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作者： Joao Manuel Gomes Moura NOVA University of Lisbon

学位级别：博士

With the recent development of a new ubiquitous nature of data and the profusity of available knowledge, there is nowadays the need to reason from multiple sources of often incomplete and uncertain knowledge. Our goal was to provide a way to combine declarative knowledge bases – represented as logic programming modules under the answer set semantics – as well as the individual results one already inferred from them, without having to recalculate the results for their composition and without having to explicitly know the original logic programming encodings that produced such results. This posed us many challenges such as how to deal with fundamental problems of modular frameworks for logic programming, namely how to define a general compositional semantics that allows us to compose unrestricted modules. Building upon existing logic programming approaches, we devised a framework capable of composing generic logic programming modules while preserving the crucial property of compositionality, which informally means that the combination of models of individual modules are the models of the union of modules. We are also still able to reason in the presence of knowledge containing incoherencies, which is informally characterised by a logic program that does not have an answer set due to cyclic dependencies of an atom from its default negation. In this thesis we also discuss how the same approach can be extended to deal with probabilistic knowledge in a modular and compositional way. We depart from the Modular Logic programming approach in Oikarinen & Janhunen (2008); Janhunen et al. (2009) which achieved a restricted form of com- positionality of answer set programming modules. We aim at generalising this framework of modular logic programming and start by lifting restrictive conditions that were originally imposed, and use alternative ways of combining these (so called by us) Generalised Modular Logic Programs. We then deal with conﬂicts arising in generalised modular lo

关键词： Logic programming answer set programming Paracoherence Paraconsistency Modular Logic programming Probabilistic Logic programming Bayesian Networks Access Control Policies

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Disjunctive answer set solvers via templates

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THEORY AND PRACTICE OF LOGIC programming 2016年第4期16卷 465-497页

作者： Brochenin, Remi Maratea, Marco Lierler, Yuliya Univ Genoa DIBRIS Viale F Causa 15 I-16145 Genoa Italy Univ Nebraska Dept Comp Sci 6001 Dodge St Omaha NE 68182 USA

answer set programming is a declarative programming paradigm oriented towards difficult combinatorial search problems. A fundamental task in answer set programming is to compute stable models, i.e., solutions of logic programs. answer set solvers are the programs that perform this task. The problem of deciding whether a disjunctive program has a stable model is Sigma(P)(2)-complete. The high complexity of reasoning within disjunctive logic programming is responsible for few solvers capable of dealing with such programs, namely DLV, GNT, CMODELS, clasp and wasp. In this paper, we show that transition systems introduced by Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers can be adapted for disjunctive answer set solvers. Transition systems give a unifying perspective and bring clarity in the description and comparison of solvers. They can be effectively used for analyzing, comparing and proving correctness of search algorithms as well as inspiring new ideas in the design of disjunctive answer set solvers. In this light, we introduce a general template, which accounts for major techniques implemented in disjunctive solvers. We then illustrate how this general template captures solvers DLV, GNT, and CMODELS. We also show how this framework provides a convenient tool for designing new solving algorithms by means of combinations of techniques employed in different solvers.

关键词： answer set programming abstract solvers

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Information diffusion in a multi-social-network scenario: framework and ASP-based analysis

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KNOWLEDGE AND INFORMATION SYSTEMS 2016年第3期48卷 619-648页

作者： Marra, Giuseppe Ursino, Domenico Ricca, Francesco Terracina, Giorgio Engn Tributi SpA Trento Italy Univ Mediterranea Reggio Calabria DIIES Domenico Ursino Via Graziella I-89122 Reggio Di Calabria Italy Univ Calabria DeMaCs Arcavacata Di Rende CS Italy

Information diffusion is a classical problem in social network analysis, which has been largely investigated with reference to single social networks. However, the current scenario is multi-social-network. Here, many social networks coexist and are strictly connected to each other, thanks to those users who join more social networks, acting as bridges among them. But, what happens to information diffusion when passing from a single-social-network context to a multi-social-network scenario? In this paper, we answer this question. In particular, thanks to the definition of a framework for handling these issues and to a large set of experiments, we show that, in this context, new actors and new features play the key roles. We also identify two possible improvements of our framework, namely the management of some "activation nucleuses" (i.e., some starting-node configurations that are likely to improve information diffusion) and the management of topics concerning the information to spread. In these activities, answer set programming provided us with a powerful and flexible tool for an easy setup and implementation of our investigation.

