Advances in Intelligent Systems for Defence

Jain, Lakhmi C.; Ichalkaranje, Nikhil; Tonfoni, Graziella

doi:10.1142/5139

Cited by 3 publications

(1 citation statement)

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“…In distributed problem solving, communication between computing entities, quantity of information shared are predetermined and embedded in design of computing entity. Distributed problem solving is rigid due to the embedded strategies and consequently offers little or no flexibility.In contrast to distributed problem solving, Multi-agent systems (MAS) [9][10][11] deal with the behaviour of the computing entities available to solve a given problem. In a multi-agent system each computing entity is referred to as an agent.…”

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confidence: 99%

An Introduction to Multi-Agent Systems

Balaji

Srinivasan

2010

Studies in Computational Intelligence

177

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Summary. Multi-agent systems is a subfield of Distributed Artificial Intelligence that has experienced rapid growth because of the flexibility and the intelligence available solve distributed problems. In this chapter, a brief survey of multi-agent systems has been presented. These encompass different attributes such as architecture, communication, coordination strategies, decision making and learning abilities. The goal of this chapter is to provide a quick reference to assist in the design of multi-agent systems and to highlight the merit and demerits of the existing methods.Keywords: Multi-agent systems, Agent architecture, Coordination strategies and MAS communication. Distributed Artificial Intelligence (DAI)Distributed artificial intelligence (DAI) is a subfield of Artificial Intelligence [1] that has gained considerable importance due to its ability to solve complex real-world problems. The primary focus of research in the field of distributed artificial intelligence has included three different areas. These are parallel AI, Distributed problem solving(DPS) and Multi-agent systems (MAS). Parallel AI primarily refers to methodologies used to facilitate classical AI [2][3][4][5][6][7][8] techniques when applied to distributed hardware architectures like multiprocessor or cluster based computing. The main aim of parallel AI is to increase the speed of operation and to work on parallel threads in order to arrive at a global solution for a particular problem. Distributed problem solving is similar to parallel AI and considers how a problem can be solved by sharing the resources and knowledge between large number of cooperating modules known as Computing entity. In distributed problem solving, communication between computing entities, quantity of information shared are predetermined and embedded in design of computing entity. Distributed problem solving is rigid due to the embedded strategies and consequently offers little or no flexibility.In contrast to distributed problem solving, Multi-agent systems (MAS) [9][10][11] deal with the behaviour of the computing entities available to solve a given problem. In a multi-agent system each computing entity is referred to as an agent. MAS can be defined as a network of individual agents that share knowledge and communicate 2 P.G. Balaji and D. Srinivasan with each other in order to solve a problem that is beyond the scope of a single agent. It is imperative to understand the characteristics of the individual agent or computing entity to distinguish a simple distributed system and multi-agent system.The chapter is organized into nine sections. Section 2 gives a brief overview of an agent and its properties. The characteristics of multi-agent system is given in section 3. Section 4 shows the general classification of MAS based on their organization and structure. Section 5 gives details of various mechanisms used in the communication of information between agents. Section 6 gives details of the decision making strategies used in MAS and is followed by the coordination pr...

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confidence: 99%