figure2.
The agent evaluates the marketplaceand selects an action
(i.e. bids). Asa result of this action, the auctions compute
new states. The other agents then respond to the newinformation, whichin turn causes the auctions to recompute
states. Eachiteration of the cycle is called a stage, denoted
t.
Formally, the environmentin whicha trading agent operates is a collection of auctions, {cq,..., am}, each with
its ownset of rules. The principal agent is denotedu, and
the marketis inhabited by other agents, D= {dl, .. ¯, dn}.
Thesubset of Dthat participates in auction i is denotedDi.
If Di f’l Dj = { } for all i # ] then we can safely assume
that the agent’s actions in i does not affect the behaviorof
the agents in j. However,we expect that there will be many
situations in whichthe participation is not disjoint among
the auctionsof interest.
Each auction has a state, ~rg, which is de ned by the
sequence of messages(bids) that the agents have sent
the auction. The marketstate is the set of auction states,
S = {crt1 ... cry’}. The rules of each auction greatly in uence the state variables that mustbe stored to represent the
auction. In addition, the rules de ne the agent’s permissible
actions froma given state. Let fi (~r~) be the function that
returns the set of actions, includingthe null action, that the
principal agent cantake in auction i at timet.
Typically, but not always, the permissible actions depend
on only the most recent bid fromeach agent in each auction,
which enables us to makeuse of the Markovassumption.
However,in somesituations (eg. with activity rules) f can
dependuponthe entire history of messages.In such cases,
the state maybe able to be augmentedwith summaryinformationwhich captures the current restrictions imposedby
the activity rules. Theset of joint actions that the agent can
take at a given instant is the combinationof actions in individual auctions. That is, At = ®fi (~r~).
Therules also de ne the auction’s state transition for
a given action, a E A. Wedenote the state transition
function for the auction i by #i. Thus, a successor state,
~r’ = #i(a, (r), is reachedwhenaction a is taken. In mostresearch on iterative auctions, this function is deterministic-if the agent bids morethan the current highest bidder, it
will be the new high bidder. However, in some recent
iterative combinatorial auctions (DeMartiniet al. 1998;
Wurman&Wellman2000) the agent cannot always predict
fromthe informationit is giventhe state transition that will
result from an action. This occurs becausethe agent cannot
directly observethe importantaspects of the auction’s state
(i.e. all of the current bids). Instead, it can observeonlythe
price informationrevealedby the auction. Whenthe state is
only partially observable,the agent maynot be able to tell
with certainty whichstate it is in, greatly complicatingthe
decision problem.
Uncertainstate transitions can also be usedto modelirregularities in systemperformance.
For instance, it is a fact of
Internet life that occasionallymessagesare lost in the networkand servers crash. Also, the amountof time that it
takes for a bid to reach the auction server and be admitted
varies with networkcongestion and server load. Thus, all
bids, but especially last minutebids, incur someprobability
of failing to reach the server in time to be counted. These
randomevents can be accountedfor in the state transition
model.
Typically, the other participants will respond to the
agent’s action with actions of their own.It is at this point
that we start to see the interplay betweenthe marketmodel
and the decision representation. If the agent is planningon
using gametheoretic reasoning, it needs a methodto compute the other agents’ choicesof actions froma given state.
Typically, the other agents have the samebasic choices as
the principal agent, and thus the logic used in the function
f can be reused. However,wheneach of the other agents is
acting independently,the "marketresponds"step in Figure 2
wouldhave to be expanded.
Onthe other hand, the agent maychoose to use an aggregate modelof the other participants that treats themas a
single representative agent whichtakes a single (joint) action 7-. Todeterminethe representative agent’s choice(s)
actions, weuse the responsefunction,/~. Whenthe response
function is deterministic, our agent’s next decision point is
in state o’" = gi(r, gi(a, o’)) wherer It (gi(a, o’)). Wh
the functionis not deterministic,it returns a set of possible
states with associated probabilities. Notethat the representative agent’s responsefunction is also constrained by the
rules of the auctions.
It is natural in this modelto distinguish betweenthe effects of the principal’s actions and the effects of the other
agents in the system.Thus, wesuggest that a exible trading
agent be basedon an explicit-event model(Boutilier, Dean,
& Hanks1999). Doingso allows us to explore the approach
of treating marketmodelsas moduleswith well de ned programminginterfaces. Whennecessary, we can construct an
implicit modelfromthe explicit models.