关键词： Social network analysis Logic programming Multi-social-network scenario answer set programming Information diffusion

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Data repair of inconsistent nonmonotonic description logic programs

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ARTIFICIAL INTELLIGENCE 2016年第0期239卷 7-53页

作者： Eiter, Thomas Fink, Michael Stepanova, Dania Vienna Univ Technol Inst Informat Syst Vienna Austria

Combining Description Logic (DL) ontologies and nonmonotonic rules has gained increasing attention in the past decade, due to the growing range of applications of DLs. A well-known proposal for such a combination are non-monotonic DL-programs, which support rule based reasoning on top of DL ontologies in a loose coupling, using a well-defined query interface. However, inconsistency may easily arise as a result of the interaction of the rules and the ontology, such that no answer set (i.e., model) of a DL-program exists;this makes the program useless. To overcome this problem, we present a framework for repairing inconsistencies in DL-programs by exchanging formulas of an ontology formulated DL-Lite(A), which is a prominent DL that allows for tractable reasoning. Viewing the data part of the ontology as a source of inconsistency, we define program repairs and repair answer sets based on them. We analyze the complexity of the notion, and we extend an algorithm for evaluating DL-programs to compute repair answer sets, under optional selection of preferred repairs that satisfy additional constraints. The algorithm induces a generalized ontology repair problem, in which the entailment respectively non-entailment of queries to the ontology, subject to possible updates, must be achieved by a data change. While this problem is intractable in general, we identify several tractable classes of preferred repairs that are useful in practice. For the class of deletion repairs among them, we optimize the algorithm by reducing query evaluation to constraint matching, based on the novel concept of support set, which roughly speaking is a portion of the data from which entailment of an ontology query follows. Our repair approach is implemented within an answer set program system, using a declarative method for repair computation. An experimental evaluation on a suite of benchmark problems shows the effectiveness of our approach and promising results, both regarding performance and qu

关键词： Rules and ontologies answer set programming Description logics Inconsistency management Nonmonotonic reasoning

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Using linear constraints for logic program termination analysis

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THEORY AND PRACTICE OF LOGIC programming 2016年第3期16卷 353-377页

作者： Calautti, Marco Greco, Sergio Molinaro, Cristian Trubitsyna, Irina Univ Calabria DIMES I-87036 Arcavacata Di Rende CS Italy

It is widely acknowledged that function symbols are an important feature in answer set programming, as they make modelling easier, increase the expressive power, and allow us to deal with infinite domains. The main issue with their introduction is that the evaluation of a program might not terminate and checking whether it terminates or not is undecidable. To cope with this problem, several classes of logic programs have been proposed where the use of function symbols is restricted but the program evaluation termination is guaranteed. Despite the significant body of work in this area, current approaches do not include many simple practical programs whose evaluation terminates. In this paper, we present the novel classes of rule-bounded and cycle-bounded programs, which overcome different limitations of current approaches by performing a more global analysis of how terms are propagated from the body to the head of rules. Results on the correctness, the complexity, and the expressivity of the proposed approach are provided.

关键词： answer set programming Function symbols Bottom-up evaluation Program evaluation termination Stable models

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Query answering in resource-based answer set semantics

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THEORY AND PRACTICE OF LOGIC programming 2016年第5-6期16卷 619-635页

作者： Costantini, Stefania Formisano, Andrea Univ Aquila DISIM I-67100 Laquila Italy Univ Perugia DMI GNCS INdAM I-06100 Perugia Italy

In recent work we defined resource-based answer set semantics, which is an extension to answer set semantics stemming from the study of its relationship with linear logic. In fact, the name of the new semantics comes from the fact that in the linear-logic formulation every literal (including negative ones) were considered as a resource. In this paper, we propose a query-answering procedure reminiscent of Prolog for answer set programs under this extended semantics as an extension of XSB-resolution for logic programs with negation.(1) We prove formal properties of the proposed procedure. Under consideration for acceptance in TPLP.

关键词： answer set programming Procedural Semantics Top-down Query-answering

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A systematic analysis of levels of integration between high-level task planning and low-level feasibility checks

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AI COMMUNICATIONS 2016年第2期29卷 319-349页

作者： Erdem, Esra Patoglu, Volkan Schuller, Peter Sabanci Univ Comp Sci & Engn Fac Engn & Nat Sci Istanbul Turkey Sabanci Univ Fac Engn & Nat Sci Mechatron Engn Istanbul Turkey Marmara Univ Dept Comp Engn Istanbul Turkey

We provide a systematic analysis of levels of integration between discrete high-level reasoning and continuous low-level feasibility checks to address hybrid planning problems in robotic applications. We identify four distinct strategies for such an integration: (i) low-level checks are done for all possible cases in advance and the results are used during plan generation;(ii) low-level checks are done exactly when they are needed during the search for a plan;(iii) low-level checks are done after a plan is computed, and if the plan is found infeasible then a new plan is computed;(iv) similar to the previous strategy but the results of previous low-level checks are used during computation of a new plan. We analyze the usefulness of these strategies and their combinations by experiments on hybrid planning problems in different robotic application domains, in terms of computational efficiency and plan quality (relative to its feasibility).

