Abdellah Bedrouni

Coordination Strategies and Techniques in Distributed Intelligent Systems – Applications

This paper is devoted to describing the broad range of application domains which implement many of the coordination strategies and techniques from the field of multi-agent systems. The domains include defense, transportation, health care, telecommunication and e-business, emergency management, etc. The paper describes the diversity of the applications in which multi-agent coordination techniques have been applied to overcome the challenges or obstacles that have existed with regard to performance, interoperability and/or scalability. While the number of application domains is steadily increasing, the intent of this paper is to provide a small sampling of domains which are applying coordination techniques to build intelligent systems. This paper will also describe an emerging and important problem domain which requires the coordination among many entities across the civil-military boundary, and can benefit from multi-agent coordination techniques.

Coordination Strategies and Techniques

This chapter is devoted to providing a broad overview of the various strategies and techniques designed and implemented to enable the coordination of autonomous entities within organizations, processes, and distributed multi-agent systems. In addition, examples are incorporated to illustrate the use of these strategies and techniques in various software implementations and prototypes. Finally, additional issues are discussed in order to help the reader further understand both the potential advantages and limitations of applying such strategies or techniques.

Future Directions and Conclusion

Industrial applications and future commercial opportunities offer a useful perspective and provide the ability to identify key issues and future trends for multi-agent research in coordination. This chapter focuses on highlighting discrepancies between current and future needs and existing coordination technologies. In this context, it is organized to draw attention to potential directions and opportunities for future research, and discusses a wide range of open and emerging issues pertaining to multi-agent coordination.

Scalability and Performance Evaluation for the Coordination of Distributed Systems

This chapter investigates emerging challenges related to the coordination of large-scale distributed systems. It highlights two key design factors that must be considered when building practical and robust distributed systems, namely scalability and performance. Hence, the present chapter is devoted to the discussion of scalability and performance analysis of distributed multi-agent systems in the context of the coordination space. It also presents a critical description of the factors that affect scalability, and further describes existing standards and metrics for performance evaluation and analysis.

Applications of Multi-agent Coordination

The purpose of this chapter is to describe the broad range of application domains which implement many of the coordination strategies and techniques from the field of multi-agent systems. The domains include defense, transportation, health care, telecommunication and e-business. The objective of this chapter is to describe the diversity of the applications in which multi-agent coordination techniques have been applied to overcome the challenges or obstacles that have existed with regard to performance, interoperability and / or scalability. While the number of application domains is steadily increasing, the intent of this chapter is to provide a small sampling of domains which are applying coordination techniques to build intelligent systems. This chapter will also describe an emerging and important problem domain which can benefit from multi-agent coordination techniques.

Coordination Models and Frameworks

The previous chapters have identified, highlighted, and addressed different issues and key aspects pertaining to coordination such as the need to understand scalability issues, as well as examples of the context in which coordination can occur, specifically, within the domain of distributed problem solving. With these issues and examples in mind, this chapter takes a higher level view and outlines the need for adequate models to provide support for studying coordination and building reliable organizations and systems. This chapter is thus devoted to presenting and describing relevant coordination models and frameworks designed to provide the foundation for understanding and managing the space of agent interactions.

Coordination in Distributed Problem Solving and Planning

Coordination in distributed problem-solving and planning systems presents multiple dimensions to be notionally characterized. This chapter first introduces distributed problem-solving properties and motivations through various applications. It then explores fundamental notions, problems and issues associated to task and result -sharing as well as characterizing the salient features of distributed planning. Different distributed planning categories are discussed: distributed planning for distributed plans, distributed planning for centralized plans and distributed planning for distributed plans.

Learning and Coordination

Adaptive learning techniques can automate the large-scale coordination of multi-agent systems and enhance their robustness in dynamic environments. Several learning approaches have been developed to address the different aspects of coordination, from learning coordination rules to the integrated learning of trust and reputation in order to facilitate coordination in open systems. Although convergence in multi-agent learning is still an open research question, several applications have emerged using some of the learning techniques presented.