1. Agent-mediated Electronic Commerce 한국정보과학회 ’00 가을 학술발표 Tutorial 2000. 10. 28 부경대학교 정 목 동

2. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 2 ContentsContents s Introduction s State of the art s Auctions and biddings s Negotiation s Future Trends

3. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 3 IntroductionIntroduction s The combination of EC and intelligent agents –Deliver enormous economic benefits s 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

4. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 4 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

5. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 5 Introduction (cont) s 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

6. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 6 Introduction(cont)Introduction(cont) s Forrester Research estimates –Online retail sales were about $600 million in 1996 –Exceed $2 billion in 1997 –Will reach $17 billion USD in 2001 s People can go to find, buy, and sell goods –BF, OnSale, Amazon –None of these sites has the idea of an autonomous agent s Today’s first generation shopping agent –Limited to comparing merchant offerings only on price

7. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 7 Introduction(cont)Introduction(cont) s Agent mediated electronic commerce –Enables cheap negotiation between buyers and sellers on the details of an individual transaction –Product features, services, financing and price s 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

8. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 8 Introduction(cont)Introduction(cont) s 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

9. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 9 Introduction(cont)Introduction(cont) s Fully autonomous agent –Requires a complete set of preferences in order to represent a user correctly in all situations s Semi-autonomous agent –Bid on behalf of the user when it has enough knowledge –Query the user when its best action is ill-defined

10. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 10 State of the art s 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

11. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 11 State of the art(cont) s 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

12. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 12 State of the art (cont) s 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

13. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 13 State of the art(cont) s 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

14. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 14 State of the art(cont) s 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

15. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 15 State of the art(cont) s 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

16. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 16 State of the art(cont) s PERSUADER : CMU –Integrates Case-Base Reasoning and MAUT to resolve conflicts through negotiation in group problem solving settings s MAUT(Multi-Attribute Utility Theory) –Analyzes decision problems quantitatively through utilities s Constraint Satisfaction Problems(CSPs) –Analyze decision problems more qualitatively through constraints

17. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 17 Auction and Bidding s Auction mechanisms –Discover the optimal price for a good through the bidding action of self-interested agents s Traditional off-line auction –The interested parties gather on a physical location s The infrastructure of EC –Reduces the costs of participation –Allows auctions to reach a large and physically distributed audience

18. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 18 Auction and Bidding(cont) s Ascending price auctions –The auctioneer reveals the highest bid received –The ask price is minimum increment above the price of the current highest bid s Descending price auction –The auctioneer lowers the ask price until the first bid is received

19. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 19 Auction and Bidding(cont) s 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” s Second price auctions –An agent pays the highest amount that was bid by another agent

20. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 20 Auction and Bidding(cont) s In a sealed bid auction –All bids are private –The auctioneer selects the winning bid after a fixed period of time s 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 highest bid in first price sealed bid auctions –few real-world examples

21. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 21 Auction and Bidding(cont) s 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

22. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 22 Auction and Bidding(cont) s 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 s 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

23. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 23 Auction and Bidding(cont) s Which bids to accept using knowledge about –The bids –The task and subtask values –Its own time constraints and the bidder s 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

24. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 24 NegotiationsNegotiations s Two types of negotiations –Distributive negotiation –Integrative negotiation s Typical negotiation –Figure 1: Typical operation model

25. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 25 NegotiationsNegotiations

26. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 26 Negotiations(cont)Negotiations(cont) s 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)

27. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 27 Negotiations(cont)Negotiations(cont) s 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

28. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 28 Kasbah’s CBB model s 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

29. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 29 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

30. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 30 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

31. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 31 MAUTMAUT s 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 x 1 to consequence x 2. –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.

32. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 32 MAUT (cont) Figure 2. A two-attribute consequence space

33. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 33 MAUT(cont)MAUT(cont) s Identifying relevant independence assumptions –Verify whether Y and Z are additive independent and if either attribute is utility independent of the other. s Identifying relevant qualitative characteristics –Whether or not the utility function is monotonic. –If x k is greater than x j, is x k always preferred to x j ? –Whether u is risk averse, risk neutral, or risk prone –Ask the decision maker if he prefers or x

34. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 34 MAUT(cont)MAUT(cont) s Choosing a Utility Function –Utility functions : monotonically increasing in x and decreasingly risk averse u(x) = h + k(-e -ax -be -cx ), where a, b, c, and k are positive constants

35. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 35 MAUT(cont)MAUT(cont) s Ask the decision maker some meaningful qualitative questions about k i 's s Would you rather have attribute X 1 pushed to x * 1 than both X 2 and X 3 pushed to x * 2 and x * 3 ? –A yes answer would imply k 1 > k 2 + k 3, which means k 1 >.5 s Would you rather have attribute X 2 pushed from x o 2 to x* 2 than X 3 pushed from x o 3 to x* 3 ? –Yes, k 2 > k 3

36. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 36 MAUT(cont)MAUT(cont) s Suppose that we assess k 1 =.6, that is, the decision maker is indifferent between (x* 1, x o 2, x o 3 ) and the lottery s What is the value of p so that you are indifferent between (x o 1, x * 2, x o 3 ) and ? –If the decision maker's response is.7, we have k2 = p(k2 + k3) =.28 –Then u(x 1, x 2, x 3 ) =.6u 1 (x 1 ) +.28u 2 (x 2 ) +.12u 3 (x 3 )

37. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 37 MAUT(cont)MAUT(cont) s Generally, if the utility function u(x 1, x 2, x 3 ) is additive and utility independent, then –u(x 1, x 2, x 3 ) = k 1 u 1 (x 1 ) + k 2 u 2 (x 2 ) + k 3 u 3 (x 3 ), where u i (x o i ) = 0, u i (x * i ) = 1, for all i.

38. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 38 Future Trends s 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. s 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

39. 한국정보과학회 '00 가을 학술발표 Tutorial 부경대학교 분산인공지능연구실 39 감사합니다감사합니다  부경대학교 전자컴퓨터정보통신공학부 분산인공지능연구실, 정 목 동 s Email : mdchung@pknu.ac.krmdchung@pknu.ac.kr : mdchung@hanmail.net s Homepage : http://ce.pknu.ac.kr s Voice : 051-620-6883