Buyer Agent Decision Process Based on Automatic Fuzzy Rules Generation Methods

1. 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

2. Outline • Introduction • Market Members – Scenario • Buyer Behavior – Decision Process • Buyer Fuzzy Logic System • Fuzzy Rules Generation • Results

3. Introduction • 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

4. Market Members - Scenario • 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

5. Buyer Behavior – Decision process (1/2) • 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)

6. Buyer Behavior – Decision process (2/2) • 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)

7. Buyer Fuzzy Logic System (1/2) • Architecture • Contains a set of Fuzzy rules • Rules are automatically generated based on experts dataset

8. Buyer Fuzzy Logic System (2/2) • 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

9. Fuzzy Rules Generation (1/2) • 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:

10. Fuzzy Rules Generation (2/2) • 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

11. Results (1/3) • 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.

12. Results (2/3) • Agreement zones • Numerical results

13. Results (3/3) • Performance of algorithms in the BG

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