Kudenko / Kazakov / Alonso Adaptive Agents and Multi-Agent Systems II

Adaptation and Multi-Agent Learning
2005
ISBN: 978-3-540-32274-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 313 Seiten, eBook

Reihe: Lecture Notes in Artificial Intelligence

Adaptive agents and multi-agent systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, software engineering, and developmental biology, as well as cognitive and social science.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.

Zielgruppe

Research

Autoren/Hrsg.

Kudenko, Daniel

Kazakov, Dimitar

Alonso, Eduardo

Inhaltsverzeichnis

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.