Innovations in Multi-Agent Systems and Application – 1, Volume 1
Dipti Srinivasan, Lakhmi C. Jain
Springer Science & Business Media, Aug 10, 2010 - Computers - 302 pages
In today’s world, the increasing requirement for emulating the behavior of real-world applications for achieving effective management and control has necessitated the usage of advanced computational techniques. Computational intelligence-based 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 real-world 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 agent-based problem solvers. An agent can be viewed as a self-contained, concurrently executing thread of control that encapsulates some state and communicates with its environment, and possibly other agents via message passing. Agent-based systems offer advantages when independently developed components must interoperate in a heterogenous environment. Such agent-based systems are increasingly being applied in a wide range of areas including telecommunications, Business process modeling, computer games, distributed system control and robot systems.
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adaptive agent communication language agent-based simulation aircraft analysis application approach architecture argument Artiﬁcial Intelligence asynchronous behavior bidding price BPNN California electricity crisis CHA framework classiﬁer clustering communication complex components control layer convergence cooperative decision deﬁned diﬀerent distributed domain dynamic environment estimation accuracy evolutionary example failure fault tolerance ﬁeld ﬁrst function fuzzy genetic algorithm goal Heidelberg hybrid multi-agent systems i-th IEEE IEEE Transactions increase input interaction International Conference Iteration knowledge learning algorithms LNCS Machine Learning market fundamentals MARL algorithms mechanism mobile robot multi-agent systems multiple Nash equilibrium neural network neuro-fuzzy node optimal parameter performance power trading predator problem Proceedings programming provides Q-function Q-learning Q-value RADB reinforcement learning represents resynchronization reward rule base scheme Section selected semantic single-agent steps stochastic game strategy structure supervised learning tasks techniques training data trajectories of agents Type update wholesaler z-th zone