25/11/2005Context-Aware Negotiation in E-commerce 1 Reyhan AYDOĞAN

25/11/2005Context-Aware Negotiation in E-commerce 2 OUTLINE Introduction Matchmaking Negotiation Proposed Negotiation Scheme Architecture Learning Discussion

25/11/2005Context-Aware Negotiation in E-commerce 3 Introduction Agents for flexible e-commerce applications Two agent roles: –Producer: Advertise and provide service –Consumer: Request and possibly accept the service Service can be –Reserving a room –Selling a car, and so on.

25/11/2005Context-Aware Negotiation in E-commerce 4 Matchmaking Comparison of advertisement description of the producers with the service description requested by the consumer Matchmaking degrees: [Li et al, 2003] –Exact : If A and R are equal description A≡ R –Plug-in: If R is sub-description of A R≤A –Subsume: If R is super-description of A A≤R –Intersection: If Intersection of R and A is satisfiable ¬ (A ∩ R ≤ ) –Disjoint: Otherwise A ∩ R ≤

25/11/2005Context-Aware Negotiation in E-commerce 5 Matchmaking Cont. R = request S= service provided m= # of missing properties Taken from [] Taken from [ Broens, 2004]

25/11/2005Context-Aware Negotiation in E-commerce 6 Negotiation When no exact matched service, negotiation starts Negotiation mechanism [ Debenham, 2002] –Single issue negotiation Auction ( i.e. Vickrey Auction) –One-to-one negotiation (bargaining) Alternating offers mechanism Single-round, “one-hit” mechanism

25/11/2005Context-Aware Negotiation in E-commerce 7 Proposed Negotiation Scheme Not based on single issue like “price” Based on actual service description –Multiple attributes such as delivery time, price, other features of outputs, and so on Uses the terminology, “Ontology” Uses the context information Considers preferences Offers alternatives Learns by time

25/11/2005Context-Aware Negotiation in E-commerce 8 Ontology Common understanding of knowledge concerning the domain of interest [Fensel, 2003] Describe concepts and specify properties of concepts Establish relationships among concepts E.g. Car ontology –Car is a concept. –Price, color, brand, model are some properties of car concept. –Vehicle is another concept. –Car is a type-of vehicle or Car is-a vehicle.

25/11/2005Context-Aware Negotiation in E-commerce 9 Service SERVICE Service Type Output (s)Input (s)Attribute (s) Context Information Selling Rental ….. The required attributes Credit card no Date information … Crème Car ….. Color Brand Model …. Age Location ….

25/11/2005Context-Aware Negotiation in E-commerce 10 Context Information Enables to provide better service to consumer agents Related with the products and customer information E.g. –Special Beauty Crème requires age information. –Location information may be used.

25/11/2005Context-Aware Negotiation in E-commerce 11 Preferences Consumer’s preference –E.g. Which one is more important for consumer? Price versus delivery time? Color versus brand ? – Can be specified as a number at range [0-1] –Known or learned by time? Producer’s preference –Business Policy

25/11/2005Context-Aware Negotiation in E-commerce 12 Generating Alternatives From feature vector –Generate by combination of the attribute values –E.g. Easily estimated similarity function Effects of weighted sum of preferences

25/11/2005Context-Aware Negotiation in E-commerce 13 Generating Alternatives Cont. From taxonomy by using relationships like parent-child, is-a and kind-of relationship Taken from [Udupi, at all, 2006]

25/11/2005Context-Aware Negotiation in E-commerce 14 Negotiation Architecture Consumer Agent …………… ? Producer Agent ? SHARED ONTOLOGY Knowledge Repository …………… 1- Request 2-Evaluate Request and Learning 3-Provide Service or Offer alternative 4-Evaluate the offer 5-Accept or Re-request N-negotiate and provide service … … …

25/11/2005Context-Aware Negotiation in E-commerce 15 Evaluation of a request If there are any prerequisites for the service –If the information coming from consumer agent is not compatible with the prerequisites of the service Offer a suitable service which is compatible with the consumer’s context information Check whether there is a service which exactly matches with the request Service type, output, input, features –If exists, offer the service –Otherwise, offer an alternative service

