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» Knowledge Representation with Logic Programs
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ICTAI
2007
IEEE
15 years 10 months ago
ExOpaque: A Framework to Explain Opaque Machine Learning Models Using Inductive Logic Programming
In this paper we developed an Inductive Logic Programming (ILP) based framework ExOpaque that is able to extract a set of Horn clauses from an arbitrary opaque machine learning mo...
Yunsong Guo, Bart Selman
ICLP
2010
Springer
15 years 8 months ago
Improving the Efficiency of Gibbs Sampling for Probabilistic Logical Models by Means of Program Specialization
Abstract. There is currently a large interest in probabilistic logical models. A popular algorithm for approximate probabilistic inference with such models is Gibbs sampling. From ...
Daan Fierens
ILP
2004
Springer
15 years 10 months ago
On Avoiding Redundancy in Inductive Logic Programming
ILP systems induce first-order clausal theories performing a search through very large hypotheses spaces containing redundant hypotheses. The generation of redundant hypotheses ma...
Nuno A. Fonseca, Vítor Santos Costa, Fernan...
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AI
2001
Springer
15 years 9 months ago
Knowledge and Planning in an Action-Based Multi-agent Framework: A Case Study
The situation calculus is a logical formalism that has been extensively developed for planning. We apply the formalism in a complex multi-agent domain, modelled on the game of Clue...
Bradley Bart, James P. Delgrande, Oliver Schulte
AMAI
2004
Springer
15 years 10 months ago
Production Inference, Nonmonotonicity and Abduction
We introduce a general formalism of production inference relations that posses both a standard monotonic semantics and a natural nonmonotonic semantics. The resulting nonmonotonic...
Alexander Bochman