Del i serien Lecture Notes in Computer Science

Adaptive Agents and Multi-Agent Systems II

Adaptation and Multi-Agent Learning

Häftad, Engelska, 2005

AvDaniel Kudenko,Dimitar Kazakov,Eduardo Alonso

739 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

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.

Produktinformation

Utgivningsdatum2005-03-04
Mått155 x 235 x 18 mm
Vikt499 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor313
Upplaga2005
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540252603

Tillhör följande kategorier

Artificiell intelligens inom Data och IT

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.

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Del i serien Lecture Notes in Computer Science

Adaptive Agents and Multi-Agent Systems II

Adaptation and Multi-Agent Learning

Häftad, Engelska, 2005

AvDaniel Kudenko,Dimitar Kazakov,Eduardo Alonso

739 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

Produktinformation

Utgivningsdatum2005-03-04
Mått155 x 235 x 18 mm
Vikt499 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor313
Upplaga2005
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540252603

Tillhör följande kategorier

Artificiell intelligens inom Data och IT

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.