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...
Guardat en:
| Autor principal: | |
|---|---|
| Autor corporatiu: | , |
| Format: | Livre numérique |
| Idioma: | Anglais |
| Publicat: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Col·lecció: | Lecture notes in computer science. Lecture notes in artificial intelligence
1221 |
| Matèries: | |
| Accés en línia: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
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 |
Taula de continguts:
- 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.