Bilateral Negotiation Model for Agent Mediated Electronic Commerce
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Bilateral Negotiation Model for Agent Mediated Electronic Commerce

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Old stuff from my master degree study

Old stuff from my master degree study

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Bilateral Negotiation Model for Agent Mediated Electronic Commerce Presentation Transcript

  • 1. Bilateral Negotiation Model for Agent Mediated Electronic Commerce Gustavo Eliano de Paula Francisco Ramos Geber Lisboa Ramalho Universidade Federal de Pernambuco gep@cin.ufpe.br, fsr@npd.ufpe.br, glr@cin.ufpe.br
  • 2. Current Bilateral Negotiation in E-commerce
    • Limited to:
      • proposals exchanges
      • multiple attributes (e.g., price, delivery time,...)
    • Have you got the new
    • Guns and Roses CD?
    • Yes! It costs US$15 and you can
    • have it in two days only!
    • Could you make it for US$13
    • and delivering in one day?
    • I can deliver within one day only if
    • you pay US$14!
    Virtual Shop
    • Ok!
    Buyer Seller
  • 3. Possible Extension: Product Amonut
    • Usual in real world negotiation
    • Hard to model.
    • Have you got the new
    • Guns and Roses CD?
    • Yes and it costs US$15!
    • Could you make it for US$13?
    • Only if you buy two!
    Virtual Shop Buyer Seller
  • 4. Possible Extensions
    • Product amount
    • Suggestion similar product
      • guns and roses new CD  guns and Roses “Use of Illusion” CD
    • Suggestion of correlated product
      • TV & video cassette
    • Ultimatum
      • this is my last offer!
    • Time cost
      • selling an ice-cream in a sunny day
  • 5. Summary
    • Four Problems of Modeling Bilateral Negotiation
    • State of the Art on Bilateral negotiation
    • Our Negotiation Model
    • Implementation and Results
    • Conclusions and Future Work.
  • 6. Four General Problems of Modeling Bilateral Negotiation
    • How to represent proposals?
      • using single or multiple deal attributes?
        • price, taxes, ...
      • including product attributes?
        • monitor size, processor speed, ...
    • How to evaluate proposals?
      • attributes average
        • (a 1 + a 2 + ... + a n ) / n
      • multi-attributes utility theory
        • (w 1 a 1 + w 2 a 2 + ... + w n a n ) / (w 1 +w 2 +...+w n )
  • 7. Four General Problems of Modeling Bilateral Negotiation
    • Which are the possible moves?
      • accept, reject (and send a counterproposal), quit
      • give an ultimatum, suggest similar product, suggest correlated product, etc.
    • How to choose the adequate move?
      • proposal´s comparison
        • local decision based on current opponent proposal
      • game theory
        • hard to consider complex models
      • heuristic (Knowledge based).
  • 8. State of the Art Kasbah Farantin’s Model Proposal Representation Proposal Evaluation Possible Moves Decision Making Single Attribute (price) Multiple Attributes Price stands for proposal evaluation Weighted attributes combination Proposal comparison Proposal comparison Accept, reject, quit Accept, reject, quit
  • 9. State of the Art: Limitations
    • Kasbah and Faratin’s model
      • proposals model is too simple
      • moves do not consider some possibilities of a client-salesman negotiation
    • Faratin’s model
      • local deal constraints violation
      • local deal degeneration.
  • 10. Our Negotiation Model
    • Richer proposal representation
    • New moves (alternative product suggestion and ultimatum)
    • Decision making consider negotiation costs (e.g., time)
    • Solve Faratin’s local deals problems.
  • 11. Proposals Representation Proposal Deal Attributes Price Delivery Time Delivery Tax Product Attributes Monitor Size Processor Speed Fax-modem Speed CD-ROM Speed
  • 12. Proposals Evaluation
      • Deal attributes are weighted combined:
      • Product attributes are weighted combined:
      • Deal and product evaluations are weighted combined:
    • For a proposal Proposal j = {P i , X j }, where:
      • Pj stands for the product attributes (features) and
      • Xj stands for the deal attributes
  • 13. Protocol and Possible Moves Builds Counter - offer Rejects Offer Suggests Altern . Product Chooses Altern . Product Last Offer Opponent Receives Offer Analyzes Offer Sends Offer Gives up Accepts Offer Builds Initial Offer Sends Last Offer Receives Last Offer Analyzes Offer Rejects Offer Accepts Offer
  • 14. Decision making
    • Knowledge based
      • consider a set of negotiation heuristics;
  • 15. Implementation and Results
    • Implementation
      • 100% pure Java
      • KQML via Jatlite
    • A user can:
      • negotiate by himself
      • delegate negotiation to an autonomous agent and use an avatar to represent him.
    avatar + figura
  • 16. Implementation and Results
  • 17. Implementation and Results
  • 18. Implementation and Results
    • A kind of “Turing test” on different scenarios
      • Human buyer vs. Autonomous seller
      • Human seller vs. Autonomous buyer
      • Human buyer vs. Human seller
    • Goal
      • Check whether our negotiation model simulate human behavior
    • Results
      • Human negotiator can not identify whether its opponent is human or autonomous.
  • 19. Conclusions and Future Work
    • Contributions: extension to Faratin’s model
      • richer proposal representation
      • new moves (alternative product suggestion and ultimatum)
      • decision making considers negotiation costs
      • solves local deals problems;
    • Future
      • Features: correlated products, product quantity
      • Learning: same product, same negotiator
      • Funded by IBM to be integrated to WebSphere - Net Commerce