On the relationship between logical Bayesian networks and probabilistic logic programming based on the distribution semantics
作者： Daan Fierens
DOI: 10.1007/978-3-642-13840-9_3
关键词: Probabilistic logic network 、 Functional logic programming 、 Subjective logic 、 Semantics 、 Probabilistic logic 、 Autoepistemic logic 、 Ontology language 、 Computer science 、 Statistical relational learning 、 Dynamic logic (modal logic) 、 Well-founded semantics 、 Description logic 、 Theoretical computer science 、 Logic programming 、 Fifth-generation programming language 、 Probabilistic CTL 、 Higher-order logic 、 Horn clause 、 Computational logic 、 Multimodal logic 、 Probabilistic argumentation 、 Inductive programming 、 Converse
摘要: A significant part of current research on (inductive) logic programming deals with probabilistic logical models. Over the last decade many logics or languages for representing such models have been introduced. There is currently a great need insight into relationships between all these languages. One kind are those that extend elements logic, as language Logical Bayesian Networks (LBNs). Some other follow converse strategy extending programs semantics, often in way similar to Sato's distribution semantics. In this paper we study relationship LBNs and based semantics. Concretely, define mapping from theories Independent Choice Logic (ICL). We also show how can be used learn ICL data.
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Joost Vennekens, Sofie Verbaeten, Maurice Bruynooghe, Logic programs with annotated disjunctions international conference on logic programming. ,vol. 3132, pp. 431- 445 ,(2004) , 10.1007/978-3-540-27775-0_30
Bernd Gutmann, Angelika Kimmig, Kristian Kersting, Luc De Raedt, Parameter learning in probabilistic databases: a least squares approach european conference on machine learning. ,vol. 5211, pp. 473- 488 ,(2008) , 10.1007/978-3-540-87479-9_49
Lise Getoor, Ben Taskar, Introduction to statistical relational learning MIT Press. ,(2007)
Nir Friedman, Moises Goldszmidt, Learning Bayesian networks with local structure uncertainty in artificial intelligence. pp. 252- 262 ,(1996) , 10.1007/978-94-011-5014-9_15
Luc De Raedt, Angelika Kimmig, Hannu Toivonen, None, ProbLog: a probabilistic prolog and its application in link discovery international joint conference on artificial intelligence. pp. 2468- 2473 ,(2007)
Tarek Abudawood, Peter Flach, Evaluation Measures for Multi-class Subgroup Discovery european conference on machine learning. pp. 35- 50 ,(2009) , 10.1007/978-3-642-04180-8_20
Lise Getoor, Ben Taskar, Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) The MIT Press. ,(2007)
Daan Fierens, Jan Ramon, Maurice Bruynooghe, Hendrik Blockeel, Learning directed probabilistic logical models: ordering-search versus structure-search Annals of Mathematics and Artificial Intelligence. ,vol. 54, pp. 99- 133 ,(2008) , 10.1007/S10472-009-9134-9
David Poole, Abducing through negation as failure: stable models within the independent choice logic Journal of Logic Programming. ,vol. 44, pp. 5- 35 ,(2000) , 10.1016/S0743-1066(99)00071-0
Jan Struyf, Wannes Meert, Hendrik Blockeel, Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques Fundamenta Informaticae. ,vol. 89, pp. 131- 160 ,(2008)