First-order probabilistic inference

Poole, David

First-order probabilistic inference

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Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 985-990.

Abstract

There have been many proposals for first-order belief networks (i.e., where we quantify over individuals) but these typically only let us reason about the individuals that we know about. There are many in stances where we have to quantify over all of the individuals in a population. When we do this the population size often matters and we need to reason about all of the members of the population (but not necessarily individually). This paper presents an algorithm to reason about multiple individuals, where we may know particular facts about some of them, but want to treat the others as a group. Combining unification with variable elimination lets us reason about classes of individuals without needing to ground out the theory.

David Poole. 2003. First-order probabilistic inference. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03. IJCAI, 985–990. https://ijcai.org/proceedings/2003/141

@inproceedings{ijcai2003p141,
  title     = {First-order probabilistic inference},
  author    = {Poole, David},
  booktitle = {Proceedings of the Eighteenth International Joint Conference on
               Artificial Intelligence, {IJCAI-03}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Georg Gottlob},
  pages     = {985--990},
  year      = {2003},
  url       = {https://ijcai.org/proceedings/2003/141},
}

TY  - CPAPER
TI  - First-order probabilistic inference
AU  - Poole, David
T2  - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2003
SP  - 985
EP  - 990
UR  - https://ijcai.org/proceedings/2003/141
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