A bidding policy, lr is a mappingbetweenstates and actions, ~r : S --+ A. Whenthe states are only partially ob-
137
Nolicense: PDFproducedby PStill (c) F. Siegert - http://www.this.nel/~frank/pstill.html
servable, the policy is a mappingbetweenobservations and
actions.
The purposeof an auction is to generate an allocation of
the resources as a function of the messagesreceived. The
allocations that an agent receivesdeterminesits nal utility.
Wecall the auction’s policy for determiningallocations the
allocation function, h. Essentially, the allocation function
runs the samecode as the auction server, althoughit can do
so withoutthe real-time constraints of the actual servers and
can take advantageof certain ef ciencies in the computation.
2.5 Generating Admissible Actions
The parametrization of the auction design space provides
a convenient way in which to de ne and communicatethe
rules of an auction. However,the decompositionof the auction rules does not correspondto a useful parametrization
of the space of bidding strategies. To illustrate the point,
considerthe strategies of an agent biddingin ebay’s version
of the English auction--which closes at axed time--and
an agent bidding in Amazon.com’s
moretraditional English
auction. The only rule different betweenthe two sites is
the parameterthat controls the timingof the clearing event.
However,that difference induces very dissimilar bidding
strategies. In particular, ebay’s rules promotea strategy
equivalentto that of the rst-price, sealed-bidauction: estimate the secondhighest bid and bid slightly aboveit. Amazon’s version of the auction induces the standard English
type bidding behavior--keep bidding higher than the other
buyersuntil either youwinor youare unwillingto pay more.
Becausethere are potentially millions of waysto combine
the auction parameters, we cannot makemuchprogress towardexible agents by analyzing individual auction types.
Moreover,whenmultiple auctions are considered, the timing of the events and relationships betweenthe goodscan
interact in complexways. Thus, we advocate automatic exploration of the space of strategies. Toaccomplishthis, we
need a way to transform the auction rules into admissible
actions.
Oneapproachwhichwe are exploringis the use of declarative languagesfor reasoning about actions. The following
description is meantto be suggestive of our approach.Consider a rule that de nes the action of placing a bid that is c
morethan the current price quotein auction i:
bid(i,
quote(i,
s) + e) ~ winning(i,s)
A~elosed(i, s).
Thisrule can be interpreted as: bid in auctioni for a price
e morethan the current price quote if auction i is not closed
andthe agent is currently winningzero units in i.
This schemedependsuponus storing quite a bit of informationin the state o’. In particular, we needto store winning, closed, and quote for each auction. However,
this informationis easily derived from the auction’s state
transition function.
Theaboverule is applicable only to single unit auctions.
To develop a rule set applicable to the examplefrom Section 1, we needto introduce quantity to both the state and
Auction closed
1
truc
2
false
3
false
winning
1
0
0
bid
1 @$10
1 @$7
1 @$6
quote
1 @$10
1@$9
1 @$8
2 @$7.5 each
Table1: A state in the thee auction example.
the action rules. Table 1 showsa state that mayoccur for
the three auction examplefrom Section 1. Thequote for the
third auction displays prices that dependuponthe numberof
units requested.
The modied action rule is
bid(i,
quots(i,s,q)
+e,q) ,in ning(i,s) < q
A’=elosed(i,s).
Thisrule states that an agent can place a bid in auction i
for q units for a dollar value quote(i, s, q) + e if the auction is not closed and the agent is winningless than q units.
Assumingthe state in Table 1 and that the domainof i is
{1,2,3} and the domainof q is {1, 2}, the following conclusions can be drawn:
bid(2,
$9 + e, 1)
bid(a, $8 + c, 1)
bid(3, $15 + e, 2)
Notice that this formulation permits the action
bid(a,$15 + c,2) even though the agent has already
wonan item in the rst auction. In this step weare computing all possibleactions. It is the task of the decisionprocess
to evaluatethe utility of these actions.
The above presentation is suggestive--a completeaction
speci cation will require null actions and wait actions, and
a variety of other time-relatedreasoning.
3 Decision Representations
Wesee a natural connection betweendecision representations and the market model. Whenthe agent constructs a
modelof the other individuals in the market, it can use game
theory to select a strategy. However,whenthe market is
modeledas a reactive black box, then MarkovDecisionProcesses are an appropriatetool.
