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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| Collectivités auteurs: | , |
| Format: | Livre numérique |
| Langue: | Anglais |
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Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Collection: | Lecture notes in computer science. Lecture notes in artificial intelligence
1221 |
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| 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) |
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| 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 |
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| 100 | 1 | |a Weiss, Gerhard, |d 1962- | |
| 245 | 1 | 0 | |a Distributed artificial intelligence meets machine learning : |b 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 |c [edited by] Gerhard Weiß. |
| 260 | |a Berlin [etc.] : |b Springer. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Lecture notes in computer science. Lecture notes in artificial intelligence |v 1221 |x 1611-3349 |x 2945-9141 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a Reader's guide -- Challenges for machine learning in cooperative information systems -- A modular approach to multi-agent reinforcement learning -- Learning real team solutions -- Learning by linear anticipation in multi-agent systems -- Learning coordinated behavior in a continuous environment -- Multi-agent learning with the success-story algorithm -- On the collaborative object search team: a formulation -- Evolution of coordination as a metaphor for learning in multi-agent systems -- Correlating internal parameters and external performance: Learning Soccer Agents -- Learning agents' reliability through Bayesian Conditioning: A simulation experiment -- A study of organizational learning in multiagents systems -- Cooperative Case-based Reasoning -- Contract-net-based learning in a user-adaptive interface agency -- The communication of inductive inferences -- Addressee Learning and Message Interception for communication load reduction in multiple robot environments -- Learning and communication in Multi-Agent Systems -- Investigating the effects of explicit epistemology on a Distributed learning system. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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. | ||
| 650 | |a Informatique | ||
| 650 | |a Apprentissage automatique | ||
| 650 | |a Intelligence artificielle répartie | ||
| 650 | |a Langages de programmation | ||
| 650 | |a Simulation par ordinateur | ||
| 650 | |a Intelligence artificielle | ||
| 650 | |a Actes de congrès | ||
| 711 | 2 | |a European Conference on Artificial Intelligence |n (12 |d :1996 |c :Budapest). |4 aut | |
| 711 | 2 | |a International conference on multi-agent systems |n (02 |d :1996 |c :Kyoto, Japon). |4 aut | |
| 776 | 0 | |0 030040965 |t Distributed artificial intelligence meets machine learning |o learning in multi-agent environments |o ECAI'96 Workshop LDAIS, Budapest, Hungary, August 13, 1996, ICMAS'96 Workshop LIOME, Kyoto, Japan, December 10, 1996, selected papers |f Gerhard Weiss, (ed.) |d 1997 |c Berlin |n Springer |p 1 vol. (X-294 p.) |s Lecture notes in computer science |z 3-540-62934-3 | |
| 776 | 0 | |t Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments |b Texte imprimé |z 9783662172223 | |
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