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Stochastic reasoning with action probabilistic logic programs¹

AI COMMUNICATIONS 2014年第2期27卷 157-167页

作者： Simari, Gerardo I. Univ Oxford Dept Comp Sci Oxford England

In this thesis, we introduce action probabilistic (ap-) logic programs, a formalism for reasoning about the probability of events whose inter-dependencies are not known. Our approach is based on probabilistic logic programming, which is especially useful for this purpose since it allows the representation of imprecise probabilities. All work in this area up to now has focused on the problem of entailment (verifying if a query formula follows from the available knowledge). This thesis identifies and addresses several other complementary problems that need to be solved when deriving the probabilities of events: (i) computing most probable worlds, (ii) answering abduction queries;and (iii) reasoning about promises. We address different variants of these problems, propose exact and heuristic algorithms to scalably solve them, present empirical evaluations of their performance, and discuss their applications in real-world settings.

关键词： Reasoning under uncertainty probabilistic logic programming imprecise probabilities

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Integration of logic and probability in inductive and terminological reasoning

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INTELLIGENZA ARTIFICIALE 2014年第1期8卷 23-38页

作者： Bellodi, Elena Univ Ferrara Engn Dept Via Saragat 1 I-44122 Ferrara Italy

Representing uncertain information is crucial for modeling real world domains. This has been fully recognized both in the field of logic programming and of Description logics (DLs), with the introduction of probabilistic logic languages (PLL) in logic and with various probabilistic extensions of DLs respectively. In this work, we consider the distribution semantics and face the problem of learning PLLs and expressing and reasoning with probabilistic DLs. For PLLs, we present a parameter learning algorithm (EMBLEM) and two structure learning algorithms (SLIPCASE, SLIPCOVER): EMBLEM is based on the Expectation Maximization method and efficiently computes the expectations directly on the Binary Decision Diagrams (BDD) built for inference. It is embedded in both SLIPCASE and SLIPCOVER. The algorithms were tested on real world relational datasets and showed superior performance in almost all cases with respect to the state of the art. We then transposed both the distribution semantics and the inference techniques based on BDDs to DLs, developing a probabilistic semantics (DISPONTE) and an algorithm (BUNDLE) that computes the probability of queries over DISPONTE DLs by encoding their explanations in BDDs. We show that BUNDLE is competitive with the probabilistic reasoner PRONTO on a real probabilistic ontology.

关键词： Statistical relational learning probabilistic logic programming distribution semantics binary decision diagrams probabilistic description logics

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Focused most probable world computations in probabilistic logic programs

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2012年第2-3期64卷 113-143页

作者： Simari, Gerardo I. Martinez, Maria Vanina Sliva, Amy Subrahmanian, V. S. Univ Maryland Dept Comp Sci College Pk MD 20742 USA Univ Maryland UMIACS College Pk MD 20742 USA

The "Most Probable World" (MPW) problem in probabilistic logic programming (PLPs) is that of finding a possible world with the highest probability. Past work has shown that this problem is computationally intractable and involves solving exponentially many linear programs, each of which is of exponential size. In this paper, we study what happens when the user focuses his interest on a set of atoms in such a PLP. We show that we can significantly reduce the number of worlds to be considered by defining a "reduced" linear program whose solution is in one-one correspondence with the exact solution to the MPW problem. However, the problem is still intractable. We develop a Monte Carlo sampling approach that enables us to build a quick approximation of the reduced linear program that allows us to estimate (inexactly) the solution to the MPW problem. We show experimentally that our approach works well in practice, scaling well to problems where the exact solution is intractable to compute.

关键词： probabilistic logic programming Imprecise probabilities Most probable worlds

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Imprecise probabilistic query answering using measures of ignorance and degree of satisfaction

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2012年第2-3期64卷 145-183页

作者： Yue, Anbu Liu, Weiru Hunter, Anthony Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland UCL Dept Comp Sci London WC1E 6BT England

In conditional probabilistic logic programming, given a query, the two most common forms for answering the query are either a probability interval or a precise probability obtained by using the maximum entropy principle. The former can be noninformative (e.g., interval [0, 1]) and the reliability of the latter is questionable when the priori knowledge is imprecise. To address this problem, in this paper, we propose some methods to quantitatively measure if a probability interval or a single probability is sufficient for answering a query. We first propose an approach to measuring the ignorance of a probabilistic logic program with respect to a query. The measure of ignorance (w.r.t. a query) reflects how reliable a precise probability for the query can be and a high value of ignorance suggests that a single probability is not suitable for the query. We then propose a method to measure the probability that the exact probability of a query falls in a given interval, e.g., a second order probability. We call it the degree of satisfaction. If the degree of satisfaction is high enough w.r.t. the query, then the given interval can be accepted as the answer to the query. We also prove our measures satisfy many properties and we use a case study to demonstrate the significance of the measures.

