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Regularization in probabilistic inductive logic programming 1

32nd International Conference on inductive logic programming (ILP)

作者： Gentili, Elisabetta Bizzarri, Alice Azzolini, Damiano Zese, Riccardo Riguzzi, Fabrizio Univ Ferrara Dept Engn Ferrara Italy Univ Ferrara Dept Environm & Prevent Sci Ferrara Italy Univ Ferrara Dept Chem Pharmaceut & Agr Sci Ferrara Italy Univ Ferrara Dept Math & Comp Sci Ferrara Italy

ISBN: (数字)9783031492990

ISBN: (纸本)9783031492983;9783031492990

probabilistic logic programming combines uncertainty and logic-based languages. Liftable probabilistic logic Programs have been recently proposed to perform inference in a lifted way. LIFTCOVER is an algorithm used to perform parameter and structure learning of liftable probabilistic logic programs. In particular, it performs parameter learning via Expectation Maximization and LBFGS. In this paper, we present an updated version of LIFTCOVER, called LIFTCOVER+, in which regularization was added to improve the quality of the solutions and LBFGS was replaced by gradient descent. We tested LIFTCOVER+ on the same 12 datasets on which LIFTCOVER was tested and compared the performances in terms of AUC-ROC, AUC-PR, and execution times. Results show that in most cases Expectation Maximization with regularization improves the quality of the solutions.

关键词： probabilistic inductive logic programming Regularization Statistical Relational Artificial Intelligence

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Pruning strategies for the efficient traversal of the search space in PILP environments

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KNOWLEDGE AND INFORMATION SYSTEMS 2021年第12期63卷 3183-3215页

作者： Corte-Real, Joana Dutra, Ines Rocha, Ricardo Univ Porto Fac Sci Dept Comp Sci Rua Campo Alegre 1021 P-4169007 Porto Portugal CRACS Porto Portugal INESC TEC Porto Portugal CINTESIS Porto Portugal

probabilistic inductive logic programming (PILP) is a statistical relational learning technique which extends inductive logic programming by considering probabilistic data. The ability to use probabilities to represent uncertainty comes at the cost of an exponential evaluation time when composing theories to model the given problem. For this reason, PILP systems rely on various pruning strategies in order to reduce the search space. However, to the best of the authors' knowledge, there has been no systematic analysis of the different pruning strategies, how they impact the search space and how they interact with one another. This work presents a unified representation for PILP pruning strategies which enables end-users to understand how these strategies work both individually and combined and to make an informed decision on which pruning strategies to select so as to best achieve their goals. The performance of pruning strategies is evaluated both time and quality-wise in two state-of-the-art PILP systems with datasets from three different domains. Besides analysing the performance of the pruning strategies, we also illustrate the utility of PILP in one of the application domains, which is a real-world application.

关键词： Statistical relational learning probabilistic inductive logic programming Search space evaluation Implementation Performance

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Learning failure-free PRISM programs

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2015年 67卷 73-110页

作者： Alsanie, Waleed Cussens, James King Abdulaziz City Sci & Technol Commun & Informat Technol Res Inst Natl Ctr Computat Technol & Appl Math Riyadh Saudi Arabia Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

PRISM is a probabilistic logic programming formalism which allows defining a probability distribution over possible worlds. This paper investigates learning a class of generative PRISM programs known as failure-free. The aim is to learn recursive PRISM programs which can be used to model stochastic processes. These programs generalise dynamic Bayesian networks by defining a halting distribution over the generative process. Dynamic Bayesian networks model infinite stochastic processes. Sampling from infinite process can only be done by specifying the length of sequences that the process generates. In this case, only observations of a fixed length of sequences can be obtained. On the other hand, the recursive PRISM programs considered in this paper are self-terminating upon some halting conditions. Thus, they generate observations of different lengths of sequences. The direction taken by this paper is to combine ideas from inductive logic programming and learning Bayesian networks to learn PRISM programs. It builds upon the inductive logic programming approach of learning from entailment. (C) 2015 Elsevier Inc. All rights reserved.

关键词： probabilistic inductive logic programming probabilistic logic PRISM Structure learning

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Expectation maximization over binary decision diagrams for probabilistic logic programs

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INTELLIGENT DATA ANALYSIS 2013年第2期17卷 343-363页

作者： Bellodi, Elena Riguzzi, Fabrizio Univ Ferrara ENDIF Dipartimento Ingn I-44122 Ferrara Italy

Recently much work in Machine Learning has concentrated on using expressive representation languages that combine aspects of logic and probability. A whole field has emerged, called Statistical Relational Learning, rich of successful applications in a variety of domains. In this paper we present a Machine Learning technique targeted to probabilistic logic Programs, a family of formalisms where uncertainty is represented using logic programming tools. Among various proposals for probabilistic logic programming, the one based on the distribution semantics is gaining popularity and is the basis for languages such as ICL, PRISM, ProbLog and logic Programs with Annotated Disjunctions. This paper proposes a technique for learning parameters of these languages. Since their equivalent Bayesian networks contain hidden variables, an Expectation Maximization (EM) algorithm is adopted. In order to speed the computation up, expectations are computed directly on the Binary Decision Diagrams that are built for inference. The resulting system, called EMBLEM for "EM over Bdds for probabilistic logic programs Efficient Mining", has been applied to a number of datasets and showed good performances both in terms of speed and memory usage. In particular its speed allows the execution of a high number of restarts, resulting in good quality of the solutions.

