5 Result(s)
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Chapter and Conference Paper
Logic, Knowledge Representation, and Bayesian Decision Theory
In this paper I give a brief overview of recent work on uncertainty in AI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks for reasoning that emphasiz...
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Chapter and Conference Paper
Type Uncertainty in Ontologically-Grounded Qualitative Probabilistic Matching
This paper is part of a project to match real-world descriptions of instances of objects to models of objects. We use a rich ontology to describe instances and models at multiple levels of detail and multiple ...
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Chapter
The Independent Choice Logic and Beyond
The Independent Choice Logic began in the early 90’s as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set...
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Chapter and Conference Paper
Probabilistic Relational Learning and Inductive Logic Programming at a Global Scale
Building on advances in statistical-relational AI and the Semantic Web, this talk outlined how to create knowledge, how to evaluate knowledge that has been published, and how to go beyond the sum of human know...
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Chapter and Conference Paper
Logic, Probability and Computation: Foundations and Issues of Statistical Relational AI
Over the last 25 years there has been considerable body of research into combinations of predicate logic and probability forming what has become known as (perhaps misleadingly) statistical relational artificia...
