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First-order stable model semantics with intensional functions

ARTIFICIAL INTELLIGENCE 2019年 273卷 56-93页

作者： Bartholomew, Michael Lee, Joohyung Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA

In classical logic, nonBoolean fluents, such as the location of an object, can be naturally described by functions. However, this is not the case in answer set programs, where the values of functions are pre-defined, and nonmonotonicity of the semantics is related to minimizing the extents of predicates but has nothing to do with functions. We extend the first-order stable model semantics by Ferraris, Lee, and Lifschitz to allow intensional functions-functions that are specified by a logic program just like predicates are specified. We show that many known properties of the stable model semantics are naturally extended to this formalism and compare it with other related approaches to incorporating intensional functions. Furthermore, we use this extension as a basis for defining answer set programming Modulo Theories (ASPMT), analogous to the way that Satisfiability Modulo Theories (SMT) is defined, allowing for SMT-like effective first-order reasoning in the context of answer set programming (ASP). Using SMT solving techniques involving functions, ASPMT can be applied to domains containing real numbers and alleviates the grounding problem. We show that other approaches to integrating ASP and CSP/SMT can be related to special cases of ASPMT in which functions are limited to non intensional ones. (C) 2019 Elsevier B.V. All rights reserved.

关键词： answer set programming Intensional functions Satisfiability modulo theories

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Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2019年第1期25卷 438-448页

作者： Moritz, Dominik Wang, Chenglong Nelson, Greg L. Lin, Halden Smith, Adam M. Howe, Bill Heer, Jeffrey Univ Washington Seattle WA 98195 USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA

There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on answer set programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments.

关键词： Automated Visualization Design Perceptual Effectiveness Constraints Knowledge Bases answer set programming

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P-log: refinement and a new coherency condition

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2019年第1-3期86卷 149-192页

作者： Balai, Evgenii Gelfond, Michael Zhang, Yuanlin Texas Tech Univ Lubbock TX 79409 USA

This paper focuses on the investigation and improvement of knowledge representation language P-log that allows for both logical and probabilistic reasoning. We refine the definition of the language by eliminating some ambiguities and incidental decisions made in its original version and slightly modify the formal semantics to better match the intuitive meaning of the language constructs. We also define a new class of coherent (i.e., logically and probabilistically consistent) P-log programs which facilitates their construction and proofs of correctness. There are a query answering algorithm, sound for programs from this class, and a prototype implementation which, due to their size, are not included in the paper. They, however, can be found in the dissertation of the first author.

关键词： answer set programming Probabilistic inference Knowledge representation

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A Brief Survey on Forgetting from a Knowledge Representation and Reasoning Perspective

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KUNSTLICHE INTELLIGENZ 2019年第1期33卷 9-33页

作者： Eiter, Thomas Kern-Isberner, Gabriele TU Wien Inst Logic & Computat Vienna Austria TU Dortmund Dept Comp Sci Dortmund Germany

Forgetting is an ambivalent concept of (human) intelligence. By definition, it is negatively related to knowledge in that knowledge is lost, be it deliberately or not, and therefore, forgetting has not received as much attention in the field of knowledge representation and reasoning (KRR) as other processes with a more positive orientation, like query answering, inference, or update. However, from a cognitive view, forgetting also has an ordering function in the human mind, suppressing information that is deemed irrelevant and improving cognitive capabilities to focus and deal only with relevant aspects of the problem under consideration. In this regard, forgetting is a crucial part of reasoning. This paper collects and surveys approaches to forgetting in the field of knowledge representation and reasoning, highlighting their roles in diverse tasks of knowledge processing, and elaborating on common techniques. We recall forgetting operations for propositional and predicate logic, as well as for answer set programming (as an important representative of nonmonotonic logics) and modal logics. We discuss forgetting in the context of (ir)relevance and (in)dependence, and explicit the role of forgetting for specific tasks of knowledge representation, showing its positive impact on solving KRR problems.

