7 - Indira Gandhi Centre for Atomic Research

suit open applications, where a number of possibly a-priori unknown and autonomous
entities have to cooperate. In this case, focusing on dependencies between the components,
as a control-driven model would do, would somehow clash with the autonomy of the
components and the dynamics of the open environment. Focusing on data preserve
autonomy and dynamics of autonomous components, which are usually designed to
acquire information rather than control, as in the case of software agents.
4. Conclusion
Multi-agent concepts and methodologies are finding increasing applications in controlling
complex, unpredictable systems in real time. Agent-like approaches routinely accept
uncertainty and distribution, leading to control schemes where decision-making and
responsibility are distributed much more widely than in conventional engineering practice.
The area of intelligent manufacturing systems aims to use multi-agent systems both as a
modeling tool and a control software system. The challenge for the researchers is to
identify and focus on the general issues of coordination and control, which are important
for such applications, and to use the applications to stimulate and test advances in research
on:
• How and where to represent information about the systems and the agent groups that
operate on them
• Mechanisms for delivery of adequate responses to time-critical demands
• Coordination methods that are computationally economical enough for real-time use
• Decentralized management of limited common resources
• Adaptation of results from machine learning to collective learning in multi-agent
systems
• New schemes of decentralized control to take account of the “real time” dimension
than in conventional engineering practice
References
1. Marilyn, Deegan, & Simon, Tanner. (2002). Digital futures: strategies for the information
age. London: Library Association.
2. David V. Pynadath and Milind Tambe. (2002). Team Coordination among Distributed
Agents:Analyzing Key Teamwork Theories and Models. Chicago,American Association for
Artificial Intelligence.
3. Michael Wooldridge. (1999). Multi-Agent systems: A modern approach to distributed
artificial intelligence. Massachusetts,MIT Press.
4. Rosaria Conte. (2002). Agent-based modeling for understanding social intelligence. PNAS,
May 14.
5. T. G. Dietterich. (1998) Machine-learning research:Four current directions. The AI
Magazine, 18(4): 97 Œ136).
6. D. Frey, T. Stockheim, P.O. Woelk and R. Zimmermann. (2003). Integrated multi-agentbased
supply chain management. In: Proceedings of the 12th IEEE International
Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises
(WETICE-2003) IEEE, p.24.
7. Y. Guo, J. Muller, and B. Bauer. (2004). A multi-agent approach for logistics performance
prediction using historical and context information. In: Proceedings of The Third
International Joint Conference on Autonomous Agents and Multi-agent Systems
(AAMAS'04), New York, IEEE Press.
214