Adaptive Agents and Multi-Agent Systems II
Editat de Daniel Kudenko, Dimitar Kazakov, Eduardo Alonsoen Limba Engleză Paperback – 4 mar 2005
This book presents 17 revised and carefully reviewed papers taken from two workshops on the topic as well as 2 invited papers by leading researchers in the area. The papers deal with various aspects of machine learning, adaptation, and evolution in the context of agent systems and autonomous agents.
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Specificații
ISBN-13: 9783540252603
ISBN-10: 3540252606
Pagini: 328
Ilustrații: VIII, 313 p.
Dimensiuni: 155 x 235 x 18 mm
Greutate: 0.5 kg
Ediția:2005
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540252606
Pagini: 328
Ilustrații: VIII, 313 p.
Dimensiuni: 155 x 235 x 18 mm
Greutate: 0.5 kg
Ediția:2005
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
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Public țintă
ResearchCuprins
Gödel Machines: Towards a Technical Justification of Consciousness.- Postext – A Mind for Society.- Comparing Resource Sharing with Information Exchange in Co-operative Agents, and the Role of Environment Structure.- Baselines for Joint-Action Reinforcement Learning of Coordination in Cooperative Multi-agent Systems.- SMART (Stochastic Model Acquisition with ReinforcemenT) Learning Agents: A Preliminary Report.- Towards Time Management Adaptability in Multi-agent Systems.- Learning to Coordinate Using Commitment Sequences in Cooperative Multi-agent Systems.- Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-agent Systems.- Evolving the Game of Life.- The Strategic Control of an Ant-Based Routing System Using Neural Net Q-Learning Agents.- Dynamic and Distributed Interaction Protocols.- Advice-Exchange Between Evolutionary Algorithms and Reinforcement Learning Agents: Experiments in the Pursuit Domain.- Evolving Strategies for Agents in the Iterated Prisoner’s Dilemma in Noisy Environments.- Experiments in Subsymbolic Action Planning with Mobile Robots.- Robust Online Reputation Mechanism by Stochastic Approximation.- Learning Multi-agent Search Strategies.- Combining Planning with Reinforcement Learning for Multi-robot Task Allocation.- Multi-agent Reinforcement Learning in Stochastic Single and Multi-stage Games.- Towards Adaptive Role Selection for Behavior-Based Agents.