关键词： Forgetting Knowledge representation and reasoning Logic answer set programming Nonmononotonic reasoning Modal logics Interpolation Relevance Independence

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Digital forensics and investigations meet artificial intelligence

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2019年第1-3期86卷 193-229页

作者： Costantini, Stefania De Gasperis, Giovanni Olivieri, Raffaele Univ Aquila Laquila Italy Dipartimento Ingn & Sci Informaz & Matemat Via Vetoio Snc Loc Coppito I-67100 Laquila Italy

In the frame of Digital Forensic (DF) and Digital Investigations (DI), the Evidence Analysis phase has the aim to provide objective data, and to perform suitable elaboration of these data so as to help in the formation of possible hypotheses, which could later be presented as elements of proof in court. The aim of our research is to explore the applicability of Artificial Intelligence (AI) along with computational logic tools - and in particular the answer set programming (ASP) approach - to the automation of evidence analysis. We will show how significant complex investigations, hardly solvable for human experts, can be expressed as optimization problems belonging in many cases to the P or NP complexity classes. All these problems can be expressed in ASP. As a proof of concept, in this paper we present the formalization of realistic investigative cases via simple ASP programs, and show how such a methodology can lead to the formulation of tangible investigative hypotheses. We also sketch a design for a feasible Decision Support System (DSS) especially meant for investigators, based on artificial intelligence tools.

关键词： Digital forensics Digital investigation Artificial intelligence answer set programming Automatic investigation Forensic models

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A Syntactic Operator for Forgetting that Satisfies Strong Persistence

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THEORY AND PRACTICE OF LOGIC programming 2019年第5-6期19卷 1038-1055页

作者： Berthold, Matti Goncalves, Ricardo Knorr, Matthias Leite, Joao Tech Univ Dresden Dresden Germany Univ Leipzig Leipzig Germany Univ Nova Lisboa Dept Informat NOVA LINCS Lisbon Portugal

Whereas the operation of forgetting has recently seen a considerable amount of attention in the context of answer set programming (ASP), most of it has focused on theoretical aspects, leaving the practical issues largely untouched. Recent studies include results about what sets of properties operators should satisfy, as well as the abstract characterization of several operators and their theoretical limits. However, no concrete operators have been investigated. In this paper, we address this issue by presenting the first concrete operator that satisfies strong persistence - a property that seems to best capture the essence of forgetting in the context of ASP - whenever this is possible, and many other important properties. The operator is syntactic, limiting the computation of the forgetting result to manipulating the rules in which the atoms to be forgotten occur, naturally yielding a forgetting result that is close to the original program.

关键词： answer set programming Forgetting Strong Persistence Syntactic Operator

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Better Paracoherent answer sets with Less Resources

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THEORY AND PRACTICE OF LOGIC programming 2019年第5-6期19卷 757-772页

作者： Amendola, Giovanni Dodaro, Carmine Ricca, Francesco Univ Calabria Arcavacata Di Rende Italy

answer set programming (ASP) is a well-established formalism for logic programming. Problem solving in ASP requires to write an ASP program whose answers sets correspond to solutions. Albeit the non-existence of answer sets for some ASP programs can be considered as a modeling feature, it turns out to be a weakness in many other cases, and especially for query answering. Paracoherent answer set semantics extend the classical semantics of ASP to draw meaningful conclusions also from incoherent programs, with the result of increasing the range of applications of ASP. State of the art implementations of paracoherent ASP adopt the semi-equilibrium semantics, but cannot be lifted straightforwardly to compute efficiently the (better) split semi-equilibrium semantics that discards undesirable semi-equilibrium models. In this paper an efficient evaluation technique for computing a split semi-equilibrium model is presented. An experiment on hard benchmarks shows that better paracoherent answer sets can be computed consuming less computational resources than existing methods.

