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Learning probabilistic logic models from probabilistic examples

2008
*
Machine Learning
*

Our results demonstrate that the PILP approach provides a way of

doi:10.1007/s10994-008-5076-4
pmid:19888348
pmcid:PMC2771423
fatcat:vpnu5djquncwfgsfvxlzsaoe3i
*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*...##
###
Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract)
[chapter]

*
Inductive Logic Programming
*

The ILP approach

doi:10.1007/978-3-540-78469-2_3
dblp:conf/ilp/ChenMS07
fatcat:3iy6kxkmsjamxicjahruzf46oe
*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. ...##
###
Statistical Relational Learning: An Inductive Logic Programming Perspective
[chapter]

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). ...

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

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). ...

##
###
Statistical Relational Learning
[chapter]

2011
*
Encyclopedia of Machine Learning
*

Definition Statistical relational

doi:10.1007/978-0-387-30164-8_786
fatcat:i6y52kf2rrgfnbocvfyuwu4yoa
*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. ...##
###
Probabilistic Inductive Logic Programming
[chapter]

2004
*
Lecture Notes in Computer Science
*

More precisely, we outline three classical settings for inductive

doi:10.1007/978-3-540-30215-5_3
fatcat:jze46hnrobdx5psd6vdibvnu6y
*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. ...##
###
Probabilistic Inductive Logic Programming
[chapter]

2008
*
Lecture Notes in Computer Science
*

More precisely, we outline three classical settings for inductive

doi:10.1007/978-3-540-78652-8_1
fatcat:6xf2hirgn5dydb4uqthlfu6qre
*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. ...##
###
Guest editors' introduction: special issue on inductive logic programming (ILP-2007)

2008
*
Machine Learning
*

In their paper "

doi:10.1007/s10994-008-5078-2
fatcat:xqqvh4peh5bi3fmtmc7je5uu7a
*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 ...##
###
ProbLog2: Probabilistic Logic Programming
[chapter]

2015
*
Lecture Notes in Computer Science
*

The system provides efficient algorithms for querying such

doi:10.1007/978-3-319-23461-8_37
fatcat:3vgd5ckrmvfarg3dlujuwzrnpa
*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. ...##
###
Probabilistic logic learning

2003
*
SIGKDD Explorations
*

The past few years have witnessed an significant interest in

doi:10.1145/959242.959247
fatcat:m7xplwu6effohcvrrspqhbqbcq
*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). ...##
###
Probabilistic Logic Learning
[chapter]

2014
*
Encyclopedia of Social Network Analysis and Mining
*

The past few years have witnessed an significant interest in

doi:10.1007/978-1-4614-6170-8_100530
fatcat:3wfefnqcavhopd4xewydxsutya
*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). ...##
###
Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains
[article]

2020
*
arXiv
*
pre-print

The

arXiv:2006.08480v1
fatcat:d23e4d6kcfbtjduztfr6y4536a
*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*. ...##
###
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation
[article]

2021
*
arXiv
*
pre-print

We introduce SMProbLog, a generalization of the

arXiv:2110.01990v2
fatcat:luax2uanmvcpvmdqhyrfrwvxce
*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 ...##
###
A History of Probabilistic Inductive Logic Programming

2014
*
Frontiers in Robotics and AI
*

Since the start, the problem of

doi:10.3389/frobt.2014.00006
fatcat:tirvvcv76faq3a6v7a6cdrierq
*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*. ...##
###
Learning directed probabilistic logical models: ordering-search versus structure-search

2008
*
Annals of Mathematics and Artificial Intelligence
*

We discuss how to

doi:10.1007/s10472-009-9134-9
fatcat:pt5zatdj3rcb3biyyabrgv3any
*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). ...
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