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Multiagent System Technologies ; Vol. 4196 ; 4th German Conference, MATES 2006, Erfurt, Germany, September 19-20, 2006, Proceedings
This book constitutes the refereed proceedings of the 4th German Conference on Multiagent Systems Technologies, MATES 2006, co-located with Net.ObjectDays (NoDe 2006). The 15 revised full papers presented were carefully reviewed and selected from 52 submissions. The papers are organized in topical sections on agent communication and interaction, applications and simulation, agent planning, agent-oriented software engineering, as well as trust and security.
Multiagent System Technologies ; 5th German Conference, MATES 2007, Leipzig, Germany, September 24-26, 2007, Proceedings
The papers are organized in topical sections on engineering multi-agent systems, multi-agent planning and learning, multi-agent communication, interaction, and coordination, multi-agent resource allocation, multi-agent planning and simulation, as well as trust and reputation.
Environments for Multi-Agent Systems III ; 3rd International Workshop, E4MAS 2006, Hakodate, Japan, May 8, 2006, Selected Revised and Invited Papers
This book are organized in topical sections on models, architecture, and design, mediated interaction and stigmery, governing environment, and applications.
Environments for Multi-Agent Systems II ; 2nd International Workshop, E4MAS 2005, Utrecht, The Netherlands, July 25, 2005, Selected Revised and Invited Papers
This book constitutes the thoroughly refereed post-proceedings of the Second International Workshop on Environments for Multiagent Systems, E4MAS 2005, held in July 2005. The 16 revised papers presented were carefully reviewed and selected from the lectures given at the workshop.
Environments for Multi-Agent Systems ; 1st International Workshop, E4MAS, 2004, New York, NY, July 19, 2004, Revised Selected Papers
The modern ?eld of multiagent systems has developed from two main lines of earlier research. Its practitioners generally regard it as a form of arti?cial intelligence (AI). Some of its earliest work was reported in a series of workshops in the US dating from1980,revealinglyentitled,“DistributedArti?cialIntelligence,”andpioneers often quoted a statement attributed to Nils Nilsson that “all AI is distributed. ” The locus of classical AI was what happens in the head of a single agent, and much MAS research re?ects this heritage with its emphasis on detailed modeling of the mental state and processes of individual agents. From this perspective, intelligenceisultimatelythepurviewofasinglemind,thoughitcanbeampli?ed by appropriate interactions with other minds. These interactions are typically mediated by structured protocols of various sorts, modeled on human conver- tional behavior. But the modern ?eld of MAS was not born of a single parent. A few - searchershavepersistentlyadvocatedideasfromthe?eldofarti?ciallife(ALife). These scientists were impressed by the complex adaptive behaviors of commu- ties of animals (often extremely simple animals, such as insects or even micro- ganisms). The computational models on which they drew were often created by biologists who used them not to solve practical engineering problems but to test their hypotheses about the mechanisms used by natural systems. In the ar- ?cial life model, intelligence need not reside in a single agent, but emerges at the level of the community from the nonlinear interactions among agents. - cause the individual agents are often subcognitive, their interactions cannot be modeled by protocols that presume linguistic competence.
Distributed computing – IWDC 2005 ; 7th International Workshop, Kharagpur, India, December 27-30, 2005, Proceedings
This book constitutes the refereed proceedings of the 7th International Workshop on Distributed Computing, IWDC 2004, held in Kharagpur, India in December 2005. The 28 revised full papers and 33 revised short papers presented together with 5 invited keynote talks were carefully reviewed and selected from 253 submissions. The papers are organized in topical sections on theory of distributed computing, sensor networks, fault tolerance, optical networks, peer-to-peer networks, wireless networks, network security, grid and networks, middleware and data management, mobility management, and distributed artificial intelligence.
Distributed artificial intelligence ; 2nd International conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings
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.
Learning and Adaption in Multi-Agent Systems ; 1st International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers
Contains selected and revised papers of the International Workshop on Lea- ing and Adaptation in Multi-Agent Systems (LAMAS 2005), held at the AAMAS 2005 Conference in Utrecht, The Netherlands, July 26. An important aspect in multi-agent systems (MASs) is that the environment evolves over time, not only due to external environmental changes but also due to agent int- actions. For this reason it is important that an agent can learn, based on experience, and adapt its knowledge to make rational decisions and act in this changing environment autonomously. Machine learning techniques for single-agent frameworks are well established. Agents operate in uncertain environments and must be able to learn and act - tonomously. This task is, however, more complex when the agent interacts with other agents that have potentially different capabilities and goals. The single-agent case is structurally different from the multi-agent case due to the added dimension of dynamic interactions between the adaptive agents. Multi-agent learning, i.e., the ability of the agents to learn how to cooperate and compete, becomes crucial in many domains. Autonomous agents and multi-agent systems (AAMAS) is an emerging multi-disciplinary area encompassing computer science, software engineering, biology, as well as cognitive and social sciences. A t- oretical framework, in which rationality of learning and interacting agents can be - derstood, is still under development in MASs, although there have been promising ?
Applications of Agent Technology in Traffic and Transportation
Building effective and user-friendly transportation systems is one of the big challenges for engineers in the 21st century. There is an increasing need to understand, model, and govern such systems at both the individual (micro) and the society (macro) level. Still, this raises significant technical problems, as transportation systems may contain thousands of autonomous, "intelligent" entities that need to be simulated and/or controlled. Therefore, traffic and transportation scenarios are extraordinarily appealing for Distributed Artificial Intelligence, and (multi-) agent technology in particular. This book gives an overview of recent advances in agent-based transportation systems. It includes both a state-of-the-art survey and reports on cutting-edge research in the field.
AI 2020 : Advances in artificial intelligence ; 33rd Australasian Joint Conference, AI 2020, Canberra, ACT, Australia, November 29–30, 2020, Proceedings
Constitutes the proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence, AI 2020, held in Canberra, ACT, Australia, in November 2020.* The 36 full papers presented in this volume were carefully reviewed and selected from 57 submissions. The paper were organized in topical sections named: applications; evolutionary computation; fairness and ethics; games and swarms; and machine learning.
