An autonomous agent is a computational system that acquires sensory data from its environment and decides by itself how to relate the external stimulus to its behaviors in order to attain certain goals. Responding to different stimuli received from its task environment, the agent may select and exhibit different behavioral patterns. The behavioral patterns may be carefully predefined or dynamically acquired by the agent based on some learning and adaptation mechanism(s). In order to achieve structural flexibility, reliability through redundancy, adaptability, and reconfigurability in real-world tasks, some researchers have started to address the issue of multiagent cooperation. Broadly speaking, the power of autonomous agents lies in their ability to deal with unpredictable, dynamically changing environments. Agent-based systems are becoming one of the most important computer technologies, holding out many promises for solving real-world problems.The aims of this book are to provide a guided tour to the pioneering work and the major technical issues in agent research, and to give an in-depth discussion on the computational mechanisms for behavioral engineering in autonomous agents. Through a systematic examination, the book attempts to provide the general design principles for building autonomous agents and the analytical tools for modeling the emerged behavioral properties of a multiagent system.

An autonomous agent is a computational system that acquires sensory data from its environment and decides by itself how to relate the external stimulus to its behaviors in order to attain certain goals. Responding to different stimuli received from its task environment, the agent may select and exhibit different behavioral patterns. The behavioral patterns may be carefully predefined or dynamically acquired by the agent based on some learning and adaptation mechanism(s). In order to achieve structural flexibility, reliability through redundancy, adaptability, and reconfigurability in real-world tasks, some researchers have started to address the issue of multiagent cooperation. Broadly speaking, the power of autonomous agents lies in their ability to deal with unpredictable, dynamically changing environments. Agent-based systems are becoming one of the most important computer technologies, holding out many promises for solving real-world problems. The aims of this book are to provide a guided tour to the pioneering work and the major technical issues in agent research, and to give an in-depth discussion on the computational mechanisms for behavioral engineering in autonomous agents. Through a systematic examination, the book attempts to provide the general design principles for building autonomous agents and the analytical tools for modeling the emerged behavioral properties of a multiagent system.

The challenges in implementing intelligent and autonomous software systems remain the development of self-adapting systems, self-healing applications, corporate global creation, and collaborated robotic teams. With software agent technology widely recognized as a key approach in implementing such global infrastructure, the importance of the role of

This book constitutes the refereed proceedings of the 9th Pacific Rim International Workshop on Multi-Agents, PRIMA 2006, held in Guilin, China, in August 2006. The book presents 39 revised full papers and 57 revised short papers together with 4 invited talks, addressing subjects from theoretical and methodological issues to applications. Topics include agent models, agent architectures, agent-oriented software engineering, semantic Web service, collaboration, coordination and negotiation, and more.

This two-volume set of LNAI 11775 and LNAI 11776 constitutes the refereed proceedings of the 12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019, held in Athens, Greece, in August 2019. The 77 revised full papers and 23 short papers presented together with 10 poster papers were carefully reviewed and selected from 240 submissions. The papers of the first volume are organized in the following topical sections: Formal Reasoning and Ontologies; Recommendation Algorithms and Systems; Social Knowledge Analysis and Management ; Data Processing and Data Mining; Image and Video Data Analysis; Deep Learning; Knowledge Graph and Knowledge Management; Machine Learning; and Knowledge Engineering Applications. The papers of the second volume are organized in the following topical sections: Probabilistic Models and Applications; Text Mining and Document Analysis; Knowledge Theories and Models; and Network Knowledge Representation and Learning.

The origins of evolutionary computation can be traced back to the late 1950's where it remained, almost unknown, to the broader scientific community for three decades until the 1980's when it started to receive significant attention, as did the study of multi-agent systems (MAS). This focuses on systems in which many intelligent agents interact with each other. Today these systems are not simply a research topic but are also beginning to become an important subject of academic teaching and industrial and commercial application. Co-Evolutionary Computational and Multi-Agent Systems introduces the author's recent work in these two new and important branches of artificial intelligence.

This book originates from the First International Workshop on Computational Autonomy -Potential, Risks, Solutions, AUTONOMY 2003, held in Melbourne, Australia in July 2003 as part of AAMAS 2003. In addition to 7 revised selected workshop papers, the volume editors solicited 14 invited papers by leading researchers in the area. The workshop papers and the invited papers present a comprehensive and coherent survey of the state of the art of research on autonomy, capturing various theories of autonomy, perspectives on autonomy in different kinds of agent-based systems, and practical approaches to dealing with agent autonomy.