Learning for distributed artificial intelligence systems

Michael L. Dowell, Ronald D. Bonnell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Over the last four decades, machine learning's primary interest has been single agent learning. In general, single agent learning involves improving the performance or increasing the knowledge of a single agent [5]. An improvement in performance or an increase in knowledge allows the agent to solve past problems with better quality or efficiency. An increase in knowledge may also allow the agent to solve new problems. An increase in performance is not necessarily due to an increase in knowledge. It may be brought about simply by rearranging the existing knowledge or utilizing it in a different manner. In addition, new knowledge may not be employed immediately but may be accumulated for future use.

Original languageEnglish (US)
Title of host publicationProceedings - 23rd Southeastern Symposium on System Theory, SSST 1991
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)0818621907, 9780818621901
StatePublished - Jan 1 1991
Externally publishedYes
Event23rd Southeastern Symposium on System Theory, SSST 1991 - Columbia, United States
Duration: Mar 10 1991Mar 12 1991

Publication series

NameProceedings - 23rd Southeastern Symposium on System Theory, SSST 1991


Conference23rd Southeastern Symposium on System Theory, SSST 1991
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Control and Systems Engineering


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