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Pervasive Computing Research Group, Department of Informatics and Telecommunications University of Athens, Greece. Buyer Agent Decision Process Based on Automatic Fuzzy Rules Generation Methods. WCCI – FUZZ 2010 Barcelona - Spain. Roi Arapoglou, Kostas Kolomvatsos, Stathes Hadjiefthymiades.

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Buyer agent decision process based on automatic fuzzy rules generation methods

Pervasive Computing Research Group, Department of Informatics and Telecommunications

University of Athens, Greece

Buyer Agent Decision Process Based on Automatic Fuzzy Rules Generation Methods

WCCI – FUZZ 2010

Barcelona - Spain

Roi Arapoglou, Kostas Kolomvatsos, Stathes Hadjiefthymiades


Outline
Outline Informatics and Telecommunications

  • Introduction

  • Market Members – Scenario

  • Buyer Behavior – Decision Process

  • Buyer Fuzzy Logic System

  • Fuzzy Rules Generation

  • Results


Introduction
Introduction Informatics and Telecommunications

  • Intelligent Agents

    • Autonomous software components

    • Represent users

    • Learn from their owners

  • Electronic Markets

    • Places where entities not known in advance can negotiate for the exchange of products

  • Fuzzy Logic

    • Algebra based on fuzzy sets

    • Deals with incomplete or uncertain information

    • Enhance the knowledge base of agents


Market members scenario
Market Members - Scenario Informatics and Telecommunications

  • Buyers

  • Sellers

  • Middle entities (matchmakers, brokers, market entities)

    Intelligent Agents may represent each of these entities

  • Scenario

    • Modeled as a finite-horizon Bargaining Game

    • No knowledge about the characteristics of the opponent (i.e., the other side) is available


Buyer behavior decision process 1 2
Buyer Behavior – Decision process (1/2) Informatics and Telecommunications

  • The buyer stays in the game for a specific number of rounds

  • Profit

    • A Utility Function is used

    • , where V is the buyer valuation and p is the product price

    • The smaller the price is the greater the profit becomes

  • Pricing Function

    , where p0 is an initial price, V is the valuation, x is the number of the proposal, Tb is the deadline and k is a policy factor (k>1:patient, k<1:aggressive, k=1:neutral)


Buyer behavior decision process 2 2
Buyer Behavior – Decision process (2/2) Informatics and Telecommunications

  • Receives proposals and accepts or rejects them making its own proposals

  • Utilizes a reasoning mechanism based on FL

  • The mechanism results the value of the Acceptance Degree (AD)

  • The reasoning mechanism is based on the following parameters:

    • Relevance factor (r)

    • Price difference (d)

    • Belief about the expiration of the game (b)

    • Time difference (t)

    • Valuation (V)


Buyer fuzzy logic system 1 2
Buyer Fuzzy Logic System (1/2) Informatics and Telecommunications

  • Architecture

  • Contains a set of Fuzzy rules

  • Rules are automatically generated based on experts dataset


Buyer fuzzy logic system 2 2
Buyer Fuzzy Logic System (2/2) Informatics and Telecommunications

  • Advantages of the automatic Fuzzy rules generation

    • Mainly, it does not require a lot of time in the developer side

    • It does not require experience in FL rules definition

    • It uses simple numbers representing values of basic parameters

    • Fuzzy rules are automatically tuned


Fuzzy rules generation 1 2
Fuzzy Rules Generation (1/2) Informatics and Telecommunications

  • Clustering techniques are used

  • Algorithms:

    • K-means

    • Fuzzy C-means (FCM)

    • Subtractive clustering

    • Nearest Neighborhood Clustering (NNC)

  • Every cluster corresponds to a Fuzzy rule

  • Example

    If is a cluster center the rule is:


Fuzzy rules generation 2 2
Fuzzy Rules Generation (2/2) Informatics and Telecommunications

  • Additional techniques

    • Learning from Examples (LFE)

    • Modified Learning from Examples (MLFE)

  • Templates for membership functions are defined

  • Dataset

    • They describe the policy that the buyer should have, concernig the acceptance of a proposal

    • 108 rows of data

    • Each row contains data for r, d, b, t, and V


Results 1 3
Results (1/3) Informatics and Telecommunications

  • Fuzzy rule base creation time

  • Usage of the generated Fuzzy rule base in a BG

    • We use the following parameters

    • We examine the Joint Utility in seven agreement zones (theoretic maximum equal to 0.25)

      , (1) where P* is the agreement price, C is the seller cost and V is the buyer valuation

[1] MU = Monetary Unit

(1)D. Zeng & K. Sycara, ‘Bayesian Learning in Negotiation’, International Journal of Human-Computer Studies, vol(48), no 1, 1998, pp. 125-141.


Results 2 3
Results (2/3) Informatics and Telecommunications

  • Agreement zones

  • Numerical results


Results 3 3
Results (3/3) Informatics and Telecommunications

  • Performance of algorithms in the BG


Buyer agent decision process based on automatic fuzzy rules generation methods

Thank you! Informatics and Telecommunications

http://p-comp.di.uoa.gr