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...
| Συγγραφή απο Οργανισμό/Αρχή: | |
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| Άλλοι συγγραφείς: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
1997.
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| Έκδοση: | 1st ed. 1997. |
| Σειρά: | Lecture Notes in Artificial Intelligence ;
1221 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
