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The Most Probable Explanation for Probabilistic logic programs with annotated disjunctions 1

24th International Conference on Inductive logic Programming (ILP)

作者： Shterionov, Dimitar Renkens, Joris Vlasselaer, Jonas Kimmig, Angelika Meert, Wannes Janssens, Gerda KULeuven Leuven Belgium

ISBN: (数字)9783319237084

ISBN: (纸本)9783319237084;9783319237077

Probabilistic logic languages, such as ProbLog and CP-logic, are probabilistic generalizations of logic programming that allow one to model probability distributions over complex, structured domains. Their key probabilistic constructs are probabilistic facts and annotated disjunctions to represent binary and mutli-valued random variables, respectively. ProbLog allows the use of annotated disjunctions by translating them into probabilistic facts and rules. This encoding is tailored towards the task of computing the marginal probability of a query given evidence (MARG), but is not correct for the task of finding the most probable explanation (MPE) with important applications e.g., diagnostics and scheduling. In this work, we propose a new encoding of annotated disjunctions which allows correct MARG and MPE. We explore from both theoretical and experimental perspective the trade-off between the encoding suitable only for MARG inference and the newly proposed (general) approach.

关键词： Probabilistic logic programming Statistical relational learning Most Probable Explanation logic programs with annotated disjunctions ProbLog

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Reasoning on logic programs with annotated disjunctions

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INTELLIGENZA ARTIFICIALE 2012年第1期6卷 77-96页

作者： Bragaglia, Stefano Univ Bologna DEIS Viale Risorgimento Bologna Italy

Probabilistic Inductive logic Programming and Statistical Relational Learning are families of techniques that are exploited in Machine Learning applications to perform advanced tasks in several domains. Every day the size and complexity of such problems increases and advanced, expressive and efficient tools are needed to successfully solve them. The literature proposes several algorithms to cope with these problems, each of them with its own quirks and perks. Among various solutions, logic Programming with annotated disjunctions (LPAD) is one of the more attractive formalisms, thanks to the expressiveness and readability of its language. Unfortunately, its most advanced implementations are lacking efficient features and techniques that have been introduced for other formalisms, such as ProbLog. In this work, after introducing LPADs and an inference algorithm for computing the probability of a query, we investigate four different approximated algorithms, inspired by similar work done in ProbLog. In particular, we present each algorithm and we evaluate its performances on real and artificial datasets. The results show that our approaches have performances that are usually in line with ProbLog. The Monte Carlo algorithm, however, has performances that are better than the exact approach in terms of both the maximum size of the problems and the execution time, with a neglectable loss in the accuracy of the result.

关键词： Probabilistic Inductive logic Programming logic programs with annotated disjunctions ProbLog

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Summarizing significant subgraphs by probabilistic logic programming

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INTELLIGENT DATA ANALYSIS 2019年第6期23卷 1299-1312页

作者： Bellodi, Elena Satoh, Ken Sugiyama, Mahito Univ Ferrara Dept Engn Ferrara Italy Natl Inst Informat Tokyo Japan PRESTO JST Tokyo Japan

Although recent advances of significant subgraph mining enable us to find subgraphs that are statistically significantly associated with the class variable from graph databases, it is challenging to interpret the resulting subgraphs due to their massive number and their propositional representation. Here we represent graphs by probabilistic logic programming and solve the problem of summarizing significant subgraphs by structure learning of probabilistic logic programs. Learning probabilistic logical models leads to a much more interpretable, expressive and succinct representation of significant subgraphs. We empirically demonstrate that our approach can effectively summarize significant subgraphs with keeping high accuracy.

关键词： Subgraph summarization significant subgraph mining statistical significance probabilistic logic programming logic programs with annotated disjunctions

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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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Causal inference in cplint

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2017年 91卷 216-232页

作者： Riguzzi, Fabrizio Cota, Giuseppe Bellodi, Elena Zese, Riccardo Univ Ferrara Dipartimento Matemat & Informat Via Saragat 1 I-44122 Ferrara Italy Univ Ferrara Dipartimento Ingn Via Saragat 1 I-44122 Ferrara Italy

cplint is a suite of programs for reasoning and learning with Probabilistic logic Programming languages that follow the distribution semantics. In this paper we describe how we have extended cplint to perform causal reasoning. In particular, we consider Pearl's do calculus for models where all the variables are measured. The two cplint modules for inference, PITA and MCINTYRE, have been extended for computing the effect of actions/interventions on these models. We also executed experiments comparing exact and approximate inference with conditional and causal queries, showing that causal inference is often cheaper than conditional inference. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Probabilistic logic Programming Distribution semantics logic programs with annotated disjunctions ProbLog Causal inference Statistical relational artificial intelligence

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SLGAD Resolution for Inference on logic programs with annotated disjunctions

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FUNDAMENTA INFORMATICAE 2010年第3-4期102卷 429-466页

作者： Riguzzi, Fabrizio Univ Ferrara ENDIF I-44100 Ferrara Italy

logic programs with annotated disjunctions (LPADs) allow to express probabilistic information in logic programming. The semantics of an LPAD is given in terms of the well-founded models of the normal logic programs obtained by selecting one disjunct from each ground LPAD clause. Inference on LPADs can be performed using either the system Ailog2, that was developed for the Independent Choice logic, or SLDNFAD, an algorithm based on SLDNF. However, both of these algorithms run the risk of going into infinite loops and of performing redundant computations. In order to avoid these problems, we present SLGAD resolution that computes the (conditional) probability of a ground query from a range-restricted LPAD and is based on SLG resolution for normal logic programs. As SLG, it uses tabling to avoid some infinite loops and to avoid redundant computations. The performances of SLGAD are evaluated on classical benchmarks for normal logic programs under the well-founded semantics, namely a 2-person game and the ancestor relation, and on games of dice. SLGAD is compared with Ailog2 and SLDNFAD on the problems in which they do not go into infinite loops, namely those that are described by a modularly acyclic program. The results show that SLGAD is sometimes slower than Ailog2 and SLDNFAD but, if the program requires the repeated computations of the same goals, as for the dice games, then SLGAD is faster than both.

