Agent-mediated Electronic Commerce 한국정보과학회 ’00 가을 학술발표 Tutorial 2000....

Agent-mediatedElectronic Commerce

Agent-mediatedElectronic Commerce

한국정보과학회’00 가을 학술발표 Tutorial

2000. 10. 28

부경대학교 정 목 동

한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 2

ContentsContents

Introduction

State of the art

Auctions and biddings

Negotiation

Future Trends


IntroductionIntroduction The combination of EC and intelligent agents

– Deliver enormous economic benefits

Electronic Commerce(EC)– A dynamic set of technologies, integrated applications

and multienterprise business processes that link enterprise together

– Advertising, searching, negotiating, ordering, delivering, paying, using, and servicing


Introduction(cont)Introduction(cont) Intelligent Agents

– Exchange information and services with other programs and thereby solve problems that cannot be solved alone

– A system that senses the environment and acts on it, in pursuit of its own agenda


Introduction (cont)Introduction (cont) Agent classification

– Reactive : responds to changes in the environment

– Autonomous : exercises control over its own actions

– Goal-oriented (= pro-active purposeful) : doesn't simply act in response to the environment

– Learning(= adaptive) : changes its behavior based on its previous experience

– Mobile : able to transport itself from one machine to another

– Flexible : actions are not strict


Introduction(cont)Introduction(cont) Forrester Research estimates

– Online retail sales were about $600 million in 1996

– Exceed $2 billion in 1997

– Will reach $17 billion USD in 2001

People can go to find, buy, and sell goods– BF, OnSale, Amazon

– None of these sites has the idea of an autonomous agent

Today’s first generation shopping agent – Limited to comparing merchant offerings only on price


Introduction(cont)Introduction(cont) Agent mediated electronic commerce

– Enables cheap negotiation between buyers and sellers on the details of an individual transaction

– Product features, services, financing and price

Auctions – Provide a well-defined framework for negotiation

between buyers and sellers

– Can be extended to include negotiations over product features, warranties, and service policies


Introduction(cont)Introduction(cont) Bidding agents

– Tradeoffs between features within an auction framework

– The space of different product specifications is large

– Goods are nonstandard

– How to elicit only necessary information from users


Introduction(cont)Introduction(cont) Fully autonomous agent

– Requires a complete set of preferences in order to represent a user correctly in all situations

Semi-autonomous agent– Bid on behalf of the user when it has enough

knowledge

– Query the user when its best action is ill-defined


State of the artState of the art Kasbah : MIT Media Lab

– Web-based multi-agent classified ad system

– A useful platform for experiments with groups of users

– A user creates an agent, gives some strategic direction, and send it off into a centralized agent market place

– After matched, buying agents offer a bid to sellers

– Selling agents respond with either a “yes” or “no”

– three strategies : anxious, cool-headed, and frugal


State of the art(cont)State of the art(cont) Tete-a-Tete : MIT Media Lab

– An agent-mediated comparison shopping system

– Negotiate across multiple terms of transaction, such as warranties, delivery times, service contrasts, and return policies

– The decision support module uses MAUT(Multi-Attribute Utility Theory) and product customization


State of the art (cont)State of the art (cont) AuctionBot : Univ of Michigan

– General purpose Internet auction server

– Users create new auctions to buy or sell products

– Buyers and sellers bid according to the negotiation protocols

– Provides an API to create user’s own software agents


State of the art(cont)State of the art(cont) MAGNET : Univ of Minnesota

– Includes a market infrastructure and a set of agents

– Plan Execution by Contracting

– Begins after the session has been initiated by a customer agent

– The customer issues a call-for-bids

– The suppliers reply with bids

– The customer accepts the bids


State of the art(cont)State of the art(cont) Fish Market : Spain, CSIC

– Described as a place where several scenes run simultaneously

– The principal scene is the auction itself, in which buyers bid for boxes of fish

