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A probabilistic logic programming event calculus

theory and practice of logic programming 2015年第2期15卷 213-245页

作者： Skarlatidis, Anastasios Artikis, Alexander Filippou, Jason Paliouras, Georgios NCSR Demokritos Inst Informat & Telecommun Athens Greece Univ Piraeus Dept Digital Syst Piraeus Greece Univ Maryland College Pk MD 20742 USA

We present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-defined temporal combinations of STA. The constraints on the STA that, if satisfied, lead to the recognition of an LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.

关键词： event recognition event pattern matching Event Calculus ProbLog

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Characterizing and extending answer set semantics using possibility theory

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theory and practice of logic programming 2015年第1期15卷 79-116页

作者： Bauters, Kim Schockaert, Steven De Cock, Martine Vermeir, Dirk Univ Ghent Dept Appl Math Comp Sci & Stat B-9000 Ghent Belgium Cardiff Univ Sch Comp Sci & Informat Cardiff CF24 3AA S Glam Wales Vrije Univ Brussel Dept Comp Sci B-1050 Brussels Belgium

Answer Set programming (ASP) is a popular framework for modelling combinatorial problems. However, ASP cannot be used easily for reasoning about uncertain information. Possibilistic ASP (PASP) is an extension of ASP that combines possibilistic logic and ASP. In PASP a weight is associated with each rule, whereas this weight is interpreted as the certainty with which the conclusion can be established when the body is known to hold. As such, it allows us to model and reason about uncertain information in an intuitive way. In this paper we present new semantics for PASP in which rules are interpreted as constraints on possibility distributions. Special models of these constraints are then identified as possibilistic answer sets. In addition, since ASP is a special case of PASP in which all the rules are entirely certain, we obtain a new characterization of ASP in terms of constraints on possibility distributions. This allows us to uncover a new form of disjunction, called weak disjunction, that has not been previously considered in the literature. In addition to introducing and motivating the semantics of weak disjunction, we also pinpoint its computational complexity. In particular, while the complexity of most reasoning tasks coincides with standard disjunctive ASP, we find that brave reasoning for programs with weak disjunctions is easier.

关键词： logic programming answer set programming possibility theory

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Generating explanations for biomedical queries

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theory and practice of logic programming 2015年第1期15卷 35-78页

作者： Erdem, Esra Oztok, Umut Sabanci Univ TR-34956 Istanbul Turkey

We introduce novel mathematical models and algorithms to generate (shortest or k different) explanations for biomedical queries, using answer set programming. We implement these algorithms and integrate them in BIOQUERY-ASP. We illustrate the usefulness of these methods with some complex biomedical queries related to drug discovery, over the biomedical knowledge resources PHARMGKB, DRUGBANK, BIOGRID, CTD, SIDER, DISEASE ONTOLOGY, and ORPHADATA.

关键词： answer set programming explanation generation query answering biomedical queries

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Inference and learning in probabilistic logic programs using weighted Boolean formulas

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theory and practice of logic programming 2015年第3期15卷 358-401页

作者： Fierens, Daan Van den Broeck, Guy Renkens, Joris Shterionov, Dimitar Gutmann, Bernd Thon, Ingo Janssens, Gerda De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci B-3001 Heverlee Belgium

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks, such as computing the marginals, given evidence and learning from (partial) interpretations, have not really been addressed for probabilistic logic programs before. The first contribution of this paper is a suite of efficient algorithms for various inference tasks. It is based on the conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce inference tasks to well-studied tasks, such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs expectation-maximization, and is built on top of the developed inference algorithms. The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state of the art in probabilistic logic programming, and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations.