关键词： probabilistic logic programming Queries Ignorance Imprecise probability

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Experimentation of an expectation maximization algorithm for probabilistic logic programs

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INTELLIGENZA ARTIFICIALE 2012年第1期6卷 3-18页

作者： Bellodi, Elena Riguzzi, Fabrizio Univ Ferrara Dipartimento Ingn ENDIF Via Saragat 1 I-44122 Ferrara Italy

Statistical Relational Learning and probabilistic Inductive logic programming are two emerging fields that use representation languages able to combine logic and probability. In the field of logic programming, the distribution semantics is one of the prominent approaches for representing uncertainty and underlies many languages such as ICL, PRISM, ProbLog and LPADs. Learning the parameters for such languages requires an Expectation Maximization algorithm since their equivalent Bayesian networks contain hidden variables. EMBLEM (EM over BDDs for probabilistic logic programs Efficient Mining) is an EM algorithm for languages following the distribution semantics that computes expectations directly on the Binary Decision Diagrams that are built for inference. In this paper we present experiments comparing EMBLEM with LeProbLog, Alchemy, CEM, RIB and LFI-ProbLog on six real world datasets. The results show that EMBLEM is able to solve problems on which the other systems fail and it often achieves significantly higher areas under the Precision Recall and the ROC curves in a similar time.

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

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The PITA system: Tabling and answer subsumption for reasoning under uncertainty

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THEORY AND PRACTICE OF logic programming 2011年第4-5期11卷 433-449页

作者： Riguzzi, Fabrizio Swift, Terrance Univ Ferrara ENDIF I-44122 Ferrara Italy Univ Nova Lisboa CENTRIA P-1200 Lisbon Portugal

Many real world domains require the representation of a measure of uncertainty. The most common such representation is probability, and the combination of probability with logic programs has given rise to the field of probabilistic logic programming (PLP), leading to languages such as the Independent Choice logic, logic Programs with Annotated Disjunctions (LPADs), Problog, PRISM, and others. These languages share a similar distribution semantics, and methods have been devised to translate programs between these languages. The complexity of computing the probability of queries to these general PLP programs is very high due to the need to combine the probabilities of explanations that may not be exclusive. As one alternative, the PRISM system reduces the complexity of query answering by restricting the form of programs it can evaluate. As an entirely different alternative, Possibilistic logic Programs adopt a simpler metric of uncertainty than probability. Each of these approaches-general PLP, restricted PLP, and Possibilistic logic programming-can be useful in different domains depending on the form of uncertainty to be represented, on the form of programs needed to model problems, and on the scale of the problems to be solved. In this paper, we show how the PITA system, which originally supported the general PLP language of LPADs, can also efficiently support restricted PLP and Possibilistic logic Programs. PITA relies on tabling with answer subsumption and consists of a transformation along with an API for library functions that interface with answer subsumption. We show that, by adapting its transformation and library functions, PITA can be parameterized to PITA(IND,EXC) which supports the restricted PLP of PRISM, including optimizations that reduce non-discriminating arguments and the computation of Viterbi paths. Furthermore, we show PITA to be competitive with PRISM for complex queries to Hidden Markov Model examples, and sometimes much faster. We further show

关键词： probabilistic logic programming Possibilistic logic programming Tabling Answer Subsumption Program Transformation

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Stochastic simulation and modelling of metabolic networks in a machine learning framework

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SIMULATION MODELLING PRACTICE AND THEORY 2011年第9期19卷 1957-1966页

作者： Biba, Marenglen Xhafa, Fatos Esposito, Floriana Ferilli, Stefano Univ New York Tirana Dept Comp Sci Tirana Albania Tech Univ Catalonia Dept Languages & Informat Syst Catalonia Spain Univ Bari Dept Comp Sci I-70121 Bari Italy