关键词： Statistical relational learning probabilistic inductive logic programming probabilistic logic programs logic programs with annotated disjunctions expectation maximization binary decision diagrams

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Learning probabilistic logic models from probabilistic examples

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MACHINE LEARNING 2008年第1期73卷 55-85页

作者： Chen, Jianzhong Muggleton, Stephen Santos, Jose Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

We revisit an application developed originally using abductive inductive logic programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two probabilistic ILP (PILP) approaches-abductive Stochastic logic Programs (SLPs) and programming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. 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.

关键词： probabilistic inductive logic programming stochastic logic programs abduction probabilistic examples

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Lifted discriminative learning of probabilistic logic programs

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MACHINE LEARNING 2019年第7期108卷 1111-1135页

作者： Fadja, Arnaud Nguembang Riguzzi, Fabrizio Univ Ferrara Dipartimento Ingn Via Saragat 1 I-44122 Ferrara Italy Univ Ferrara Dipartimento Matemat & Informat Via Saragat 1 I-44122 Ferrara Italy

probabilistic logic programming (PLP) provides a powerful tool for reasoning with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. In this paper we consider PLP models that are amenable to lifted inference and present an algorithm for performing parameter and structure learning of these models from positive and negative examples. We discuss parameter learning with EM and LBFGS and structure learning with LIFTCOVER, an algorithm similar to SLIPCOVER. The results of the comparison of LIFTCOVER with SLIPCOVER on 12 datasets show that it can achieve solutions of similar or better quality in a fraction of the time.

关键词： Statistical relational learning probabilistic inductive logic programming probabilistic logic programming Lifted inference Expectation maximization

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Applying the information bottleneck to statistical relational learning

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MACHINE LEARNING 2012年第1期86卷 89-114页

作者： Riguzzi, Fabrizio Di Mauro, Nicola Univ Ferrara Dipartimento Ingn I-44100 Ferrara Italy Univ Bari Aldo Moro Dipartimento Informat Bari Italy

In this paper we propose to apply the Information Bottleneck (IB) approach to the sub-class of Statistical Relational Learning (SRL) languages that are reducible to Bayesian networks. When the resulting networks involve hidden variables, learning these languages requires the use of techniques for learning from incomplete data such as the Expectation Maximization (EM) algorithm. Recently, the IB approach was shown to be able to avoid some of the local maxima in which EM can get trapped when learning with hidden variables. Here we present the algorithm Relational Information Bottleneck (RIB) that learns the parameters of SRL languages reducible to Bayesian Networks. In particular, we present the specialization of RIB to a language belonging to the family of languages based on the distribution semantics, logic Programs with Annotated Disjunction (LPADs). This language is prototypical for such a family and its equivalent Bayesian networks contain hidden variables. RIB is evaluated on the IMDB, Cora and artificial datasets and compared with LeProbLog, EM, Alchemy and PRISM. The experimental results show that RIB has good performances especially when some logical atoms are unobserved. Moreover, it is particularly suitable when learning from interpretations that share the same Herbrand base.

关键词： probabilistic inductive logic programming Statistical relational learning inductive logic programming Knowledge based model construction Distribution semantics

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Structure learning of probabilistic logic programs by searching the clause space

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THEORY AND PRACTICE OF logic programming 2015年第2期15卷 169-212页

作者： Bellodi, Elena Riguzzi, Fabrizio Univ Ferrara Dipartimento Ingn I-44122 Ferrara Italy Univ Ferrara Dipartimento Matemat & Informat I-44122 Ferrara Italy

Learning probabilistic logic programming languages is receiving an increasing attention, and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) or both structure and parameters (SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the algorithm SLIPCOVER for "Structure LearnIng of probabilistic logic programs by searChing OVER the clause space." It performs a beam search in the space of probabilistic clauses and a greedy search in the space of theories using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood, SLIPCOVER performs Expectation Maximization with EMBLEM. The algorithm has been tested on five real world datasets and compared with SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov logic Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER achieves higher areas under the precision-recall and receiver operating characteristic curves in most cases.

关键词： probabilistic inductive logic programming statistical relational learning structure learning distribution semantics logic programs with annotated disjunction CP-logic

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Approximate Inference for logic Programs with Annotated Disjunctions

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20th International Conference on inductive logic programming (ILP)

作者： Bragaglia, Stefano Riguzzi, Fabrizio Univ Ferrara ENDIF Univ Bologna DEIS I-44100 Ferrara Italy

ISBN: (纸本)9783642212949;9783642212956

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. In this paper we adapt to LPAD the approaches for approximate inference that have been developed for ProbLog, namely k-best and Monte Carlo. k-Best finds a lower bound of the probability of a query by identifying the k most probable explanations while Monte Carlo estimates the probability by smartly sampling the space of programs. The two techniques have been implemented in the cplint suite and have been tested on real and artificial datasets representing graphs. The results show that both algorithms are able to solve larger problems often in less time than the exact algorithm.

关键词： probabilistic inductive logic programming logic Programs with Annotated Disjunctions ProbLog

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Learning probabilistic logic models from probabilistic examples

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Joint Conference of the 17th International Conference on inductive logic programming/24th International Conference on Machine Learning

作者： Chen, Jianzhong Muggleton, Stephen Santos, Jose Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

ISBN: (纸本)3540784683

关键词： probabilistic inductive logic programming stochastic logic programs abduction probabilistic examples

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