关键词： probabilistic logic programming probabilistic inference parameter learning

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Rewriting recursive aggregates in answer set programming: back to monotonicity

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theory and practice of logic programming 2015年第4-5期15卷 559-573页

作者： Alviano, Mario Faber, Wolfgang Gebser, Martin Univ Calabria I-87030 Commenda Di Rende Italy Univ Huddersfield Huddersfield HD1 3DH W Yorkshire England Aalto Univ HIIT Espoo Finland

Aggregation functions are widely used in answer set programming for representing and reasoning on knowledge involving sets of objects collectively. Current implementations simplify the structure of programs in order to optimize the overall performance. In particular, aggregates are rewritten into simpler forms known as monotone aggregates. Since the evaluation of normal programs with monotone aggregates is in general on a lower complexity level than the evaluation of normal programs with arbitrary aggregates, any faithful translation function must introduce disjunction in rule heads in some cases. However, no function of this kind is known. The paper closes this gap by introducing a polynomial, faithful, and modular translation for rewriting common aggregation functions into the simpler form accepted by current solvers. A prototype system allows for experimenting with arbitrary recursive aggregates, which are also supported in the recent version 4.5 of the grounder GRINGO, using the methods presented in this paper.

关键词： answer set programming polynomial faithful and modular translation aggregation functions

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Introduction to the special issue on probability, logic and learning

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theory and practice of logic programming 2015年第2期15卷 145-146页

作者： Cussens, James de Raedt, Luc Kimmig, Angelika Sato, Taisuke Univ York Dept Comp Sci York YO10 5GE N Yorkshire England Univ York York Ctr Complex Syst Anal York YO10 5GE N Yorkshire England Katholieke Univ Leuven Dept Comp Sci B-3001 Heverlee Belgium Tokyo Inst Technol Dept Comp Sci Meguro Ku Tokyo 152 Japan

Recently, the combination of probability, logic and learning has received considerable attention in the artificial intelligence and machine learning communities; see e.g. Getoor and Taskar (2007); De Raedt et al. (2008). Computational logic often plays a major role in these developments since it forms the theoretical backbone for much of the work in probabilistic programming and logical and relational learning. Contemporary work in this area is often application- and experiment-driven, but is also concerned with the theoretical foundations of formalisms and inference procedures and with advanced implementation technology that scales well.

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Example-Directed Synthesis: A Type-Theoretic Interpretation

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ACM SIGPLAN NOTICES 2016年第1期51卷 802-815页

作者： Frankle, Jonathan Osera, Peter-Michael Walker, David Zdancewic, Steve Princeton Univ Princeton NJ 08544 USA Grinnell Coll Grinnell IA 50112 USA Univ Penn Philadelphia PA 19104 USA

Input-output examples have emerged as a practical and user-friendly specification mechanism for program synthesis in many environments. While example-driven tools have demonstrated tangible impact that has inspired adoption in industry, their underlying semantics are less well-understood: what are "examples" and how do they relate to other kinds of specifications? This paper demonstrates that examples can, in general, be interpreted as refinement types. Seen in this light, program synthesis is the task of finding an inhabitant of such a type. This insight provides an immediate semantic interpretation for examples. Moreover, it enables us to exploit decades of research in type theory as well as its correspondence with intuitionistic logic rather than designing ad hoc theoretical frameworks for synthesis from scratch. We put this observation into practice by formalizing synthesis as proof search in a sequent calculus with intersection and union refinements that we prove to be sound with respect to a conventional type system. In addition, we show how to handle negative examples, which arise from user feedback or counterexample-guided loops. This theory serves as the basis for a prototype implementation that extends our core language to support ML-style algebraic data types and structurally inductive functions. Users can also specify synthesis goals using polymorphic refinements and import monomorphic libraries. The prototype serves as a vehicle for empirically evaluating a number of different strategies for resolving the nondeterminism of the sequent calculus bottom-up theorem-proving, term enumeration with refinement type checking, and combinations of both the results of which classify, explain, and validate the design choices of existing synthesis systems. It also provides a platform for measuring the practical value of a specification language that combines "examples" with the more general expressiveness of refinements.

