Title
Innovations in MultiAgent Systems and Application 1 (Studies in Computational Intelligence, 310),New
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Product Description In todays world, the increasing requirement for emulating the behavior of realworld applications for achieving effective management and control has necessitated the usage of advanced computational techniques. Computational intelligencebased techniques that combine a variety of problem solvers are becoming increasingly pervasive. The ability of these methods to adapt to the dynamically changing environment and learn in an online manner has increased their usefulness in simulating intelligent behaviors as observed in humans. These intelligent systems are able to handle the stochastic and uncertain nature of the realworld problems. Application domains requiring interaction of people or organizations with different, even possibly conflicting goals and proprietary information handling are growing exponentially. To efficiently handle these types of complex interactions, distributed problem solving systems like multiagent systems have become a necessity. The rapid advancements in network communication technologies have provided the platform for successful implementation of such intelligent agentbased problem solvers. An agent can be viewed as a selfcontained, concurrently executing thread of control that encapsulates some state and communicates with its environment, and possibly other agents via message passing. Agentbased systems offer advantages when independently developed components must interoperate in a heterogenous environment. Such agentbased systems are increasingly being applied in a wide range of areas including telecommunications, Business process modeling, computer games, distributed system control and robot systems. From the Back Cover This book provides an overview of multiagent systems and several applications that have been developed for realworld problems. Multiagent systems is an area of distributed artificial intelligence that emphasizes the joint behaviors of agents with some degree of autonomy and the complexities arising from their interactions. Multiagent systems allow the subproblems of a constraint satisfaction problem to be subcontracted to different problem solving agents with their own interest and goals. This increases the speed, creates parallelism and reduces the risk of system collapse on a single point of failure. Different multiagent architectures, that are tailormade for a specific application are possible. They are able to synergistically combine the various computational intelligent techniques for attaining a superior performance. This gives an opportunity for bringing the advantages of various techniques into a single framework. It also provides the freedom to model the behavior of the system to be as competitive or coordinating, each having its own advantages and disadvantages.
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