Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments: ECAI96 Workshop LDAIS, Budapest, Hungary, August 13, 1996, ICMAS96 Workshop LIOME, Kyoto, Japan, December 10, 1996 Selected Papers -
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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 ... Full description
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Description
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.
More Information
| Publisher | Springer Berlin Heidelberg |
|---|---|
| Release year | 1997 |
| Cover type | Softcover |
| EAN | 9783540629344 |