In the previous three sections, we introduced how to use ontology to describe MAS knowledge and a CPN-based approach to form multi-agent interactions. These two methods are suitable for applications in complex dynamic environments. A potential application of the approach is supply chain formation (SCF) (Walsh, Wellman, & Ygge, 2000). A supply chain is a network that describes interrelated exchange relationships among multiple levels of production. SCF is the process of assembling complex produc-tion and exchange relaproduc-tionships between companies. To adapt to rapidly changing market conditions, companies need automated support for SCF to form and dissolve business interactions dynamically.
Agent technologies are widely applied in automotive supply chain formation. In such applications, the domain knowledge is usually mass and dynamic. Depending on the market conditions, factors such as produce varieties, price, and supply-demand relations are changeable. In this case, using ontologies to describe domain knowledge and including ontology facilitators in the MAS (refer to the section on MAS Ontology
& Knowledge Level Agent Interactions) can bring lots of conveniences for knowledge acquiring.
Another challenge of SCF applications is how to coordinate finite resources and received interaction requests of agents. In SCF applications, agents may receive various interaction requests from other agents. On the other hand, some resources of agents are finite. A firm might be penalized if it accepts infeasible interaction requests. Therefore, the agent of a firm has to analyse received interaction requests and gives proper responses according to current resource availability within the firm. Using the CPN-based approach introduced in the third and fourth sections makes it easier for agents to analyse and dispose various interaction requests. For SCF applications, various kinds of resources and products can be defined as token data types, and a CPN can be used to describe supply-request (SR) relations of a firm. For example, the CPN of Figure 11 shows the SR relations of a firm called Firm-1. In this Figure, places P1 and P2 represent two kinds of products of the firm, R1 and R2 are received requests of P1 and P2, and S1, S2, …, S5 are required resources to produce P1 and P2. At the current stage, Firm-1 accepts a request to produce P1, and S1, S2, and S3 all contain tokens. Therefore, the request can be satisfied. If Firm-1 receives another interaction request at this moment, it will analyse the received interaction protocol and make a decision according to its current status.
Supposing Firm-1 receives the three different protocols in Figure 12, where Protocol-A requests the firm to supply product P2 and promises to requite M1; Protocol-B requests P2, promises to supply S4 and requite M1; and Protocol-C requests P2, promises to supply S3 and S4 and requite M1. According to the current status of Firm-1, Protocol-A is infeasible because of the shortage of resource S4; Protocol-B is also infeasible because resource S3 is occupied by request R1; only Protocol-C is feasible because the requester promises to supply S4 and S3. The above example can also be analysed by using the protocol analysis method introduced in the fourth section.
CONCLUSION
The social ability of an agent is exercised in a multi-agent system. For MASs, predefined agent interaction protocols reduce the flexibility of agent interaction, espe-cially in open environments. In this chapter, we have proposed an approach to enable agents to form knowledge-level interaction protocols flexibly. Furthermore, in this approach, agents can also analyse whether the received protocol is understandable, whether the interaction can be accepted with the current status of the agent, and whether the interaction conflicts with the agents’ objectives. These features make agents able to select or generate suitable protocols to interact with each other under open working environments.
ACKNOWLEDGMENT
This research was supported by an International Linkage Grant from the Australian Research Council (contract number LX0346249).
Figure 11. Resource-production relation example
Figure 12. Product request example
REFERENCES
Bai, Q., & Zhang, M. (2003, December 17-19). An ontology-based multi-agent auction system. Proceedings of the International Conference on Intelligent Technolo-gies, Chiang Mai, Thailand (pp. 197-202).
Bai, Q., & Zhang, M. (2004a, February 16-18). Agent coordination through ontology management. Proceedings of the International Conference on Artificial Intelli-gence and Applications, Innsbruck, Austria (pp. 141-146).
Bai, Q., & Zhang, M. (2004b, July 12-14). SWOAM — A semantic Web-based online auction multi-agent system. Proceedings of the International Conference on Intelligent Agents, Web Technologies, and Internet Commerce, Gold Coast, Australia (pp. 329-336).
Cost, R. (1999, May 1). Modelling agent conversations with coloured petri nets. Working Notes of the Workshop on Specifying and Implementing Conversation Policies, Seattle, WA (pp. 59-66).
Cranefield, S., Purvis, M., Nowostawski, M., & Hwang, P. (2002, July 15). Ontology for interaction protocols. Proceedings of the 2nd International Workshop on Ontolo-gies in Agent Systems — AAMAS’02, Bologna, Italy. Retrieved July 18, 2004, from http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS//Vol-66/oas02-16.pdf
Fensel, D., Harmenlen, F., Horrocks, I., McGuinness, D., & Patel-Schneider, P. (2001). OIL:
An ontology infrastructure for the semantic Web. IEEE Intelligent Systems, 16(2), 38-45.
Finin, T., Labrou, Y., & Mayfield, J. (1997). KQML as an agent communication language.
In J. M. Bradshaw (Ed.), Software agents (pp. 291-316). Cambridge, MA: MIT Press.
Foundation for Intelligent Physical Agents. FIPA ACL Message Representation in String Specification. Retrieved July 18, 2004, from http://www.fipa.org/specs/
fipa00070/
Horrocks, I., & van Harmelen, F. (2002). Reference description of Daml+OIL ontology markup language (Draft Report). Retrieved January 2002, from www.daml.org/
2001/12/reference.html
Jensen, K. (1992). Coloured petri nets — Basic concepts, analysis methods, and practical use. Volume 1: Basic concepts. Berlin, Germany: Springer-Verlag.
Jensen, K. (1998). An introduction to the practical use of colored petri nets. Lectures on Petri Nets II: Applications. Lecture Notes in Computer Science, 1492 (pp. 237-292). Berlin, Germany: Springer-Verlag.
Lesser, V. (1998). Reflections on the nature of multi-agent coordination and its implica-tions for an agent architecture. Autonomous Agents and Multi-agent Systems, 1.1, 89-111.
Lesser, V. (1999). Cooperative multi-agent systems: A personal view of the state of the art. IEEE Transactions on Knowledge and Data Engineering, 11(1), 133-142.
Nowostawski, M., Purvis, M., & Cranefield, S. (2001, May 28-June 1). A layered approach for modelling agent conversations. Proceedings of the 2nd International Workshop on Infrastructure for Agents, MAS, and Scalable MAS, Montreal, Canada (pp. 163-170).
Peterson, J. (1981). Petri net theory and the modeling of systems. Englewood Cliffs, NJ:
Prentice-Hall.
Poutakidis, D., Padgham, L., & Winikoff, M. (2002, July 15-19). Debugging multi-agent systems using design artefacts: The case of interaction protocols. Proceedings of the 1st International Joint Conference on Autonomous Agents and Multi Agent Systems, Bologna, Italy (pp. 960-967).
Rao, A. S., & Georgeff, M. P. (1992, October 25-29). An abstract architecture for rational agents. Proceedings of the 3rd International Conference on Principles of Knowl-edge Representation and Reasoning, San Mateo, CA (pp. 439-449).
Stanford KSL Ontolingua Server. (n.d.). Retrieved from http://www-ksl-svc.stanford.edu:5915/
Walsh, W., Wellman, M., & Ygge, F. (2000, October 17-20). Combinatorial auctions for supply chain formation. Proceedings of the 2nd ACM Conference on Electronic Commerce, Minneapolis, MN (pp. 260-269).