Evolving Fuzzy Rules for Relaxed-Criteria Negotiation

In the literature on automated negotiation, very few negotiation agents are designed with the flexibility to slightly relax their negotiation criteria to reach a consensus more rapidly and with more certainty. Furthermore, these relaxed-criteria negotiation agents were not equipped with the ability to enhance their performance by learning and evolving their relaxed-criteria negotiation rules. The impetus of this work is designing market-driven negotiation agents (MDAs) that not only have the flexibility of relaxing bargaining criteria using fuzzy rules, but can also evolve their structures by learning new relaxed-criteria fuzzy rules to improve their negotiation outcomes as they participate in negotiations in more e-markets. To this end, an evolutionary algorithm for adapting and evolving relaxed-criteria fuzzy rules was developed. Implementing the idea in a testbed, two kinds of experiments for evaluating and comparing EvEMDAs (MDAs with relaxed-criteria rules that are evolved using the evolutionary algorithm) and EMDAs (MDAs with relaxed-criteria rules that are manually constructed) were carried out through stochastic simulations. Empirical results show that: 1) EvEMDAs generally outperformed EMDAs in different types of e-markets and 2) the negotiation outcomes of EvEMDAs generally improved as they negotiated in more e-markets.

[1]  Ayman M. Wasfy,et al.  Two-Party Negotiation Modeling: An Integrated Fuzzy Logic Approach , 1998 .

[2]  Ryszard Kowalczyk,et al.  Fuzzy e-negotiation agents , 2002, Soft Comput..

[3]  Fu Wei,et al.  A negotiation model based on fuzzy multiple criteria decision making method , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[4]  Kwang Mong Sim,et al.  Grid Commerce, Market-Driven G-Negotiation, and Grid Resource Management , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Alex Alves Freitas,et al.  Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution , 2001, PKDD.

[6]  K. Robert Lai,et al.  Modeling Agent Negotiation Via Fuzzy Constraints in E‐Business , 2004, Comput. Intell..

[7]  Kwang Mong Sim,et al.  Equilibria, prudent Compromises,and the "Waiting" game , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Ryszard Kowalczyk,et al.  FeNAs: a fuzzy e-negotiation agents system , 2000, Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520).

[9]  R. Inderst.  Bargaining Theory with Applications , 2002 .

[10]  Kwang Mong Sim,et al.  Flexible negotiation agent with relaxed decision rules , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[11]  Laurence Tianruo Yang,et al.  Fuzzy Logic with Engineering Applications , 1999 .

[12]  A. Rubinstein,et al.  Bargaining and Markets , 1991 .

[13]  A. Rubinstein.  Perfect Equilibrium in a Bargaining Model , 1982 .

[14]  Kwang Mong Sim,et al.  Agents that react to changing market situations , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[15]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[16]  Jeffrey S. Rosenschein,et al.  Rules of Encounter - Designing Conventions for Automated Negotiation among Computers , 1994 .

[17]  Xiaojun Shen,et al.  A Fuzzy Logic Based Intelligent Negotiation Agent (FINA) in Ecommerce , 2006, 2006 Canadian Conference on Electrical and Computer Engineering.

[18]  J. Plank,et al.  Grid Resource Allocation and Control Using Computational Economies , 2003 .

[19]  Yan Feng,et al.  A Fuzzy Negotiation Model of e-Commerce and Its Implementation , 2006, 2006 Technology Management for the Global Future - PICMET 2006 Conference.

[20]  A. Michalos.  Rational Behavior and Bargaining Equilibrium in Games and Social Situations , 1982 .

[21]  Tuomas Sandholm,et al.  Bargaining with Deadlines , 1999, AAAI/IAAI.

[22]  Nicholas R. Jennings,et al.  A Classification Scheme for Negotiation in Electronic Commerce , 2001, AgentLink.

[23]  Xin Wang,et al.  A FUZZY LOGIC BASED INTELLIGENT NEGOTIATION AGENT ( FINA ) IN E-COMMERCE , 2006 .

[24]  J. Nash.  THE BARGAINING PROBLEM , 1950, Classics in Game Theory.

[25]  Kwang Mong Sim,et al.  Negotiation Agents that Make Prudent Compromises and are Slightly Flexible in Reaching Consensus , 2004, Comput. Intell..

[26]  K. Robert Lai,et al.  Fuzzy Constraint-Based Agent Negotiation , 2005, Journal of Computer Science and Technology.

[27]  Pattie Maes,et al.  Kasbah: An Agent Marketplace for Buying and Selling Goods , 1996, PAAM.

[28]  A. Rubinstein.  A BARGAINING MODEL WITH INCOMPLETE INFORMATION ABOUT TIME PREFERENCES , 1985 .

[29]  Ryszard Kowalczyk,et al.  On Fuzzy e-Negotiation Agents: autonomous negotiation with incomplete and imprecise information , 2000, Proceedings 11th International Workshop on Database and Expert Systems Applications.

[30]  Kwang Mong Sim,et al.  A relaxed-criteria bargaining protocol for grid resource management , 2006, Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06).

[31]  Kwang Mong Sim,et al.  A Market–Driven Model for Designing Negotiation Agents , 2002, Comput. Intell..

[32]  Dominick Salvatore,et al.  Microeconomics : theory and applications : the modern approach , 1986 .

[33]  Kwang Mong Sim,et al.  A survey of bargaining models for grid resource allocation , 2006, SECO.

[34]  N. R. Jennings,et al.  To appear in: Int Journal of Group Decision and Negotiation GDN2000 Keynote Paper Automated Negotiation: Prospects, Methods and Challenges , 2022 .

[35]  Nicholas R. Jennings,et al.  Negotiation decision functions for autonomous agents , 1998, Robotics Auton. Syst..

[36]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[37]  D. O'Donoghue,et al.  GeneRepair - A Repair Operator for Genetic Algorithms , 2003 .