95Accesses
6Citations
Abstract
In today’s hyper-competitive business environments virtual organisations are becoming highly dynamic and unpredictable. Individuals may want to work together across organisation boundaries but do not have much prior knowledge about potential partners. The semantic web and its associated new standards appear very promising as candidates to support a new generation of virtual organisations. Whilst knowledge can be represented in a machine interpretable way, social-like behaviours can be expected in a virtual organisation. In this paper ontology definition techniques from the semantic web are applied to define a virtual state space of a virtual organisation. Actors involved in an organisation, from high level strategy making members to low level physical devices, advertise their skills and local knowledge in a community. A task initiator, with a virtual sensor to perceive the advertised skills and with an adaptive belief model about the community, seeks for the best matched partners for cooperation. The belief model is a fuzzy neural network based on Adaptive Resonance Theory which takes the advertisements of actors as its initial belief and learns actors’ actual capabilities through interaction experience. Dynamic alliances can then take place in an automated/semi-automated way that exhibit adaptive ability, self-organisation, unsupervised learning and competition ability. The alliances thus exhibit the inherent characteristics of realistic enterprises or human societies.
This is a preview of subscription content,log in via an institution to check access.
Access this article
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
Price includes VAT (Japan)
Instant access to the full article PDF.
Similar content being viewed by others
References
Aerts A.T.M., Szirbik N.B., Goossenaerts J.B.M. (2002). A flexible, agent-based ICT architecture for virtual enterprises. Computers in Industry 49: 311–327
Akoka J., Wattiau I.C. (1996). Entity-relationship and object-oriented model automatic clustering. Data & Knowledge Engineering 20: 87–117
Avancha, S., Joshi, A., & Finin, T. (2002). Enhanced service discovery in Bluetooth.Communications, 96–99.
Baltrusch, R. (2001). Exploring organisational learning in virtual forms of organization. InProceedings of the 34th Hawaii international conference on system sciences, Vol. 4, pp. 4029–4038.
Bartfai G. (1996). An ART-based modular architecture for learning hierarchical clusterings. Neurocomputing 13: 31–45
Camarinha-Matos L.M., Afsarmanesh H. (2003). Elements of a base VE infrastructure. Computers in Industry 51: 139–163
Carpenter G.A., Grossberg S., Rosen D.B. (1991). Fuzzy ART: Fast stable learning and categorization of analog pattern by an adaptive resonance system. Neural Networks 4: 759–771
Colucci S., Noia T.D., Sciascio E.D., Donini F.M., Mongiello M. (2005). Concept abduction and contraction for semantic-based discovery of matches and negotiation spaces in an e-marketplace. Electronic Commerce Research and Applications 4: 345–361
Goldman C.V., Rosenschein J.S. (2002). Evolutionary patterns of agent organizations. IEEE Transactions on Systems, Man, and Cybernetics Part A 32: 135–148
Hishiyama, R., & Ishida, T. (2005). Modeling e-procurement as co-adaptive matchmaking with mutual relevance feedback. In: M. W. Barley & N. Kasabov (Eds.),PRIMA 2004, LNAI 3371 (pp. 67–80). Springer.
Huang J., Georgiopoulos M., Heileman G.L. (1995). Fuzzy ART properties. Neural Networks 8: 203–213
Ioerger, T. R. (2004). Reasoning about beliefs, observability, and information exchange in teamwork. In17th International conference of the Florida Artificial Intelligence Research Society (FLAIRS’04).
ISO (1994). Application protocol: Configuration controlled design, IS 10303—Part 203.
Jeng, J. J., & Cheng, B. H. C. (1995). Specification matching for software reuse.ACM SIGSOFT Software Engineering Notes, 20.
Jiang P., Mair Q., Newman J. (2006). The application of UML to the design of processes supporting product configuration management. International Journal of Computer Integrated Manufacturing 19: 393–407
Jiang, P., Peng, Y., Mair, Q., & Yuan, M. (2005). A variable-resolution virtual sensor in social behaviour networks. In H. Czap (Ed.),Self-organization and automatic informatics(I) (pp. 86–94). ISO Press.
Kopena, J., & Regli, W. C. (2003). DAMLJessKB: A tool for reasoning with the semantic web.IEEE Intelligent Systems, 74–77.