25/11/2005Context-Aware Negotiation in E-commerce 16 Matching Taken from [ Broens, 2004]

25/11/2005Context-Aware Negotiation in E-commerce 17 Offer Alternative Which offer will be first? A utility function which based on both producer’s and customer’s preferences –A weighted sum of preferences with the similarity value of the services –Estimate similarity of the feature vector of the service with the request Hamming Distance or Manhattan Distance

25/11/2005Context-Aware Negotiation in E-commerce 18 Offer Alternative Cont. What are the producer preferences? –If two products have the same functionality The expiration date? The number of product affect the preference? –Consider Business Strategies Customer preferences may not be known –Learn during the interaction Version Space Default Logic Learned preferences will affect the order of the alternatives

25/11/2005Context-Aware Negotiation in E-commerce 19 Inductive Learning The goal of the consumer agent is not stable The system should learn the best behavior Inductive learning includes learning from example –Positive examples: Request of consumer agent –Negative examples: Counter offer not accepted Version space

25/11/2005Context-Aware Negotiation in E-commerce 20 Version Space The goal : Obtain a single description Includes: Generalization of specific concept description Specialization of general concept description [REF: web1]

25/11/2005Context-Aware Negotiation in E-commerce 21 Version Space Cont. Taken from [Mitchell,1982 ]

25/11/2005Context-Aware Negotiation in E-commerce 22 Version Space Cont. Taken from [Mitchell,1982 ]

25/11/2005Context-Aware Negotiation in E-commerce 23 Candidate Eliminating Algorithm Initialize the G –with the all variables Initialize the S –with the first positive example Repeat –If positive example then Remove descriptions from G do not cover this example Generalize the S sets so as to cover this example –Otherwise, Remove descriptions from S cover this example Specialize the G sets so as to do not cover this example Until G and S are both singleton samples [REF: web2]

25/11/2005Context-Aware Negotiation in E-commerce 24 Default Reasoning Default theory T, (W,D) where – W is a set of predicate logic (axioms or facts) – D is a set of defaults E.g. “In the absence of evidence to the contrary assume that the accused is innocent” accused (X) : innocent (X) innocent (X) prerequisite justification conclusion

25/11/2005Context-Aware Negotiation in E-commerce 25 Default Reasoning Cont. If we know the prerequisite and it is consistent to current knowledge base, we can make conclusion. [Antoniou, 1997] T ( W, D) where W={green, aaaMember} D={S1,S2} S1= green: ¬likesCar S2=aaaMember: likesCar ¬likesCar likesCar Extension: –Draw more conclusion True : creditworthy True : ¬ creditworthy approveCredit ¬ creditworthy

25/11/2005Context-Aware Negotiation in E-commerce 26 Discussion Time issue, finalization condition of negotiation process –How time affect the negotiation phase Number of interaction is limited Learn as quickly as possible –Many attributes slows down the learning Decide business policies for producer agent

25/11/2005Context-Aware Negotiation in E-commerce 27 References Udupi, Y.B. and Singh, M.P., “Multidimensional Service Matching and Selection ”,AAMAS’ 06, May 8-12, Japan, 2006 Broens,T. Context-aware, Ontology based, Semantic Service Discovery (2004). Master thesis, University of Twente, the Netherlands USA. Fensel D., J. Hendler, H. Lieberman and W. Wahlster. Spinning the Semantic Web. The MIT Press, Cambridge, Massachusetts, London, England Lei Li and Ian Horrocks. A software framework for matchmaking based on Semantic web technology. In Proceedings of the Twelfth International World Wide Web Conference (WWW 2003), 2003

25/11/2005Context-Aware Negotiation in E-commerce 28 References Cont. J.K. Debenham. ‘Managing e-Market Negotiation in Context with a Multiagent System’. In: ProceedingsTwenty First InternationalConference on Knowledge Based Systems and Applied Artificial Intelligence, ES’2002: Applications and Innovations in Expert Systems X, Cambridge UK, December Antoniou, G Nonmonotonic Reasoning. MIT Press, Cambridge, Massachusetts, London,England Mitchell, TM. Generalization as search. Artificial Intelligence, 18: , 1982 [ Ref: web1] fa/Materials/2004/8 version-space 4up.pdf [Ref: web2]