– Prices in descending order

– The downward bidding protocol


State of the art(cont)State of the art(cont) Downward bidding protocol

– step 1 : the auctioneer chooses a good

– step 2 : the auctioneer opens a bidding round

– step 3 : for each price, several situations might arise

Multiple bids : several buyers submit their bids

the auctioneer restarts the round at a higher price

One bid : only one buyer submit a bid

No bids : no buyer submits a bid. If the reserve price

has been reached or not, quotes a new price or

closes the round

– step 4 :The first three steps repeat


State of the art(cont)State of the art(cont) PERSUADER : CMU

– Integrates Case-Base Reasoning and MAUT to resolve conflicts through negotiation in group problem solving settings

MAUT(Multi-Attribute Utility Theory) – Analyzes decision problems quantitatively through utili

ties

Constraint Satisfaction Problems(CSPs)– Analyze decision problems more qualitatively through

constraints


Auction and BiddingAuction and Bidding Auction mechanisms

– Discover the optimal price for a good through the bidding action of self-interested agents

Traditional off-line auction– The interested parties gather on a physical location

The infrastructure of EC – Reduces the costs of participation

– Allows auctions to reach a large and physically distributed audience


Auction and Bidding(cont)Auction and Bidding(cont) Ascending price auctions

– The auctioneer reveals the highest bid received

– The ask price is minimum increment above the price of the current highest bid

Descending price auction– The auctioneer lowers the ask price until the first bid is

received


Auction and Bidding(cont)Auction and Bidding(cont) First-price open-cry auctions

– Highest bid wins the good for that price

– The winning bid is always greater than the product’s market valuation : known as “winner’s curse”

Second price auctions– An agent pays the highest amount that was bid by

another agent


Auction and Bidding(cont)Auction and Bidding(cont) In a sealed bid auction

– All bids are private– The auctioneer selects the winning bid after a fixed period of

time

Second price sealed bid (Vickrey) auctions– Attractive in traditional auction domains– Avoid the communication cost of multiple bids in ascending

auctions– Avoid the "gaming" that is required to estimate the second hi

ghest bid in first price sealed bid auctions– few real-world examples


Auction and Bidding(cont)Auction and Bidding(cont) Call for bids includes

– A time window

– A bid deadline

– The time at which the customer will begin considering the bids

– The earliest time at which bid acceptances will be sent

– Penalty functions for each subtasks


Auction and Bidding(cont)Auction and Bidding(cont) Each supplier will inspect the call-for-bids

– Decide whether or not it should respond with a bid

– If yes, it will send a bid message

The supplier must indicate – The cost

– The time window

– The estimated duration of the work

– The same data for each of the separate subtasks

– The bid-accept deadline

– Penalty function


Auction and Bidding(cont)Auction and Bidding(cont) Which bids to accept using knowledge about

– The bids

– The task and subtask values

– Its own time constraints and the bidder

The customer decide to do one of three things– Accept the whole bid

– Accept a subset of the subtasks in the bid

– Reject the bid


NegotiationsNegotiations Two types of negotiations

– Distributive negotiation

– Integrative negotiation

Typical negotiation– Figure 1: Typical operation model


NegotiationsNegotiations

x*


Negotiations(cont)Negotiations(cont) Distributive negotiation

– Resolving a conflict involving two or more parties over a single mutually exclusive goal

– The economics : market price of a limited resource

– The game theory : a zero-sum game

– Win-lose type of negotiation

– Stock markets (NASDAQ)

– Fine art auction houses(Sotheby's)

– Flower auctions (Holland)


Negotiations(cont)Negotiations(cont) Integrative negotiation

– Resolving a conflict involving two or more parties over multiple independent, but non-mutually exclusive goals

– Multi-objective decisions comes from economics : Multi-Attribute Utility Theory(MAUT)

