Distributed Artificial Intelligence
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Distributed Artificial Intelligence
Distributed Artificial Intelligence (DAI) came to existence as an approach for solving complex learning, planning, and decision-making problems. When we talk about decision making, there may be some meta-heuristic methods where the problem solving may resemble like operation research. But exactly, it is not related completely to management research. The text examines representing and using organizational knowledge in DAI systems, dynamics of computational ecosystems, and communication-free interactions among rational agents. This publication takes a look at conflict-resolution strategies for nonhierarchical distributed agents, constraint-directed negotiation of resource allocations, and plans for multiple agents. Topics included plan verification, generation, and execution, negotiation operators, representation, network management problem, and conflict-resolution paradigms. The manuscript elaborates on negotiating task decomposition and allocation using partial global planning and mechanisms for assessing nonlocal impact of local decisions in distributed planning. The book will attract researchers and practitioners who are working in management and computer science, and industry persons in need of a beginner to advanced understanding of the basic and advanced concepts.
Distributed Artificial Intelligence
Distributed Artificial Intelligence presents a collection of papers describing the state of research in distributed artificial intelligence (DAI). DAI is concerned with the cooperative solution of problems by a decentralized group of agents. The agents may range from simple processing elements to complex entities exhibiting rational behavior. The book is organized into three parts. Part I addresses ways to develop control abstractions that efficiently guide problem-solving; communication abstractions that yield cooperation; and description abstractions that result in effective organizational structure. Part II describes architectures for developing and testing DAI systems. Part III discusses applications of DAI in manufacturing, office automation, and man-machine interactions. This book is intended for researchers, system developers, and students in artificial intelligence and related disciplines. It can also be used as a reference for students and researchers in other disciplines, such as psychology, philosophy, robotics, and distributed computing, who wish to understand the issues of DAI.
Distributed Artificial Intelligence
This book constitutes the refereed proceedings of the Second International Conference on Distributed Artificial Intelligence, DAI 2020, held in Nanjing, China, in October 2020. The 9 full papers presented in this book were carefully reviewed and selected from 22 submissions. DAI aims at bringing together international researchers and practitioners in related areas including general AI, multiagent systems, distributed learning, computational game theory, etc., to provide a single, high-profile, internationally renowned forum for research in the theory and practice of distributed AI. Due to the Corona pandemic this event was held virtually.