3.1 Game-Theoretic Models
Gametheory (Fudenberg &Tirole 1996), the fundamental
tool for analysis in microeconomic
settings, is applicableto
decision makingin fragmentedmarkets. A particular fragmentedmarketscenario can be modeledas a strategic form
gameusing standard notation. In gametheory terminology,
the sequencesof bids are the agent’s strategies, and the sequences of bids by the other agents (or marketresponses)
de ne the strategies of the opponent(s).Thus, the decision
problem reduces to a problem of nding a Hash or BayesNashequilibrium. However,the strategic form is a cumber-
138
Nolicense: PDFproducedby PStill (c) F. Siegert - http://www.this.nel/~frank/pstill.html
someview of the decision problem. It is more natural to
view the analysis in an extensive form game.
GameConstruction Let us continue with the scenario
presented in Section 1. The agent is interested in buying
two items for no more than $5 each, and has three auctions in whichit can participate. Supposethat the current
price quotes from each auction are PA= $3, PB = $4, and
Pc = $2 for one unit and $3 for two units. Supposethat the
only admissiblebids in each auction are exactly $1 over the
current price quote.
If we viewthe agent’s problemas an extensive form game,
the situation describedaboveis one branchon the gametree.
This branch, and the actions associated with it are shownin
Figure 3. The rectangle represents the gamestate, while the
roundedrectangles indicate the agent’s possible actions. The
actions are composedof bids placed in one or more of the
three auctions (i.e. bc denotes placing a bid in auction C
for the designatedvalue). Eachaction leads to a newgame
state, whichin turn presents the agent with a newchoice of
actions.
In somecases, the agent maynot be able to precisely predict the outcomeof its actions. This can occur whenthe
agent does not have complete information about the other
agent’s actions in the auction, and thus cannotprecisely predict the effect that its bid will have on the auction state.
Thesesituations can be modeledusing the notation for imperfect information in extensive form games(Fudenberg
Tirole 1996).
Strategy Selection The purpose of constructing a
game-theoretic representation of the agent’s decision
problem is to enable the agent to select a strategy.
Considerable work has been done on computational
game theory (Koller, Megiddo, & von Stengel 1994;
1996; McKelvey & McLennan 1996),
and
general
purpose
systems,
such as GAMBIT
(http://masada.hss.caltech.edu/~ambit/Gambit.html) and
GALA
(http://robot ics.stan ford.edu/koller/papers/gala.html),
havebeen built for solving games.
However,gametheoretic representations quickly become
intractable as the numberof strategies and numberof agents
increase. It maybe possibleto exploit regularities in the auction gamesto improvethe performanceof the general algorithms. For instance, in somesituations the agent’s decision
problemmaybe effectively treated hierarchically--at a high
level we mayrepresent the agent’s action in an ascending
auction simply as the maximum
bid it will place, and in a
subplanenumeratingall of the intermediatebids. Similarly,
whenthe valuesof bids are not restricted (as in the example),
we need methodsto represent and reason with action ranges
(eg. bid between$4 and $5) (cf. (Boutilier, Goldszmidt,
Sabata 1999a)).
3.2
Markov Decision Models
Wealso anticipate that somemodelsof the marketbehavior, particularly those that modelthe marketresponseas a
purely reactive entity, will be amendableto decision making using MarkovDecision Processes (MDPs)(Boutilier,
/
PA = $3
PB = $4
Pc = $2 ~:)roneunJt
$3 each ]br ~ o
Figure 3: A portion of the gametree for an agent with a choiceof biddingin three auctions withdifferent rules.
Dean, & Hanks 1999). In the simplest case, the market
function generates a single marketaction, 7- and the state
transition function is as described in Section 2.4. Thegeneral modelpresentedin this paper admits various sources of
imperfectinformation,such as probability distributions over
the agents’ preferences, af iiated common
values, or simply
the fact that the agent’s observationsof price quotes are an
indirect measureof the other agent’sbids. In suchcases, the
marketresponsefunction returns a set of possiblemarketactions whichin turn result in a set of possiblesuccessorstates.
Thesesituations can be modeledusing Partially Observable
MarkovDecision Processes (POMDPs).
The agent frameworkwhich we are building will be designed to be modularso that we can implementboth of the
above decision representations. Whenthe agent has several potential decisionrepresentationsavailable, it needsto
performmetareasoningto select one to use for a particular
probleminstance. This suggests another avenueof research,
namely,the automaticselection of decision representation
based on dynamicassessmentof problemfeatures.
4 Conclusion
In this article we have advocatedan approachto building
trading agents that stresses exibility. Wehave identi ed
three inputs whichare central to a trading agent: user preferences, auction rules, and a marketmodel.Theseinputs feed
the strategy generationand the strategy evaluation engines.
