First-order Probabilistic Inference Revisited

Braz, Rodrigo de Salvo; Roth, Dan; Amir, Eyal

Permalink

https://hdl.handle.net/2142/10817 Copy

Description

Title

First-order Probabilistic Inference Revisited

Author(s)

• Braz, Rodrigo de Salvo
• Roth, Dan
• Amir, Eyal

Issue Date

2004-06

Keyword(s)

Artificial Intelligence

Abstract

Following ideas in Poole~\poole, which we correct, formalize and extend, this paper presents the first provable algorithm for reasoning with probabilistic first-order representations at the {\em lifted} level. Specifically, the algorithm automates the process of probabilistic reasoning about populations of individuals, their properties and the relations between them, without the need to ground the probabilistic knowledge base. The algorithm makes use of unification to guide an interleaving of variable ordering and first-order variable elimination. Importantly, our contribution includes the formalization of concepts necessary to reason about the algorithm's correctness and its correctness proof.

Type of Resource

text

Permalink

http://hdl.handle.net/2142/10817

Copyright and License Information

You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).

Owning Collections