关键词： Task planning motion planning hybrid planning knowledge representation and reasoning answer set programming cognitive robotics experimental analysis

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Domain expansion for ASP-programs with external sources

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ARTIFICIAL INTELLIGENCE 2016年 233卷 84-121页

作者： Eiter, Thomas Fink, Michael Krennwallner, Thomas Redl, Christoph Vienna Univ Technol Inst Informat Syst Favoritenstr 9-11 A-1040 Vienna Austria

answer set programming (ASP) is a popular approach to declarative problem solving which for broader usability has been equipped with external source access. The latter may introduce new constants to the program (known as value invention), which can lead to infinite answer sets and non-termination;to prevent this, syntactic safety conditions on programs are common which considerably limit expressiveness (in particular, recursion). We present liberal domain-expansion (Ide) safe programs, a novel generic class of ASP programs with external source access and value invention that enjoy finite restrictability, i.e., equivalence to a finite ground version. They use term bounding functions as a parametric notion of safety, which can be instantiated with syntactic, semantic or combined safety criteria;this empowers us to generalize and integrate many other notions of safety from the literature, and modular composition of criteria makes future extensions easy. Furthermore, we devise a grounding algorithm for lde-safe programs which in contrast to traditional algorithms can ground any such program directly without the need for program decomposition. While we present our approach on top of a proposed formalism in order to make the formalization precise, the general concepts carry over to related formalisms and important special cases as well. An experimental evaluation of Ide-safety on various applications confirms the practicability of our approach. (C) 2016 Elsevier B.V. All rights reserved.

关键词： answer set programming Knowledge representation formalisms Nonmonotonic reasoning External source access Grounding Computational logic

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Names are not good enough: Reasoning over taxonomic change in the Andropogon complex

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SEMANTIC WEB 2016年第6期7卷 645-667页

作者： Franz, Nico M. Chen, Mingmin Kianmajd, Parisa Yu, Shizhuo Bowers, Shawn Weakley, Alan S. Ludascher, Bertram Arizona State Univ Sch Life Sci POB 874501 Tempe AZ 85287 USA Univ Calif Davis Dept Comp Sci 2063 Kemper Hall1 Shields Ave Davis CA 95616 USA Gonzaga Univ Dept Comp Sci 502 East Boone AveAD Box 26 Spokane WA 99258 USA Univ N Carolina Dept Biol Coker Hall120 South Rd Chapel Hill NC 27599 USA Univ Illinois Grad Sch Lib & Informat Sci 510 East Daniel St Champaign IL 61820 USA

We present a novel, logic-based solution to the challenge of reconciling the meanings of taxonomic names across multiple biological taxonomies. The challenge arises due to limitations inherent in using type-anchored taxonomic names as identifiers of granular semantic similarities and differences being expressed in original and revised taxonomic classifications. We address this challenge through: (1) the use of taxonomic concept labels - thereby individuating name usages according to particular sources and allowing each taxonomy to be recognized separately;(2) sets of user-provided Region Connection Calculus articulations among concepts (RCC-5: congruence, proper inclusion, inverse proper inclusion, overlap, exclusion);and (3) the use of an answer set programming-based reasoning toolkit that ingests these constraints to infer and visualize consistent multi-taxonomy alignments. The feasibility of this approach is demonstrated with a use case involving pairwise alignments of 11 non-congruent classifications of Eastern United States grass entities variously assigned to the Andropogon glomeratus-virginicus 'complex' over an interval of 126 years. Analyses of name: meaning identity reveal that, on average, taxonomic names are reliable identifiers of taxonomic non-/congruence for approximately 60% of the 127 merge regions obtained in 12 pairwise alignments. The name: meaning cardinality over the entire time interval ranges from 1: 6 to 4: 1, with only 1: 36 names attaining the semantically ideal 1:1 ratio. We discuss the applicability of the RCC-5 alignment approach in the context of achieving logic-based integration of non-/congruent taxonomic concept hierarchies in dynamic biodiversity data environments.

关键词： answer set programming biodiversity data integration biological nomenclature knowledge representation and reasoning name: meaning cardinality ontology alignment region connection calculus systematics taxonomic concepts visualization

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On abstract modular inference systems and solvers

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ARTIFICIAL INTELLIGENCE 2016年 236卷 65-89页

作者： Lierler, Yuliya Truszczynski, Miroslaw Univ Nebraska Dept Comp Sci Omaha NE 68182 USA Univ Kentucky Dept Comp Sci Lexington KY 40506 USA

Integrating diverse formalisms into modular knowledge representation systems offers increased expressivity, modeling convenience, and computational benefits. We introduce the concepts of abstract inference modules and abstract modular inference systems to study general principles behind the design and analysis of model generating programs, or solvers, for integrated multi-logic systems. We show how modules and modular systems give rise to transition graphs, which are a natural and convenient representation of solvers, an idea pioneered by the SAT community. These graphs lend themselves well to extensions that capture such important solver design features as learning. In the paper, we consider two flavors of learning for modular formalisms, local and global. We illustrate our approach by showing how it applies to answer set programming, propositional logic, multi-logic systems based on these two formalisms and, more generally, to satisfiability modulo theories. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Knowledge representation Model-generation Automated reasoning and inference SAT solving answer set programming

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