environments multi agent systems (15 Ergebnisse)

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the thoroughly refereedpost-workshop proceedings of the 4th International Workshop on Environments forMultiagent Systems, E4MAS 2014 - 10 years later, held in Paris, France, in May2014 as an associated event of AAMAS 2014, the 13th International Conference onAutonomous Agents and Multiagent Systems.The 6 revised full papers presented together with 1roadmap paper and 7 invited papers were carefully reviewed and selected from 14initial submissions. The papers are organized in topical sections on connectingagents, environments, and humans; environments for complex and stigmergicsystems; virtual and simulated environments; and open agent environments andinteroperability.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Environments for Multiagent Systems, E4MAS 2006, held in Hakodate, Japan in May 2006. The 15 revised papers in this volume are organized in topical sections on models, architecture, and design, mediated interaction and stigmery, governing environment, and applications.

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Zustand: New. KlappentextrnrnReinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the operating environment changes. Applying this learning to m.

Zustand: New. 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 fro.

Kartoniert / Broschiert. Zustand: New. 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 lectu.

Zustand: New. In.

Zustand: New. In.

Zustand: New. In.

Taschenbuch. Zustand: Neu. Neuware - 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. The papers are organized in topical sections on models, architecture, and design, mediated coordination, as well as applications.

Taschenbuch. Zustand: Neu. Neuware - Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the operating environment changes. Applying this learning to multiple cooperative software agents (a multi-agent system) not only allows each individual agent to learn from its own experience, but also opens up the opportunity for the individual agents to learn from the other agents in the system, thus accelerating the rate of learning. This research presents the novel use of fuzzy state aggregation, as the means of function approximation, combined with the fastest policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF). The combination of fast policy hill climbing and fuzzy state aggregation function approximation is tested in two stochastic environments; Tileworld and the simulated robot soccer domain, RoboCup. The Tileworld results demonstrate that a single agent using the combination of FSA and PHC learns quicker and performs better than combined fuzzy state aggregation and Q-learning reinforcement learning alone. Results from the multi-agent RoboCup domain again illustrate that the policy hill climbing algorithms perform better than Q-learning alone in a multi-agent environment. The learning is further enhanced by allowing the agents to share their experience through a weighted strategy sharing.

Paperback. Zustand: Brand New. 268 pages. 9.30x6.20x0.61 inches. In Stock.