Probabilistic logic programming for hybrid relational domains
نویسندگان
چکیده
منابع مشابه
Constraints for Probabilistic Logic Programming
In knowledge representation, one commonly distinguishes definitions of predicates from constraints. This distinction is also useful for probabilistic programming and statistical relational learning as it explains the key differences between probabilistic programming languages such as ICL, ProbLog and Prism (which are based on definitions) and statistical relational learning languages such as Ma...
متن کاملDeep Probabilistic Logic Programming
Probabilistic logic programming under the distribution semantics has been very useful in machine learning. However, inference is expensive so machine learning algorithms may turn out to be slow. In this paper we consider a restriction of the language called hierarchical PLP in which clauses and predicates are hierarchically organized. In this case the language becomes truth-functional and infer...
متن کاملProbLog2: Probabilistic Logic Programming
We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for queryi...
متن کاملProbabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-line...
متن کاملProbabilistic Logic Programming
Of all scientiic investigations into reasoning with uncertainty and chance, probability theory is perhaps the best understood paradigm. Nevertheless, all studies conducted thus far into the semantics of quantitative logic programming(cf.) have restricted themselves to non-probabilistic semantical characterizations. In this paper, we take a few steps towards rectifying this situation. We deene a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Machine Learning
سال: 2016
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-016-5558-8