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Agent systems are being used to model complex systems like societies, markets and biological systems. In this book we investigate issues of agent systems related to convergence and interactivity using techniques from agent based modelling to simulate complex systems, and demonstrate that interactivity/exchange and convergence in multi-agent systems are issues that are significantly interrelated. Topic and features: - Introduces the state of the art in multi-agent systems, with an emphasis on agent-based computational economics. - Sheds light on the fundamental concepts behind the stability of multi-agent systems. - Investigates knowledge exchange among agents, the rationale behind it and its effects on the ecosystem. - Explores how information provided through interaction with the system can be used to optimise its performance. - Describes a pricing strategy for a realistic large-scale distributed system. This book supplies a comprehensive resource and will be invaluable reading for researchers and postgraduates studying this topic.
This book constitutes the refereed proceedings of the 5th KES International Conference on Agent and Multi-Agent Systems, KES-AMSTA 2011, held in Manchester, UK, in June/July 2011. The 69 revised papers presented were carefully reviewed and selected for inclusion in the book. In addition the volume contains one abstract and one full paper length keynote speech. The papers are organized in topical sections on conversational agents, dialogue systems and text processing; agents and online social networks; robotics and manufacturing; agent optimisation; negotiation and security; multi-agent systems; mining and profiling; agent-based optimization; doctoral track; computer-supported social intelligence for human interaction; digital economy; and intelligent workflow, cloud computing and systems.
This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.
This is this, this ain't something else, this is this -Robert De Niro, Deerhunter his book may to some extent be viewed as the continuation of my T Doctoral thesis Epistemology, Methodology and Reliability. The dissertation was, first of all, a methodological study of the reliable performance of the AGM-axioms (Alchourr6n, Gardenfors and Makin son) of belief revision. Second of all the dissertation included the first steps toward an epistemology for the limiting convergence of knowledge for scientific inquiry methods of both discovery and assessment. The idea of methodological reliability as a desirable property of a scientific method was introduced to me while I was a visiting Ph. D. -student at the Department of Philosophy, Carnegie Mellon University in Pitts burgh, Pennsylvania, USA in 1995-96. Here I became acquainted with formal learning theory. Learning theory provides a variety of formal tools for investigating a number of important issues within epistemology, methodology and the philosophy of science. Especially with respect to the problem of induc tion, but not exclusively. The Convergence of Scientific Knowledge-a view from the limit utilizes a few concepts from formal learning theory to study problems in modal logic and epistemology. It should be duely noted that this book has virtually nothing to do with formal learning theory or inductive learning problems.
Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Learning and Adaption in Multi-Agent Systems, LAMAS 2005, held in The Netherlands, in July 2005, as an associated event of AAMAS 2005. The 13 revised papers presented together with two invited talks were carefully reviewed and selected from the lectures given at the workshop.
This book constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Coordination, Organizations, Institutions, and Norms for Governance of Multi-Agent Systems, COINE 2022, which was held in Auckland, New Zealand, on May 9, 2022. The 14 papers included in these proceedings were carefully reviewed and selected from 15 submissions. They deal with autonomous agents and multi-agent systems, focusing on the scientific and technological aspects of social coordination, organizational theory, artificial (electronic) institutions, and normative and ethical MAS.
As organizations, businesses, and other institutions work to move forward during a new era of ubiquitous modern technology, new computing and technology implementation strategies are necessary to harness the shared knowledge of individuals to advance their organizations as a whole. Intelligent and Knowledge-Based Computing for Business and Organizational Advancements examines the emerging computing paradigm of Collective Intelligence (CI). The global contributions contained in this publication will prove to be essential to both researchers and practitioners in the computer and information science communities as these populations move toward a new period of fully technology-integrated business.
Assuming no prior knowledge of Distributed Artificial Intelligence (DAI), this book deals with the complete development lifecycle of multi-agent systems for industrial applications.