25/11/2005Context-Aware Negotiation in E-commerce 1 Reyhan AYDOĞAN [email protected].

25/11/2005 Context-Aware Negotiation in E-commerce

1

Context-Aware Negotiation in E-commerce

Reyhan AYDOĞAN

[email protected]


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OUTLINE

• Introduction

• Matchmaking

• Negotiation

• Proposed Negotiation Scheme

• Architecture

• Learning

• Discussion


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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.


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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 ≤


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Matchmaking Cont.

R = request S= service provided

m= # of missing properties

Taken from []

Taken from [ Broens, 2004]


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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


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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


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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.


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Service

SERVICE

Service Type

Output (s)Input (s) Attribute (s)

Context Information

Selling Rental

…..

The required attributesCredit card no

Date information …

Crème Car…..

Color BrandModel

….

AgeLocation

….


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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.


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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


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Generating Alternatives

• From feature vector <attr1,attr2,…attrN>– Generate by combination of the attribute values– E.g. <Color, Brand, Model, PriceRange>

• <silver, “ Canon”, “IXUS-5.0”, “$[350-400]”>

• <blue, “ Canon”, “IXUS-5.0”, “$[350-400]”>

• <silver, “ Nikon”, “Coolpix 5900”, “$[250-350]”>

• <silver, “ Canon”, “Coolpix S2”, “$[350-400]”>

• Easily estimated similarity function

• Effects of weighted sum of preferences


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Generating Alternatives Cont.

• From taxonomy by using relationships like parent-child, is-a and kind-of relationship

Taken from [Udupi, at all, 2006]


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Negotiation Architecture

Consumer Agent

<Preferences><price v=low/><speed v=high/>……………</Preferences>

?

Producer Agent

?

SHAREDONTOLOGY

KnowledgeRepository

<Preferences><price v=high><profit v=high/>……………</Preferences>

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

… … …


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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


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Matching

Taken from [ Broens, 2004]


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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


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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


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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


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Version Space

• The goal : Obtain a single description

• Includes:

• Generalization of specific concept description

• Specialization of general concept description

[REF: web1]


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Version Space Cont.

Taken from [Mitchell ,1982 ]


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Version Space Cont.

Taken from [Mitchell ,1982 ]Taken from [Mitchell ,1982 ]


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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]


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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


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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


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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


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References

Udupi, Y.B. and Singh, M.P. , “Multidimensional Service Matching andSelection ”,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 SemanticWeb. The MIT Press, Cambridge, Massachusetts, London, England. 2003.

Lei Li and Ian Horrocks. A software framework for matchmaking based onSemantic web technology. In Proceedings of the Twelfth International WorldWide Web Conference (WWW 2003), 2003


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References Cont.J.K. Debenham. ‘Managing e-Market Negotiation in Context with a MultiagentSystem’. In: ProceedingsTwenty First InternationalConference on Knowledge BasedSystems and Applied Artificial Intelligence, ES’2002: Applications and Innovations inExpert Systems X, Cambridge UK, December 2002.

Antoniou, G. 1997. Nonmonotonic Reasoning. MIT Press, Cambridge, Massachusetts,London,England. 1997

Mitchell, TM. Generalization as search. Artificial Intelligence, 18:203--226, 1982

[Ref: web1] http://www.cs.cornell.edu/courses/CS472/ 2004fa/Materials/2004/8version-space 4up.pdf

[Ref: web2]http://www.cs.cf.ac.uk/Dave/AI2/node146.html#SECTION000161200000000000000

http://www.cs.cf.ac.uk/Dave/AI2/node146.html#SECTION000161200000000000000

25/11/2005Context-Aware Negotiation in E-commerce 1 Reyhan AYDOĞAN [email protected].

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