关键词： Languages theory Functional programming Proof Search Sequent Calculus Program Synthesis Type theory Refinement Types

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Proving correctness of imperative programs by linearizing constrained Horn clauses

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theory and practice of logic programming 2015年第4-5期15卷 635-650页

作者： De Angelis, Emanuele Fioravanti, Fabio Pettorossi, Alberto Proietti, Maurizio Univ G dAnnunzio DEC Pescara Italy Univ Roma Tor Vergata DICII Rome Italy CNR IASI Rome Italy

We present a method for verifying the correctness of imperative programs which is based on the automated transformation of their specifications. Given a program prog, we consider a partial correctness specification of the form {phi} prog {psi}, where the assertions phi and psi are predicates defined by a set Spec of possibly recursive Horn clauses with linear arithmetic (LA) constraints in their premise (also called constrained Horn clauses). The verification method consists in constructing a set PC of constrained Horn clauses whose satisfiability implies that {phi} prog {psi} is valid. We highlight some limitations of state-of-the-art constrained Horn clause solving methods, here called LA-solving methods, which prove the satisfiability of the clauses by looking for linear arithmetic interpretations of the predicates. In particular, we prove that there exist some specifications that cannot be proved valid by any of those LA-solving methods. These specifications require the proof of satisfiability of a set PC of constrained Horn clauses that contain nonlinear clauses (that is, clauses with more than one atom in their premise). Then, we present a transformation, called linearization, that converts PC into a set of linear clauses (that is, clauses with at most one atom in their premise). We show that several specifications that could not be proved valid by LA-solving methods, can be proved valid after linearization. We also present a strategy for performing linearization in an automatic way and we report on some experimental results obtained by using a preliminary implementation of our method.

关键词： Program verification Partial correctness specifications Horn clauses Constraint logic programming Program transformation

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Introduction to the 31st International Conference on logic programming special issue

Introduction to the 31st International Conference on Logic P...

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作者： Eiter, Thomas Toni, Francesca Technische Universität Wien Austria Imperial College London United Kingdom

The special issue of theory and practice of logic programming (TPLP) consists of the regular papers accepted for presentation at International Conference of logic programming (ICLP) 2015. Altogether 89 submissions of abstracts were received of which 74 resulted in submissions of papers. The program chairs, acting as guest editors of this special issue, organized the refereeing process with the help of the program committee and external reviewers. Each paper was reviewed by at least three anonymous referees who provided full written evaluations. The paper Complexity and Compilation of GZ-Aggregates in Answer Set programming, by Mario Alviano and Nicola Leone, was awarded the ICLP 2015 Best Paper Prize. In addition to the presentations of accepted papers, the technical program of ICLP 2015 included two panels, on The Future Publication of ICLP Proceedings, chaired by Torsten Schaub, and Teaching Computer Science and Declarative programming in Schools and Universities, chaired by Manuel Hermenegildo.

关键词： logic programming

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Abstract interpretation of temporal concurrent constraint programs

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theory and practice of logic programming 2015年第3期15卷 312-357页

作者： Falaschi, Moreno Olarte, Carlos Palamidessi, Catuscia Univ Siena Dipartimento Ingn Informaz & Sci Matemat I-53100 Siena Italy Pontificia Univ Javeriana Dept Elect & Ciencias Computac Cali Colombia Ecole Polytech INRIA Palaiseau France Ecole Polytech LIX Palaiseau France

Timed Concurrent Constraint programming (tcc) is a declarative model for concurrency offering a logic for specifying reactive systems, i.e., systems that continuously interact with the environment. The universal tcc formalism (utcc) is an extension of tcc with the ability to express mobility. Here mobility is understood as communication of private names as typically done for mobile systems and security protocols. In this paper we consider the denotational semantics for tcc, and extend it to a "collecting" semantics for utcc based on closure operators over sequences of constraints. Relying on this semantics, we formalize a general framework for data flow analyses of tcc and utcc programs by abstract interpretation techniques. The concrete and abstract semantics that we propose are compositional, thus allowing us to reduce the complexity of data flow analyses. We show that our method is sound and parametric with respect to the abstract domain. Thus, different analyses can be performed by instantiating the framework. We illustrate how it is possible to reuse abstract domains previously defined for logic programming to perform, for instance, a groundness analysis for tcc programs. We show the applicability of this analysis in the context of reactive systems. Furthermore, we also make use of the abstract semantics to exhibit a secrecy flaw in a security protocol. We also show how it is possible to make an analysis which may show that tcc programs are suspension-free. This can be useful for several purposes, such as for optimizing compilation or for debugging.

关键词： timed concurrent constraint programming process calculi abstract interpretation denotational semantics reactive systems

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