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

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

3. Introduction 3  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 4  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) 5  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 p 0 is an initial price, V is the valuation, x is the number of the proposal, T b 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) 6

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

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 8

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: 9

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 10

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 11 AlgorithmRule Base creation time (ms) Subtractive35 FCM2560 K-Means25 LFE20 MLFE25 NNC20 Buyer ParametersSeller Parameters Initial Price100 MUs [1] Cost250 MUs Valuation255 MUsInitial Profit250 MUs [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 12 Buyer ValuationAgreement Zone 255 MUs5 MUs 260 MUs10 MUs 270 MUs20 MUs 300 MUs50 MUs 500 MUs250 MUs 700 MUs450 MUs 1000 MUs750 MUs Scenario No Agreement Zone Average JUMaximum JUAlgorithm 15 MUs0.080.24FCM, K-Means 210 MUs0.140.24FCM, K-Means 320 MUs0.160.21LFE 450 MUs0.240.247FCM, K-Means 5250 MUs0.2380.24MLFE 6450 MUs0.2080.21 MLFE 7750 MUs0.170.172MLFE

13. Results (3/3)  Performance of algorithms in the BG 13 AlgorithmAgreements PercentageAverage JU Subtractive92%0.217 FCM69%0.219 K-Means69%0.202 LFE57%0.223 MLFE85%0.244 NNC86%0.244 AlgorithmAverage AD Value Subtractive80.96 FCM68.91 K-Means62.84 LFE72.65 MLFE74.52 NNC76.58

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