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 /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Weiß, Gerhard (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1997.
• Έκδοση: 1st ed. 1997.
• Σειρά: Lecture Notes in Artificial Intelligence ; 1221
• Θέματα: Artificial intelligence.; Computer simulation.; Programming languages (Electronic computers).; Artificial Intelligence.; Simulation and Modeling.; Programming Languages, Compilers, Interpreters.
• Φυσική περιγραφή: XII, 300 p. online resource.
• ISBN: 9783540690504
• DOI: 10.1007/3-540-62934-3

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.