Gametheory and decision theory, whencombinedwith an
appropriate marketmodel,can be used for the task of strategy selection. Thearchitecture can handle a widevariety of
auction rules and agent preferences.
Weare currently building a trading agent in the spirit of
the one describedhere.
acknowledgments
I would like to thank Stuart Feldman, AmyGreenwald,
David Parkes, MikeWellman,and Weili Zhu for valuable
conversationsregarding this work.
139
No license: PDFproduced by PStill (e) F. Siegert - http://www.this.net/~frank/pstill.html
References
Boutilier, C.; Dean, T.; and Hanks, S. 1999. Decisiontheoretic planning: Structural assumptionsand computational leverage. Journalof Arti cial Intelligence Research
11:1-94.
Boutilier, C.; Goldszmidt,M.; and Sabata, B. 1999a. Continuous value function approximationfor sequential bidding policies. In Fifteenth Conferenceon Uncertaintyin
Arti cial Intelligence.
Boutilier, C.; Goldszmidt,M.; and Sabata, B. 1999b. Sequential auctions for the allocation of resources with complementarities. In Sixteenth InternationalJoint Conference
on Arti cial Intelligence.
Demange,G.; Gale, D.; and Sotomayor, M. 1986. Multiitem auctions. Journal of Political Economy
94:863-72.
DeMartini, C.; Kwasnica, A. M.; Ledyard, J. O.; and
Porter, D. 1998. A new and improved design for multiobject iterative auctions.Technicalreport, CaliforniaInstitute of Technology.
Fudenberg, D., and Tirole, J. 1996. GameTheory. MIT
Press.
Gale, I. 1990. A multiple-objectauction with superadditive
values. EconomicsLetters 34:323-28.
Hu, J., and Wellman,M. P. 1998. Online learning about
other agents in a dynamicmultiagent system. In Second
International Conference on AutonomousAgents (Agents
98), 239-246.
Koller, D.; Megiddo,N.; and von Stengel, B. 1994. Fast
algorithmsfor nding randomizedstrategies in gametrees.
In 26th ACMSymposium on the Theory of Computing,
750-759.
Koller, D.; Megiddo,N.; and von Stengel, B. 1996. Efcient computationof equilibria for extensive two-person
games. Gamesand EconomicBehavior 14(2):247-259.
Lehmann,D.; O’Callaghan, L. I.; and Shoham,Y. 1999.
Truth revelation in rapid, approximatelyef cient combina-
torial auctions. In ACMConference on Electronic Commerce, 96-102.
McKelvey,R. D., and McLennan,A. 1996. Computation
of equilibrium in nite games. In Handbookof Computational Economics,volume1. Amsterdam:Elsevier Science, B.V.
Park, S. 1999. The P-Strategy: An Adaptive Agent Bidding Strategy Based on Stochastic Modelingfor Continuous DoubleAuctions. Ph.D. Dissertation, University of
Michigan.
Parkes, D. C. 1999. /Bundle: Anefcient ascending price
bundle auction. First ACMConferenceon Electronic Commerce 148-157.
Rodr’guez,J.; Noriega,P.; Sierra, C.; and Padget,J. 1997.
FM96.5a java-based electronic auction house. In Second International Conferenceon the Practical Application
of lntelligent Agents and Multi-Agent Technology(PAAM
’97).
Rust, J.; Miller, J. H.; and Palmer, R. 1993. Behaviorof
Trading Automatain a ComputerizedDoubleAuction Market. Reading, MA:Addison-Wesley
Publishing. chapter 6,
155-98.
Wellman, M. P.; Walsh, W. E.; Wurman, P. R.; and
MacKie-Mason,J. K. to appear. Auction protocols for
decentralized scheduling. Gamesand EconomicBehavior.
Wurman,P. R., and Wellman, M. P. 2000. AkBA:A progressive,
anonymous-price
combinatorial
auction.
In Second ACMConference
on Electronic
Commerce,
21-29.
Wurman,P. R.; Weilman,M. P.; and Walsh, W. E. 1998.
The MichiganInternet AuctionBot:A con gurable auction
server for humanand software agents. In SecondInternational Conferenceon AutonomousAgents, 301-308.
Wurman,P. R.; Wellman,M. P.; and Walsh, W.E. to appear. A parametrizationof the auction design space. Games
and EconomicBehavior.
140
Nolicense: PDFproducedby PStill (c) F. Siegert - http://www.this.net/~ffank/pstill.html