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Using Iterative Deepening for Probabilistic Logic Inference [chapter]

Theofrastos Mantadelis, Ricardo Rocha
2016 Lecture Notes in Computer Science  
We present a novel approach that uses an iterative deepening algorithm in order to perform probabilistic logic inference for ProbLog, a probabilistic extension of Prolog.  ...  The most used inference method for ProbLog is exact inference combined with tabling.  ...  Acknowledgments We want to thank the anonymous reviewers for their valuable comments.  ... 
doi:10.1007/978-3-319-51676-9_14 fatcat:4zjk4pxzj5ctnduod7d4nl2hcq

Approximate Inference for Logic Programs with Annotated Disjunctions [chapter]

Stefano Bragaglia, Fabrizio Riguzzi
2011 Lecture Notes in Computer Science  
Logic Programs with Annotated Disjunctions (LPADs) are a promising language for Probabilistic Inductive Logic Programming.  ...  In order to develop efficient learning systems for LPADs, it is fundamental to have high-performing inference algorithms. The existing approaches take too long or fail for large problems.  ...  Such techniques usually require the execution of a high number of inferences in probabilistic logics, which are costly tasks.  ... 
doi:10.1007/978-3-642-21295-6_7 fatcat:xhxbwpwbxndxtcvoebilw64i5a

Belief Change Based on Global Minimisation

James P. Delgrande, Jérôme Lang, Torsten Schaub
2007 International Joint Conference on Artificial Intelligence  
Our implementation further employs an approximation algorithm that combines iterative deepening with binary decision diagrams.  ...  We introduce ProbLog, a probabilistic extension of Prolog.  ...  ., 2006] for the biological data. Hannu Toivonen has been supported by Alexander von Humboldt Foundation and Tekes. This work was also partly supported by the EU IST FET projects April and IQ.  ... 
dblp:conf/ijcai/DelgrandeLS07 fatcat:lgjuq2ob3fb3vgb2qakssumjfi

Anytime Exact Belief Propagation [article]

Gabriel Azevedo Ferreira, Quentin Bertrand, Charles Maussion, Rodrigo de Salvo Braz
2017 arXiv   pre-print
A natural expectation for this project is that a probabilistic logic reasoning algorithm reduces to a logic reasoning algorithm when provided a model that only involves 0-1 probabilities, exhibiting all  ...  Statistical Relational Models and, more recently, Probabilistic Programming, have been making strides towards an integration of logic and probabilistic reasoning.  ...  (Vlasselaer et al. 2015) proposes a method based on forward rea-soning with iterative deepening.  ... 
arXiv:1707.08704v1 fatcat:bsj66st2ojczzmftpxjbuzccki

Towards solving the multiple extension problem: Combining defaults and probabilities

1988 International Journal of Approximate Reasoning  
Abstracts Probabilistic logic has been discussed in a recent paper by Nilsson.  ...  A new entailment scheme for belief functions is used that produces welldefined results even when only "consistent" worlds are being considered.  ...  A procedure is presented that performs an iterative deepening branchand-bound search for explanations with the property that the first path found is the most likely.  ... 
doi:10.1016/0888-613x(88)90156-9 fatcat:2zklvtewwvhfxatecocrxrgsem

Constrained inference of protein interaction networks for invadopodium formation in cancer

Haizhou Wang, Mingzhou Song, Laurie G. Hudson, Angela Wandinger-Ness, Ming Leung
2016 IET Systems Biology  
It encodes prior interactions as probabilistic logic rules called local constraints, and forms global constraints using observed dynamic patterns.  ...  However, a dilemma for deciphering interactome using Bayes' rule is the demotion of novel interactions with low prior probabilities.  ...  Stochastic and iterative deepening depth-first search To find a simple path between two consecutive stops-vertices in the super transition graph, we developed the stochastic and iterative deepening depth-first  ... 
doi:10.1049/iet-syb.2015.0009 pmid:26997662 pmcid:PMC4804358 fatcat:vd675i5yvjgvxafep3hpsb3bm4

On the implementation of the probabilistic logic programming language ProbLog

ANGELIKA KIMMIG, BART DEMOEN, LUC DE RAEDT, VÍTOR SANTOS COSTA, RICARDO ROCHA
2011 Theory and Practice of Logic Programming  
In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks.  ...  AbstractThe past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning.  ...  This work is partially supported by the GOA project 2008/08 Probabilistic Logic Learning.  ... 
doi:10.1017/s1471068410000566 fatcat:q3nliq4bpnca3ohmwsz2ywjaty

Probabilistic Explanation Based Learning [chapter]