关键词： Probabilistic logic Programming Well-Founded Semantics logic programs with annotated disjunctions SLG Resolution

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Probabilistic logic programming on the web

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SOFTWARE-PRACTICE & EXPERIENCE 2016年第10期46卷 1381-1396页

作者： Riguzzi, Fabrizio Bellodi, Elena Lamma, Evelina Zese, Riccardo Cota, Giuseppe Univ Ferrara Dipartimento Matemat & Informat I-44100 Ferrara Italy Univ Ferrara Dipartimento Ingn I-44100 Ferrara Italy

We present the web application cplint on SWI-Prolog for SHaring that allows the user to write (SWISH)' Probabilistic logic programs and submit the computation of the probability of queries with a web browser. The application is based on SWISH, a web framework for logic Programming. SWISH is based on various features and packages of SWI-Prolog, in particular, its web server and its Pengine library, that allow to create remote Prolog engines and to pose queries to them. In order to develop the web application, we started from the PITA system, which is included in cplint, a suite of programs for reasoning over logic programs with annotated disjunctions, by porting PITA to SWI-Prolog. Moreover, we modified the PITA library so that it can be executed in a multi-threading environment. Developing cplint on SWISH' also required modification of the JavaScript SWISH code that creates and queries Pengines. cplint on SWISH' includes a number of examples that cover a wide range of domains and provide interesting applications of Probabilistic logic Programming. By providing a web interface to cplint, we allow users to experiment with Probabilistic logic Programming without the need to install a system, a procedure that is often complex, error prone, and limited mainly to the Linux platform. In this way, we aim to reach out to a wider audience and popularize Probabilistic logic Programming. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： logic programming probabilistic logic programming distribution semantics logic programs with annotated disjunctions web application tool for probabilistic logic programming

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Bandit-based Monte-Carlo structure learning of probabilistic logic programs

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MACHINE LEARNING 2015年第1期100卷 127-156页

作者： Di Mauro, Nicola Bellodi, Elena Riguzzi, Fabrizio Univ Bari Aldo Moro Dipartimento Informat I-70125 Bari Italy Univ Ferrara Dipartimento Ingn I-44122 Ferrara Italy Univ Ferrara Dipartimento Matemat & Informat I-44122 Ferrara Italy

Probabilistic logic programming can be used to model domains with complex and uncertain relationships among entities. While the problem of learning the parameters of such programs has been considered by various authors, the problem of learning the structure is yet to be explored in depth. In this work we present an approximate search method based on a one-player game approach, called LEMUR. It sees the problem of learning the structure of a probabilistic logic program as a multi-armed bandit problem, relying on the Monte-Carlo tree search UCT algorithm that combines the precision of tree search with the generality of random sampling. LEMUR works by modifying the UCT algorithm in a fashion similar to FUSE, that considers a finite unknown horizon and deals with the problem of having a huge branching factor. The proposed system has been tested on various real-world datasets and has shown good performance with respect to other state of the art statistical relational learning approaches in terms of classification abilities.

关键词： Statistical relational learning Structure learning Distribution semantics Multi-armed bandit problem Monte Carlo tree search logic programs with annotated disjunctions

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Speeding Up Inference for Probabilistic logic programs

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COMPUTER JOURNAL 2014年第3期57卷 347-363页

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

Probabilistic logic Programming (PLP) allows one to represent domains containing many entities connected by uncertain relations and has many applications in particular in Machine Learning. PITA is a PLP algorithm for computing the probability of queries, which exploits tabling, answer subsumption and Binary Decision Diagrams (BDDs). PITA does not impose any restriction on the programs. Other algorithms, such as PRISM, reduce computation time by imposing restrictions on the program, namely that subgoals are independent and that clause bodies are mutually exclusive. Another assumption that simplifies inference is that clause bodies are independent. In this paper, we present the algorithms PITA(IND,IND) and PITA(OPT). PITA(IND,IND) assumes that subgoals and clause bodies are independent. PITA(OPT) instead first checks whether these assumptions hold for subprograms and subgoals: if they do, PITA(OPT) uses a simplified calculation, otherwise it resorts to BDDs. Experiments on a number of benchmark datasets show that PITA(IND,IND) is the fastest on datasets respecting the assumptions, while PITA(OPT) is a good option when nothing is known about a dataset.

关键词： logic programming Probabilistic logic Programming distribution semantics logic programs with annotated disjunctions PRISM ProbLog

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ALLPAD: approximate learning of logic programs with annotated disjunctions

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MACHINE LEARNING 2008年第2-3期70卷 207-223页

作者： Riguzzi, Fabrizio Univ Ferrara Dipartimento Ingn I-44100 Ferrara Italy

logic programs with annotated disjunctions (LPADs) provide a simple and elegant framework for representing probabilistic knowledge in logic programming. In this paper we consider the problem of learning ground LPADs starting from a set of interpretations annotated with their probability. We present the system ALLPAD for solving this problem. ALLPAD modifies the previous system LLPAD in order to tackle real world learning problems more effectively. This is achieved by looking for an approximate solution rather than a perfect one. A number of experiments have been performed on real and artificial data for evaluating ALLPAD, showing the feasibility of the approach.

关键词： inductive logic programming probabilistic logic programming statistical relational learning logic programs with annotated disjunctions

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