Abstract
Cooperative Information Systems (CISs) are multiagent systems with organizational and database abstractions geared to the large open heterogeneous information environments of today. CIS is also the name of the associated research area, which has emerged from the synthesis of distributed databases and distributed artificial intelligence. In CIS, software agents mitigate an information environment's heterogeneity by interacting through common protocols, and manage its large size by making intelligent local decisions without centralized control. In order to cope with the dynamism presented by open environments, CIS agents must have the ability to adapt and learn. We discuss some of the most important problems involving learning and adaptivity in CISs, including requirements for reconciling semantics and improving coordination. We present a “customers' view” of learning technology as might find ready application in CISs.
Munindar P. Singh was partially supported by the NCSU College of Engineering, by the National Science Foundation under grants IRI-9529179 and IRI-9624425, and IBM Corporation.
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Singh, M.P., Huhns, M.N. (1997). Challenges for machine learning in cooperative information systems. In: Weiß, G. (eds) Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments. LDAIS LIOME 1996 1996. Lecture Notes in Computer Science, vol 1221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62934-3_38
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DOI: https://doi.org/10.1007/3-540-62934-3_38
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