关键词： answer set programming Paracoherent reasoning Semi-equilibrium models

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A core language for fuzzy answer set programming

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2012年第4期53卷 660-692页

作者： Janssen, Jeroen Schockaert, Steven Vermeir, Dirk De Cock, Martine Vrije Univ Brussel Dept Comp Sci B-1050 Brussels Belgium Univ Ghent Dept Appl Math & Comp Sci B-9000 Ghent Belgium

A number of different Fuzzy answer set programming (FASP) formalisms have been proposed in the last years, which all differ in the language extensions they support. In this paper we investigate the expressivity of these frameworks. Specifically we show how a variety of constructs in these languages can be implemented using a considerably simpler core language. These simulations are important as a compact and simple language is easier to implement and to reason about, while an expressive language offers more options when modeling problems. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Fuzzy logic answer set programming Declarative problem solving

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Knowledge representation analysis of graph mining

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2019年第1-3期86卷 21-60页

作者： van der Hallen, Matthias Paramonov, Sergey Janssens, Gerda Denecker, Marc Katholieke Univ Leuven Dept Comp Sci Celestijnenlaan 200ABox 2402 B-3001 Leuven Belgium

This paper analyses the graph mining problem, and the frequent pattern mining task associated with it. In general, frequent pattern mining looks for a graph which occurs frequently within a network or, in the transactional setting, within a dataset of graphs. We discuss this task in the transactional setting, which is a problem of interest in many fields such as bioinformatics, chemoinformatics and social networks. We look at the graph mining problem from a Knowledge Representation point of view, hoping to learn something about support for higher-order logics in declarative languages and solvers. Graph mining is studied as a prototypical problem;it is easily expressible mathematically and exists in many variations. As such, it appears to be a prime candidate for a declarative approach;one would expect this allows for a clear, structured, statement of the problem combined with easy adaptation to changing requirements and variations. Current state-of-the-art KR languages such as IDP and ASP aspire to be practical solvers for such problems (Bruynooghe, Theory Practice Logic Program. (TPLP) 15(6), 783-817 2015). Nevertheless, expressing the graph mining problem in these languages requires unexpectedly complicated and unintuitive encoding techniques. These techniques are in contrast to the ease with which one can transform the mathematical definition of graph mining to a higher-order logic specification, and distract from the problem essentials, complicating possible future adaptation. In this paper, we argue that efforts should be made towards supporting higher-order logic specifications in modern specification languages, without unintuitive and complicated encoding techniques. We argue that this not only makes representation clearer and more susceptible to future adaptation, but might also allow for faster, more competitive solver techniques to be implemented.

关键词： Knowledge representation Higher order Graph mining answer set programming Imperative declarative programming

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Enhancing the scalability of expressive stream reasoning via input-driven parallelization

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SEMANTIC WEB 2019年第3期10卷 457-474页

作者： Pham, Thu-Le Ali, Muhammad Intizar Mileo, Alessandra Natl Univ Ireland Insight Ctr Data Anal IDA Bussiness Pk Galway Ireland Dublin City Univ Insight Ctr Data Analyt Dublin 9 Ireland

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-of-the-art expressive reasoners, limiting their applicability to process streaming information in a scalable way. In this scenario, in order to reduce the amount of data to reason upon at each iteration, we can leverage advances in continuous query processing over Semantic Web streams. Following this principle, in previous work we have combined semantic query processing and non-monotonic reasoning over data streams in the StreamRule system. In the approach, we specifically focused on the scalability of a rule layer based on a fragment of answer set programming (ASP). We recently expanded on this approach by designing an algorithm to analyze input dependency so as to enable parallel execution and combine the results. In this paper, we expand on this solution by providing i) a proof of correctness for the approach, ii) an extensive experimental evaluation for different levels of complexity of the input program, and iii) a clear characterization of all the algorithms involved in generating and splitting the graph and identifying heuristics for node duplication, as well as partitioning the reasoning process via input splitting and combining the results.

关键词： Semantic Web stream reasoning non-monotonic reasoning answer set programming parallel reasoning data partitioning dependency graph

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