Angelika Kimmig, Luc De Raedt, Hannu Toivonen
2007 Lecture Notes in Computer Science  
So, whereas traditional explanation based learning is typically used for speed-up learning, probabilistic explanation based learning is used for discovering new knowledge.  ...  Probabilistic explanation based learning extends this idea to probabilistic logic representations, which have recently become popular within the field of statistical relational learning.  ...  The inference procedure employs Binary Decision Diagrams in combination with an approximation algorithm based on iterative deepening, cf. [5] for more details.  ... 
doi:10.1007/978-3-540-74958-5_19 fatcat:fopeklsc2nepbbpaapkfn6wkwq

Using MetaProbLog and ConArg to compute Probabilistic Argumentation Frameworks

Stefano Bistarelli, Theofrastos Mantadelis, Francesco Santini, Carlo Taticchi
2018 International Conference of the Italian Association for Artificial Intelligence  
In this paper we use MetaProbLog, a ProbLog framework where facts in a logic program are annotated by probabilities; the purpose is to compute the probability of possible worlds of arguments.  ...  In Probabilistic Abstract Argumentation, arguments and attacks (nodes and edges) in a graph instance are associated with a probability value.  ...  ; (ii) program (AAF) sampling with memoization [9] ; (iii) any-time inference using an iterative deepening algorithm [14] .  ... 
dblp:conf/aiia/BistarelliM0T18 fatcat:nukx6ak7h5hdxna5noourtmyu4

On the Efficient Execution of ProbLog Programs [chapter]

Angelika Kimmig, Vítor Santos Costa, Ricardo Rocha, Bart Demoen, Luc De Raedt
2008 Lecture Notes in Computer Science  
The past few years have seen a surge of interest in the field of probabilistic logic learning or statistical relational learning. In this endeavor, many probabilistic logics have been developed.  ...  ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks.  ...  This work is partially supported by the GOA project 2008/08 Probabilistic Logic Learning.  ... 
doi:10.1007/978-3-540-89982-2_22 fatcat:d7l3n6n3ffdkzpzp66jh7jhj7i

Structure Learning via Parameter Learning

William Yang Wang, Kathryn Mazaitis, William W. Cohen
2014 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14  
This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR.  ...  learning of logic programs on graphs: using an abductive second-order probabilistic logic, we show how first-order theories can be automatically generated via parameter learning.  ...  Acknowledgements We thank Stephen Muggleton and Dianhuan Lin for interesting discussions of an early version of this paper. We are also grateful to anonymous reviewers for useful comments.  ... 
doi:10.1145/2661829.2662022 dblp:conf/cikm/WangMC14 fatcat:waltuvdfxfeh3huk6lssow52se

Structured machine learning: the next ten years

Thomas G. Dietterich, Pedro Domingos, Lise Getoor, Stephen Muggleton, Prasad Tadepalli
2008 Machine Learning  
The goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 years.  ...  More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistical Relational Learning (SRL) and Structured Prediction.  ...  Muggleton's work was supported by his Royal Academy of Engineering/Microsoft Research Chair on "Automated Microfluidic Experimentation using Probabilistic Inductive Logic Programming," the BB-SRC grant  ... 
doi:10.1007/s10994-008-5079-1 fatcat:arzjk4d7wrgffnzt4znrsfrb5q

Conditional Random Quantities and Iterated Conditioning in the Setting of Coherence [chapter]

Angelo Gilio, Giuseppe Sanfilippo
2013 Lecture Notes in Computer Science  
Y |K and we apply this representation to Bayesian updating of probabilities, by also deepening some aspects of Bayes' formula. Then, we introduce a notion of iterated c.r.q.  ...  Our notion of iterated conditional cannot formalize Bayesian updating but has an economic rationale. Finally, we define the coherence for prevision assessments on iterated c.r.q.'  ...  The authors are grateful to the anonymous referees for they valuable criticisms and suggestions.  ... 
doi:10.1007/978-3-642-39091-3_19 fatcat:niai3mrppnethhkcgqhd6rc4ta

Probabilistic Inductive Querying Using ProbLog [chapter]

Luc De Raedt, Angelika Kimmig, Bernd Gutmann, Kristian Kersting, Vítor Santos Costa, Hannu Toivonen
2010 Inductive Databases and Constraint-Based Data Mining  
We study how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilistic extension of Prolog.  ...  ProbLog can be regarded as a database system that supports both probabilistic and inductive reasoning through a variety of querying mechanisms.  ...  This work is partially supported by IQ (European Union Project IST-FET FP6-516169) and the GOA project 2008/08 Probabilistic Logic Learning.  ... 
doi:10.1007/978-1-4419-7738-0_10 fatcat:lpistivbwbgg5pjdq5o5orryl4

Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations

Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck
2020 Neural Information Processing Systems  
of complex logical and arithmetic constraints.  ...  First, we trace the boundaries of tractability for WMI inference in terms of two key properties of a WMI problem's dependency structure: sparsity and diameter.  ...  Acknowledgement The authors would like to thank Arthur Choi for several insightful discussions about the RCR framework.  ... 
dblp:conf/nips/ZengMYVB20 fatcat:rdy33pjrpvb3vbgcdoygguty74
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