Loglinear models for rst-order probabilistic reasoning
نویسنده
چکیده
Recent work on loglinear models in proba-bilistic constraint logic programming is applied to rst-order probabilistic reasoning. Probabilities are deened directly on the proofs of atomic formulae, and by marginal-isation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled deenite clauses to deene the proof probabilities. We have a conservative extension of rst-order reasoning, so that, for example, there is a one-one mapping between logical and random variables. We show how, in this framework, Inductive Logic Programming (ILP) can be used to induce the features of a loglinear model from data. We also compare the presented framework with other approaches to rst-order probabilistic reasoning.
منابع مشابه
Loglinear models for first-order probabilistic reasoning
Recent work on loglinear models in probabilistic constraint logic programming is applied to firstorder probabilistic reasoning. Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled definite clauses to define the proof probabilities. We have a cons...
متن کاملKnowledge Presentation and Reasoning with Loglinear Models
Veska Noncheva, Nuno Marques Abstract: Our approach for knowledge presentation is based on the idea of expert system shell. At first we will build a graph shell of both possible dependencies and possible actions. Then, reasoning by means of Loglinear models, we will activate some nodes and some directed links. In this way a Bayesian network and networks presenting loglinear models are generated.
متن کاملSpecial Issue on Knowledge Representation and Machine Learning Five Useful Properties of Probabilistic Knowledge Representations from the Point of View of Intelligent Systems
Although probabilistic knowledge representations and probabilistic reasoning have by now secured their position in artiicial intelligence, it is not uncommon to encounter misunderstanding of their foundations and lack of appreciation for their strengths. This paper describes ve properties of probabilistic knowledge representations that are particularly useful in intelligent systems research. (1...
متن کاملFive Useful Properties of Probabilistic Knowledge Representations From the Point of View of Intelligent Systems
Abst ract Although probabilistic knowledge representations and probabilistic reasoning have by now secured their position in artiicial intelligence, it is not uncommon to encounter misunderstanding of their foundations and lack of appreciation for their strengths. This paper describes ve properties of probabilistic knowledge representations that are particularly useful in intelligent systems re...
متن کاملCoherence and Compatibility of Markov Logic Networks
Markov logic is a robust approach for probabilistic relational knowledge representation that uses a log-linear model of weighted first-order formulas for probabilistic reasoning. This loglinear model always exists but may not represent the knowledge engineer’s intentions adequately. In this paper, we develop a general framework for measuring this coherence of Markov logic networks by comparing ...
متن کامل