Distributed artificial intelligence meets machine learning : learning in multi-agent environments : ECAI'96 Workshop LDAIS, Budapest, Hungary, August 13, 1996 : ICMAS'96 Workshop LIOME, Kyoto, Japan, December 10, 1996 : selected papers
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 equip...
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| Auteur principal: | |
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| Collectivités auteurs: | , |
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Collection: | Lecture notes in computer science. Lecture notes in artificial intelligence
1221 |
| Sujets: | |
| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Distributed artificial intelligence meets machine learning, learning in multi-agent environments, ECAI'96 Workshop LDAIS, Budapest, Hungary, August 13, 1996, ICMAS'96 Workshop LIOME, Kyoto, Japan, December 10, 1996, selected papers, Gerhard Weiss, (ed.), 1997, Berlin, Springer, 1 vol. (X-294 p.), Lecture notes in computer science, 3-540-62934-3 • Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments, Texte imprimé, 9783662172223 |
| Résumé: | 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. |
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| Description: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783540690504 (PDF) |
| ISSN: | 1611-3349 2945-9141 |
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