Distributed Artificial Intelligence

This book explores the interplay between AI and Distributed Artificial Intelligence (DAI), focusing on enhancing decision-making, resource allocation, and conflict resolution in complex systems, catering to both researchers and industry professionals.

Distributed Artificial Intelligence explores the intricate relationship between artificial intelligence (AI) and distributed artificial intelligence (DAI), highlighting how these fields enhance each other's effectiveness for improved outcomes. The book aims to connect research solutions with key technologies in data analytics, addressing the demands of Industry 4.0 while ensuring robust network communication and the security of big data.

The text delves into the origins of DAI as a method for tackling complex challenges in learning, planning, and decision-making. It differentiates between meta-heuristic methods and traditional management research, emphasizing the unique aspects of DAI. The discussion includes the representation and utilization of organizational knowledge within DAI systems, the dynamics of computational ecosystems, and the interactions among rational agents without direct communication. Additionally, the book examines conflict-resolution strategies for nonhierarchical distributed agents and explores negotiation techniques for resource allocation.

Key topics covered include plan verification, generation, execution, and the complexities of network management. The manuscript provides insights into negotiating task decomposition and allocation through partial global planning, as well as evaluating the broader impacts of local decisions in distributed contexts. This publication is designed for researchers, practitioners in management and computer science, and industry professionals seeking a comprehensive understanding of both foundational and advanced concepts in DAI.