Fuzzy cognitive maps for dynamic grid service negotiation

The grid is moving from the scientific grid to a pervasive and economic/business grid. Service trading, in which service provider and service consumer negotiate for a mutually acceptable agreement on multi-issues such as service performance, access cost etc., is one of the most important components in building the Economic Grid. In view of Pervasive Grid, a new challenging issue is that participants on pervasive devices usually have limited computational capacity. And it is also desirable that a multi-issue negotiation agreement can be reached as quickly as possible since the wireless communication to exchange the offers is generally unreliable and power-consuming. Hence, an agile, automated, but lightweight multi-issue decision-making model is needed to facilitate service negotiation in Pervasive Grid. More over, existing methods for multi-issue negotiation only regard each issue as a separate issue, though in most of cases, there exist causal relationships between these negotiation issues. In this paper, a decision-making model based on Fuzzy Cognitive Map (FCM) theory is proposed for multi-issue negotiation which takes into account the causal relationships between the negotiation issues. In the proposed model, the causal relationships between the negotiation issues are well represented by FCMs. The service trading is modeled as a dynamic system with interdependent relationships among negotiation issues. The example and experimental results show that the proposed model is lightweight and promising to be employed in Pervasive Grid for multi-issue service negotiations.

[1]  Nicholas R. Jennings,et al.  A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments , 2003, Artif. Intell..

[2]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[3]  Nicholas R. Jennings,et al.  An agenda-based framework for multi-issue negotiation , 2004, Artif. Intell..

[4]  Monique Calisti,et al.  Constraint Satisfaction Techniques for Negotiating Agents , 2002 .

[5]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[6]  Rayford B. Vaughn,et al.  Fuzzy cognitive maps for decision support in an intelligent intrusion detection system , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[7]  W. Stach.  Modeling Software Development Projects Using Fuzzy Cognitive Maps , 2004 .

[8]  Nemo Semret,et al.  Auctions for Network Resource Sharing , 1997 .

[9]  Nicholas R. Jennings,et al.  Multi-issue negotiation under time constraints , 2002, AAMAS '02.

[10]  Michael Wooldridge,et al.  Introduction to multiagent systems , 2001 .

[11]  Elias N. Houstis,et al.  Towards a Pervasive Grid , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[12]  Rajkumar Buyya,et al.  Economic-based Distributed Resource Management and Scheduling for Grid Computing , 2002, ArXiv.

[13]  Luc Moreau,et al.  Agents for the Grid: A Comparison with Web Services (Part I: Transport Layer) , 2002, 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02).

[14]  Weiming Shen,et al.  Adaptive Negotiation for Agent-Based Grid Computing , 2002 .

[15]  Dong Mei Zhang,et al.  A Web-Based Negotiation Agent Using CBR , 2000, PRICAI Workshops.

[16]  Katia P. Sycara,et al.  Bayesian learning in negotiation , 1998, Int. J. Hum. Comput. Stud..

[17]  Zhi-Qiang Liu,et al.  On causal inference in fuzzy cognitive maps , 2000, IEEE Trans. Fuzzy Syst..

[18]  Nicholas R. Jennings,et al.  A Fuzzy-Logic Based Bidding Strategy for Autonomous Agents in Continuous Double Auctions , 2003, IEEE Trans. Knowl. Data Eng..

[19]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[20]  Katia P. Sycara,et al.  Benefits of Learning in Negotiation , 1997, AAAI/IAAI.

[21]  Chrysostomos D. Stylios,et al.  Fuzzy Cognitive Maps in modeling supervisory control systems , 2000, J. Intell. Fuzzy Syst..

[22]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[23]  B. Kosko.  Fuzzy Thinking: The New Science of Fuzzy Logic , 1993 .

[24]  Liu Xin.  A Service-oriented Negotiation Model Between Autonomous Agents , 2003 .

[25]  Nicholas R. Jennings,et al.  Brain Meets Brawn: Why Grid and Agents Need Each Other , 2004, Towards the Learning Grid.

[26]  Robert H. Crites,et al.  Multiagent reinforcement learning in the Iterated Prisoner's Dilemma. , 1996, Bio Systems.

[27]  Eduardo Alonso Fernández,et al.  Rules of encounter: designing conventions for automated negotiation among computers , 1995 .

[28]  Chrysostomos D. Stylios,et al.  Modeling complex systems using fuzzy cognitive maps , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[29]  Svetha Venkatesh,et al.  Learning other Agents' Preferences in Multiagent Negotiation , 1996, AAAI/IAAI, Vol. 1.

[30]  Ryszard Kowalczyk,et al.  On Constraint-Based Reasoning in e-Negotiation Agents , 2000, AMEC.

[31]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[32]  Chrysostomos D. Stylios,et al.  An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps , 2003, IEEE Transactions on Biomedical Engineering.

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

[34]  Zhengding Lu,et al.  Apply Market Mechanism to Agent-Based Grid Resource Management , 2003, Int. J. Softw. Eng. Knowl. Eng..

[35]  Simon Miles,et al.  Agents for the Grid: A Comparison with Web Services (part II: Service Discovery) , 2002 .

[36]  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 .

[37]  Luc Moreau,et al.  Issues in Building Agent based Computational Grids , 2000 .

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

[39]  Nicholas R. Jennings,et al.  Automating negotiation for M-services , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[40]  David Abramson,et al.  An Economy Driven Resource Management Architecture for Global Computational Power Grids , 2000, PDPTA.

[41]  Shigeo Abe,et al.  Neural Networks and Fuzzy Systems , 1996, Springer US.

[42]  Jim Cunningham,et al.  Specifying the Processes and States of Negotiation , 2001, AgentLink.

[43]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1988, IJCAI 1989.

[44]  Tuomas Sandholm,et al.  On Multiagent Q-Learning in a Semi-Competitive Domain , 1995, Adaption and Learning in Multi-Agent Systems.

[45]  Bart Kosko,et al.  Virtual Worlds as Fuzzy Cognitive Maps , 1993, Presence: Teleoperators & Virtual Environments.

[46]  Michael Luck,et al.  Automated Negotiation for Grid Notification Services , 2003, Euro-Par.

[47]  Kuo-Ming Chao,et al.  Negotiating Agents in a Market-Oriented Grid , 2002, 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02).

[48]  Nicholas R. Jennings,et al.  Designing a successful trading agent:A fuzzy set approach , 2004, IEEE Transactions on Fuzzy Systems.

[49]  Michael Stonebraker,et al.  An economic paradigm for query processing and data migration in Mariposa , 1994, Proceedings of 3rd International Conference on Parallel and Distributed Information Systems.

[50]  Nicholas R. Jennings,et al.  Designing a successful trading agent using fuzzy techniques , 2004 .

[51]  Enrico H. Gerding,et al.  Scientific approaches and techniques for negotiation. A game theoretic and artificial intelligence perspective , 2000 .