Metabolomics is increasingly becoming an important field. The fundamental task in this area is to measure and interpret complex time and condition dependent parameters such as the activity or flux of metabolites in cells, their concentration, tissues elements and other biosamples. The careful study of all these elements has led to important insights in the functioning of metabolism. Recently, however, there is a growing interest towards an integrated approach to studying biological systems. This is the main goal in Systems Biology where a combined investigation of several components of a biological system is thought to produce a thorough understanding of such systems. Biological circuits are complex to model and simulate and many efforts are being made to develop models that can handle their intrinsic complexity. A significant part of biological networks still remains unknown even though recent technological developments allow simultaneous acquisition of many metabolite measurements. Metabolic networks are not only structurally complex but behave also in a stochastic fashion. Therefore, it is necessary to express structure and handle uncertainty to construct complete dynamics of these networks. In this paper we describe how stochastic modeling and simulation can be performed in a symbolic-statistical machine learning (ML) framework. We show that symbolic ML deal with structural and relational complexity while statistical ML provides principled approaches to uncertainty modeling. Learning is used to analyze traces of biochemical reactions and model the dynamicity through parameter learning, while inference is used to produce stochastic simulation of the network. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Stochastic simulation Machine learning Metabolic networks Metabolomics probabilistic logic programming Statistical relational learning

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Analysing a Publish/Subscribe System for Mobile Ad Hoc Networks with ProbLog

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13th International Symposium on Practical Aspects of Declarative Languages

作者： Mantadelis, Theofrastos Paridel, Koosha Janssens, Gerda Vanrompay, Yves Berbers, Yolande Katholieke Univ Leuven Dept Computerwetenschappen B-3001 Heverlee Belgium

ISBN: (纸本)9783642183775

Fadip is a Publish/Subscribe system for Mobile Ad hoc Networks which uses probabilistic routing of messages to deal with the volatile nature of the network. It uses controlled propagation of publications and subscriptions, with the fading gossip technique to reduce the number of broadcasts. We present a probabilistic logic program in ProbLog that models Fadip. This allows us to calculate the probabilities that messages are successfully received by subscribers and to analyse the performance of the Fadip system.

关键词： probabilistic logic programming ProbLog Mobile Ad hoc Networks Publish/Subscribe System

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Agent-Oriented probabilistic logic programming

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Journal of Computer Science & Technology 2006年第3期21卷 412-417页

作者：王洁鞠实儿刘椿年 Key Laboratory of Multimedia and Intelligent Software Beijing University of Technology Beijing 100022 P.R. China Institute of Logic and Cognition Zhongshan University Guangzhou 510275 P.R. China

Currently, agent-based computing is an active research area, and great efforts have been made towards the agent-oriented programming both from a theoretical and practical view. However, most of them assume that there is no uncertainty in agents＇ mental state and their environment. In other words, under this assumption agent developers are just allowed to specify how his agent acts when the agent is 100% sure about what is true/false. In this paper, this unrealistic assumption is removed and a new agent-oriented probabilistic logic programming language is proposed, which can deal with uncertain information about the world. The programming language is based on a combination of features of probabilistic logic programming and imperative programming.

关键词： agent uncertainty probabilistic logic programming agent-oriented programming

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CP-logic: A language of causal probabilistic events and its relation to logic programming

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THEORY AND PRACTICE OF logic programming 2009年第3期9卷 245-308页

作者： Vennekens, Joost Denecker, Marc Bruynooghe, Maurice Katholieke Univ Leuven Dept Comp Sci B-3001 Louvain Belgium

This paper develops a logical language for representing probabilistic causal laws. Our interest in Such a language is two-fold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability trees. In such a dynamic context, where the evolution of a domain over time is considered, the idea of a causal law as something which guides this evolution is quite natural. In our formalization, a set of probabilistic causal laws can be used to represent a class of probability trees in a concise, flexible and modular way. In this way, Our work extends Shafer's by offering a convenient logical representation for his semantical objects. Second, this language also has relevance for the area of probabilistic logic programming. In particular, we prove that the formal semantics of a theory in Our language can be equivalently defined as a probability distribution over the well-founded models of certain logic programs, rendering it formally quite similar to existing languages Such as ICL or PRISM. Because we can motivate and explain Our language in a completely self-contained way as a representation of probabilistic causal laws, this provides a new way of explaining the intuitions behind such probabilistic logic programs: we can say precisely which knowledge such a program expresses, in terms that are equally understandable by a non-logician. Moreover, we also obtain an additional piece of knowledge representation methodology for probabilistic logic programs, by showing how they call express probabilistic causal laws.

关键词： uncertainty causality probabilistic logic programming

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