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First-Order Probabilistic Languages: Into the Unknown

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Inductive Logic Programming (ILP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4455))

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Abstract

This paper surveys first-order probabilistic languages (FOPLs), which combine the expressive power of first-order logic with a probabilistic treatment of uncertainty. We provide a taxonomy that helps make sense of the profusion of FOPLs that have been proposed over the past fifteen years. We also emphasize the importance of representing uncertainty not just about the attributes and relations of a fixed set of objects, but also about what objects exist. This leads us to Bayesian logic, or BLOG, a language for defining probabilistic models with unknown objects. We give a brief overview of BLOG syntax and semantics, and emphasize some of the design decisions that distinguish it from other languages. Finally, we consider the challenge of constructing FOPL models automatically from data.

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Authors and Affiliations

  1. Computer Science and AI Laboratory, Massachusetts Institute of Technology, 32 Vassar St. Room 32-G480, Cambridge, MA 02139, USA

    Brian Milch

  2. Computer Science Division, University of California at Berkeley, Berkeley, CA 94720-1776, USA

    Stuart Russell

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  1. Brian Milch
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  2. Stuart Russell
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Stephen Muggleton Ramon Otero Alireza Tamaddoni-Nezhad

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Milch, B., Russell, S. (2007). First-Order Probabilistic Languages: Into the Unknown. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2006. Lecture Notes in Computer Science(), vol 4455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73847-3_3

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  • DOI: https://doi.org/10.1007/978-3-540-73847-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73846-6

  • Online ISBN: 978-3-540-73847-3

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