– The game theory : non-zero-sum game

– Win-win type of negotiation

– A customer's goals : little money and hassle as possible

– A merchant's goals : long-term profitability

– This type of negotiation can help maximize both goals


Kasbah’s CBB modelKasbah’s CBB model Consumer Buying Behavior model

– Categorize existing agent-mediated ECs

1. Need Identification– The consumer becoming aware of some unmet need

– Is called Problem Recognition

2. Product Brokering– Determine what to buy

– The evaluation of product alternatives


Kasbah’s CBB model (cont)Kasbah’s CBB model (cont)

3. Merchant Brokering– Determine who to buy from

– The evaluation of merchant alternatives

4. Negotiation– Determine the terms of the transaction

– In traditional retail markets, prices are often fixed

– In others, the negotiation or the deal are integral


Kasbah’s CBB model(cont)Kasbah’s CBB model(cont)

5. Purchase and Delivery– Termination of the negotiation stage

6. Product Service and Evaluation– Product service, customer service, and an evaluation of

the satisfaction


MAUTMAUT Preparing for Assessment.

– Let X be the evaluator function, which associates to a consequence Q the real number x = X(Q).

– Are higher x values more or less desirable?

– Ask whether we prefer a consequence x1 to

consequence x2.

– We might ask him whether or not he prefers consequence T to consequence S in Figure 2 .

– Fig.1 shows a two-attribute, Y and Z, consequence space.


MAUT (cont)MAUT (cont)

Figure 2. A two-attribute consequence space

Q

R

y o y 1 y 2

y *

z *

z 1

z2

z oS T


MAUT(cont)MAUT(cont) Identifying relevant independence assumptions

– Verify whether Y and Z are additive independent and if either attribute is utility independent of the other.

Identifying relevant qualitative characteristics– Whether or not the utility function is monotonic.

– If xk is greater than xj, is xk always preferred to xj ?

– Whether u is risk averse, risk neutral, or risk prone

– Ask the decision maker if he prefers <x+h, x-h> or x


MAUT(cont)MAUT(cont)

Choosing a Utility Function– Utility functions : monotonically increasing in x and de

creasingly risk averse

u(x) = h + k(-e-ax -be-cx),

where a, b, c, and k are positive constants



Ask the decision maker some meaningful qualitative questions about ki's

Would you rather have attribute X1 pushed to x*1 t

han both X2 and X3 pushed to x*2 and x*

3?

– A yes answer would imply k1 > k2 + k3,

which means k1 > .5

Would you rather have attribute X2 pushed from xo

2 to x*2 than X3 pushed from xo3 to x*3 ?

– Yes, k2 > k3



Suppose that we assess k1 = .6, that is, the decision maker is indifferent between (x*1, xo

2, xo3) and the

lottery <(x*1, x*2, x*3), .6, (xo1, xo

2, xo3)>

What is the value of p so that you are indifferent between (xo

1, x*2, xo

3) and

<(xo1, x*

2, x*3), p, (xo

1, xo2, xo

3)>?– If the decision maker's response is .7,

we have k2 = p(k2 + k3) = .28

– Then u(x1, x2, x3) = .6u1(x1) + .28u2(x2) + .12u3(x3)



Generally, if the utility function u(x1, x2, x3) is addi

tive and utility independent, then– u(x1, x2, x3) = k1u1(x1) + k2u2(x2) + k3u3(x3),

where ui(xo

i) = 0, ui(x*

i) = 1, for all i.


Future TrendsFuture Trends

Today's first generation shopping agent – Limited to comparing merchant offerings usually on

price instead of their full range of value

– The negotiation model needs to be extended to include negotiations over the more attributes.

Extensions– Plan on including other factors in the cost of bids, such

as the reliability of the supplier, or the desirability of the customer to deal with a supplier

– Plan on extending the algorithm to include time considerations in addition to price


감사합니다감사합니다 부경대학교 전자컴퓨터정보통신공학부 분산인공지능연구실 , 정 목 동 Email : [email protected]

: [email protected] Homepage : http://ce.pknu.ac.kr Voice : 051-620-6883

Agent-mediated Electronic Commerce 한국정보과학회 ’00 가을 학술발표 Tutorial 2000....

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