A tutorial on theoretical issues in probabilistic artificial intelligence

  • Matthew Kearns
  • Published 1998 in
    Proceedings 39th Annual Symposium on Foundations…

Abstract

In the last decade or so, many of the central problems of “classical” artificial intelligence — such as learning, planning and logical inference — have been reformulated in statistical or probabilistic frameworks. The benefits of this trend include the adoption of a common set of mathematical tools for the various AI subdisciplines, increased attention on… (More)

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