Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments

Gerhard Weiß

ISBN 10: 3540629343 / ISBN 13: 9783540629344

Published by Springer, Springer Apr 1997, 1997

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -The complexity of systems studied in distributed artificial intelligence (DAI), such as multi-agent systems, often makes it extremely difficult or even impossible to correctly and completely specify their behavioral repertoires and dynamics. There is broad agreement that such systems should be equipped with the ability to learn in order to improve their future performance autonomously. The interdisciplinary cooperation of researchers from DAI and machine learning (ML) has established a new and very active area of research and development enjoying steadily increasing attention from both communities. This state-of-the-art report documents current and ongoing developments in the area of learning in DAI systems. It is indispensable reading for anybody active in the area and will serve as a valuable source of information.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 312 pp. Englisch.

Seller Inventory # 9783540629344

Bibliographic Details

• Title: Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments
• Publisher: Springer, Springer Apr 1997
• Publication Date: 1997
• Language: English
• ISBN 10: 3540629343
• ISBN 13: 9783540629344
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 17D millimeters 235 millimeters width by 155 millimeters height by 17 millimeters depth
• Weight: 476 grams

About this title

Synopsis

The complexity of systems studied in distributed artificial intelligence (DAI), such as multi-agent systems, often makes it extremely difficult or even impossible to correctly and completely specify their behavioral repertoires and dynamics. There is broad agreement that such systems should be equipped with the ability to learn in order to improve their future performance autonomously. The interdisciplinary cooperation of researchers from DAI and machine learning (ML) has established a new and very active area of research and development enjoying steadily increasing attention from both communities. This state-of-the-art report documents current and ongoing developments in the area of learning in DAI systems. It is indispensable reading for anybody active in the area and will serve as a valuable source of information.