Kuokka D., Harada L. (1996). Integrating information via matchmaking. Intelligent Information Systems 6: 261–279
Kurbel K., Loutchko I. (2005). A model for multi-lateral negotiations on an agent-based marketplace for personnel acquisition. Electronic Commerce Research and Applications 4: 187–203
Lee J., Liu K.F.R., Wang Y.C., Chiang W. (2004). Possibilistic Petri nets as a basis for agent service description language. Fuzzy Sets and Systems 144: 105–126
Liberatore P. (2000). The complexity of belief update. Artificial Intelligence 119: 141–190
Liu H., Petrovic M., Jacobsen H. (2005). Efficient and scalable filtering of graph-based metadata. Journal of Web Semantics 3: 294–310
Ludwig S.A., Reyhani S.M.S. (2005). Introduction of semantic matchmaking to grid computing. Journal of Parallel and Distributed Computing 65: 1533–1541
Ludwig, S. A., Naylor, W., Padget, J., & Rana, O. F. (2005). Matchmaking support for mathematical web services.Proceedings of the UK e-science all hands meeting, Nottingham UK.
Ludwig S.A., Reyhani S.M.S. (2006). Semantic approach to service discovery in a Grid environment. Journal of Web Semantics 4: 1–13.
Martinez M.T., Park K.H., Favrel J. (2001). Virtual enterprise: Organisation, evolution and control. International Journal of Production Economics 74: 225–238
Miao C.Y., Goh A., Miao Y., Yang Z.H. (2002). Agent that models, reasons and makes decisions. Knowledge-Based Systems 15: 302–211
Missikoff, M., & Taglino, F. (2004). An ontology-based platform for semantic interoperability.Handbook on ontologies (pp. 617–634). Springer.
Noia, T. D., Sciascio, E. D., Donini, F. M., & Mongiello, M. (2003). A system for principled matchmaking in an electronic marketplace. InThe twelfth international world wide web conference, Budapest, Hungary.
Norman T.J., Preece A., et al. (2004). Agent-based formation of virtual organizations. Knowledge-Based Systems 17: 103–111
Russell, S., & Norvig, P. (2003).Artificial intelligence: A modern approach (2nd ed.). Upper Saddle River, NJ: Prentice Hall.
Saridis G.N. (1989). Analytical formulation of the principle of increasing precision with decreasing intelligence for intelligent machines. Automatica 25: 461–467
Shoham, Y. (1991). AGENT-0: A simple agent language and its interpreter.Proceedings of the ninth national conference on artificial intelligence, Anaheim, CA, Vol. 2, pp. 704–709.
Shoham Y. (1993). Agent-oriented programming. Artificial Intelligence 60: 51–92
Subbu R., Sanderson A.C. (2004). Network-based distributed planning using coevolutionary agents: Architecture and evaluation. IEEE Transactions on Systems, Man, and Cybernetics Part A 34: 257–269
Sycara K., Klusch M., Widoff S., Lu J. (1999). Dynamic service matchmaking among agents in open information environments. ACM SIGMOD Record 28: 47–53
Tektonidis, D., Bokma, A., Oatley, G., & Salampasis, M. (2005). Onar: An ontologies-based service oriented application integration framework.Interoperability of enterprise software and applications, Geneva, Switzerland.
Trastour D., Bartolini C., Preist C. (2003). Semantic web support for the business-to-business e-commerce pre-contractual lifecycle. Computer Networks 42: 661–673
Unland R., Kirn S., Wanka U., Hare G., Abbas S. (1995). Aegis: Agent oriented organizations. Accting Mgmt & Info Tech 5: 139–162
Williams J., Steele N. (2002). Difference, distance and similarity as a basis for fuzzy decision support based on prototypical decision classes. Fuzzy Sets and Systems 131: 35–46
Wooldridge, M. J., & Jennings, N. R. (Eds.) (1995).Intelligent agents: ECAI-94 workshop on agent theories, architectures, and languages. Berlin: Springer-Verlag.
Yuan, M., Jiang, P., & Newman, J. (2005). An energy-driven social behaviour network architecture. In H. Czap, et al. (Eds.),Self-organization and automatic informatics(I) (pp. 77–85). ISO Press.
Zadeh L.A. (1965). Fuzzy sets. Information and Control 8: 338–353
Author information
Authors and Affiliations
Department of Computing, University of Bradford, Bradford, BD7 1DP, UK
Ping Jiang
Department of Computing, Glasgow Caledonian University, Glasgow, G4 0BA, UK
Quentin Mair
Systems Engineering Institute, Xi’an Jiaotong University, Xi’an, 710049, China
Zu-Ren Feng
- Ping Jiang
You can also search for this author inPubMed Google Scholar
- Quentin Mair
You can also search for this author inPubMed Google Scholar
- Zu-Ren Feng
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toPing Jiang.
Rights and permissions
About this article
Cite this article
Jiang, P., Mair, Q. & Feng, ZR. Agent alliance formation using ART-networks as agent belief models.J Intell Manuf18, 433–448 (2007). https://doi.org/10.1007/s10845-007-0032-x
Published:
Issue Date:
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative