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Learning probabilistic logic models from probabilistic examples

Jianzhong Chen, Stephen Muggleton, José Santos
2008 Machine Learning  
Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant  ...  decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.  ...  Acknowledgements The authors would like to acknowledge support from the Royal Academy of Engineering/Microsoft Research Chair on 'Automated Microfluidic Experimentation using Probabilistic Inductive Logic  ... 
doi:10.1007/s10994-008-5076-4 pmid:19888348 pmcid:PMC2771423 fatcat:vpnu5djquncwfgsfvxlzsaoe3i

Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract) [chapter]

Jianzhong Chen, Stephen Muggleton, José Santos
Inductive Logic Programming  
The ILP approach learned logic models from non-probabilistic examples.  ...  logic models from probabilistic examples.  ...  Acknowledgement The third author would like to acknowledge the funding from Wellcome Trust for his PhD program.  ... 
doi:10.1007/978-3-540-78469-2_3 dblp:conf/ilp/ChenMS07 fatcat:3iy6kxkmsjamxicjahruzf46oe

Statistical Relational Learning: An Inductive Logic Programming Perspective [chapter]

Luc De Raedt
2005 Lecture Notes in Computer Science  
Examples in this setting are Herbrand interpretations that should be a probabilistic model for the target theory. The third setting, learning from proofs [17], is novel.  ...  In probabilistic learning from entailment, examples are ground facts that should be probabilistically entailed by the target logic program.  ...  This work is part of the EU IST FET project APRIL II (Application of Probabilistic Inductive Logic Programming II).  ... 
doi:10.1007/11564096_3 fatcat:lwxyiiawqfbopjvgy5evuj2yzi

Statistical Relational Learning: An Inductive Logic Programming Perspective [chapter]

Luc De Raedt
2005 Lecture Notes in Computer Science  
Examples in this setting are Herbrand interpretations that should be a probabilistic model for the target theory. The third setting, learning from proofs [17], is novel.  ...  In probabilistic learning from entailment, examples are ground facts that should be probabilistically entailed by the target logic program.  ...  This work is part of the EU IST FET project APRIL II (Application of Probabilistic Inductive Logic Programming II).  ... 
doi:10.1007/11564126_3 fatcat:vtoipejcmjcg7ow6piwy4eiyjm

Statistical Relational Learning [chapter]

Eric Martin, Samuel Kaski, Fei Zheng, Geoffrey I. Webb, Xiaojin Zhu, Ion Muslea, Kai Ming Ting, Michail Vlachos, Risto Miikkulainen, Alan Fern, Miles Osborne, Luc De Raedt (+13 others)
2011 Encyclopedia of Machine Learning  
Definition Statistical relational learning aka. probabilistic inductive logic programming deals with machine learning and data mining in relational domains where observations may be missing, partially  ...  In doing so, it addresses one of the central questions of artificial intelligence -the integration of probabilistic reasoning with machine learning and first order and relational representationsand deals  ...  While Markov logic is a typical example of knowledge based model construction, ProbLog is a probabilistic programming language.  ... 
doi:10.1007/978-0-387-30164-8_786 fatcat:i6y52kf2rrgfnbocvfyuwu4yoa

Probabilistic Inductive Logic Programming [chapter]

Luc De Raedt, Kristian Kersting
2004 Lecture Notes in Computer Science  
More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they  ...  In the present paper, we start from inductive logic programming and sketch how it can be extended with probabilistic methods.  ...  This research was supported by the European Union under contract number FP6-508861, Application of Probabilistic Inductive Logic Programming II.  ... 
doi:10.1007/978-3-540-30215-5_3 fatcat:jze46hnrobdx5psd6vdibvnu6y

Probabilistic Inductive Logic Programming [chapter]

Luc De Raedt, Kristian Kersting
2008 Lecture Notes in Computer Science  
More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they  ...  In the present paper, we start from inductive logic programming and sketch how it can be extended with probabilistic methods.  ...  This research was supported by the European Union under contract number FP6-508861, Application of Probabilistic Inductive Logic Programming II.  ... 
doi:10.1007/978-3-540-78652-8_1 fatcat:6xf2hirgn5dydb4uqthlfu6qre

Guest editors' introduction: special issue on inductive logic programming (ILP-2007)

Hendrik Blockeel, Jude Shavlik, Prasad Tadepalli
2008 Machine Learning  
In their paper "Learning Probabilistic Logic Models from Probabilistic Examples", Chen, Muggleton, and Santos give a possible worlds semantics to Stochastic Logic Programs (SLPs), and use it to model an  ...  Their results using both their approach and another modeling tool called PRISM (for PRogramming In Statistical Modeling) show that probabilistic logic models learned from probabilistic examples are significantly  ... 
doi:10.1007/s10994-008-5078-2 fatcat:xqqvh4peh5bi3fmtmc7je5uu7a

ProbLog2: Probabilistic Logic Programming [chapter]

Anton Dries, Angelika Kimmig, Wannes Meert, Joris Renkens, Guy Van den Broeck, Jonas Vlasselaer, Luc De Raedt
2015 Lecture Notes in Computer Science  
The system provides efficient algorithms for querying such models as well as for learning their parameters from data. It is available as an online tool on the web and for download.  ...  The offline version offers both command line access to inference and learning and a Python library for building statistical relational learning applications from the system's components.  ...  Parameter learning from interpretations takes a base model and a set of examples as sets of evidence and compiles these into an SDD.  ... 
doi:10.1007/978-3-319-23461-8_37 fatcat:3vgd5ckrmvfarg3dlujuwzrnpa

Probabilistic logic learning

Luc De Raedt, Kristian Kersting
2003 SIGKDD Explorations  
The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning  ...  This paper provides an introductory survey and overview of the stateof-the-art in probabilistic logic learning through the identification of a number of important probabilistic, logical and learning concepts  ...  Acknowledgements This work benefited from the European Union IST project IST-2001-33053 (Application of Probabilistic Inductive Logic Programming -APRIL).  ... 
doi:10.1145/959242.959247 fatcat:m7xplwu6effohcvrrspqhbqbcq

Probabilistic Logic Learning [chapter]

2014 Encyclopedia of Social Network Analysis and Mining  
The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning  ...  This paper provides an introductory survey and overview of the stateof-the-art in probabilistic logic learning through the identification of a number of important probabilistic, logical and learning concepts  ...  Acknowledgements This work benefited from the European Union IST project IST-2001-33053 (Application of Probabilistic Inductive Logic Programming -APRIL).  ... 
doi:10.1007/978-1-4614-6170-8_100530 fatcat:3wfefnqcavhopd4xewydxsutya

Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains [article]

Vaishak Belle
2020 arXiv   pre-print
The learning camp attempts to generalize from examples about partial descriptions about the world.  ...  Our narrative is structured in terms of three strands: logic versus learning, machine learning for logic, and logic for machine learning, but naturally, there is considerable overlap.  ...  For example, a single probabilistic variable in the abstracted model could denote a complex logical formula in the original model.  ... 
arXiv:2006.08480v1 fatcat:d23e4d6kcfbtjduztfr6y4536a

SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation [article]

Pietro Totis, Angelika Kimmig, Luc De Raedt
2021 arXiv   pre-print
We introduce SMProbLog, a generalization of the probabilistic logic programming language ProbLog.  ...  Therefore, the key contribution of this paper are: a more general semantics for ProbLog programs, its implementation into a probabilistic programming framework for both inference and parameter learning  ...  Conclusion Approaching probabilistic argumentation from a probabilistic logic programming perspective stresses the limiting assumptions of PLP frameworks when (probabilistic) normal logic programs are  ... 
arXiv:2110.01990v2 fatcat:luax2uanmvcpvmdqhyrfrwvxce

A History of Probabilistic Inductive Logic Programming

Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese
2014 Frontiers in Robotics and AI  
Since the start, the problem of learning probabilistic logic programs has been the focus of much attention. Learning these programs represents a whole subfield of Inductive Logic Programming (ILP).  ...  The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming.  ...  Logical rules are learned from probabilistic data in the sense that both the examples themselves and their classifications can be probabilistic.  ... 
doi:10.3389/frobt.2014.00006 fatcat:tirvvcv76faq3a6v7a6cdrierq

Learning directed probabilistic logical models: ordering-search versus structure-search

Daan Fierens, Jan Ramon, Maurice Bruynooghe, Hendrik Blockeel
2008 Annals of Mathematics and Artificial Intelligence  
We discuss how to learn non-recursive directed probabilistic logical models from relational data.  ...  We conclude that there is no significant difference between the two algorithms in terms of quality of the learnt models while ordering-search is significantly faster.  ...  We conclude that ordering-search is a good alternative to structuresearch for learning directed probabilistic logical models. Fig. 1 . 1 Example of a logical CPD for satisf action(S, C).  ... 
doi:10.1007/s10472-009-9134-9 fatcat:pt5zatdj3rcb3biyyabrgv3any
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