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33rd international conference on logic programming colocated with the 23rd international conference on Principles and Practice of Constraint programming / 20th international conference on theory and Applications of Satisfiability Testing

作者： Shakerin, Farhad Salazar, Elmer Gupta, Gopal Univ Texas Dallas Richardson TX 75083 USA

In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories. Default logic is what humans employ in common-sense reasoning. therefore, learned default theories are better understood by humans. In this paper, we present new algorithms to learn default theories in the form of non-monotonic logic programs. Experiments reported in this paper show that our algorithms are a significant improvement over traditional approaches based on inductive logic programming. Under consideration for acceptance in TPLP.

关键词： inductive logic programming non-monotonic logic programming default reasoning common-sense reasoning machine learning

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Computing LP^MLN using ASP and MLN solvers

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thEORY AND PRACTICE OF logic programming 2017年第5-6期17卷 942-960页

作者： Lee, Joohyung Talsania, Samidh Wang, Yi Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA

LPMLN is a recent addition to probabilistic logic programming languages. Its main idea is to overcome the rigid nature of the stable model semantics by assigning a weight to each rule in a way similar to Markov logic is defined. We present two implementations of LPMLN, LPMLN2ASP and LPMLN2MLN. System LPMLN2ASP translates LPMLN programs into the input language of answer set solver CLINGO, and using weak constraints and stable model enumeration, it can compute most probable stable models as well as exact conditional and marginal probabilities. System LPMLN2MLN translates LPMLN programs into the input language of Markov logic solvers, such as alchemy, TUFFY, and ROCKIT, and allows for performing approximate probabilistic inference on LPMLN programs. We also demonstrate the usefulness of the LPMLN systems for computing other languages, such as ProbLog and Pearl's Causal Models, that are shown to be translatable into LPMLN.

关键词： Answer Set programming Markov logic LPMLN

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the intricacies of three-valued extensional semantics for higher-order logic programs

The intricacies of three-valued extensional semantics for hi...

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作者： Rondogiannis, Panos Symeonidou, Ioanna Univ Athens Athens Greece

M. Bezem defined an extensional semantics for positive higher-order logic programs. Recently, it was demonstrated by Rondogiannis and Symeonidou that Bezem's technique can be extended to higher-order logic programs with negation, retaining its extensional properties, provided that it is interpreted under a logic with an infinite number of truth values. Rondogiannis and Symeonidou also demonstrated that Bezem's technique, when extended under the stable model semantics, does not in general lead to extensional stable models. In this paper, we consider the problem of extending Bezem's technique under the well-founded semantics. We demonstrate that the well-founded extension fails to retain extensionality in the general case. On the positive side, we demonstrate that for stratified higher-order logic programs, extensionality is indeed achieved. We analyze the reasons of the failure of extensionality in the general case, arguing that a three-valued setting cannot distinguish between certain predicates that appear to have a different behaviour inside a program context, but which happen to be identical as three-valued relations.

关键词： Extensional higher-order logic programming Negation in logic programming

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logic programming approaches for routing fault-free and maximally parallel wavelength-routed optical networks-on-chip (Application paper)

Logic programming approaches for routing fault-free and maxi...

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作者： Gavanelli, Marco Nonato, Maddalena Peano, Andrea Bertozzi, Davide Ferrara Univ Dept Engn Ferrara Italy QuanTek Bologna Italy

One promising trend in digital system integration consists of boosting on-chip communication performance by means of silicon photonics, thus materializing the so-called Optical Networkson- Chip. Among them, wavelength routing can be used to route a signal to destination by univocally associating a routing path to the wavelength of the optical carrier. Such wavelengths should be chosen so to minimize interferences among optical channels and to avoid routing faults. As a result, physical parameter selection of such networks requires the solution of complex constrained optimization problems. In previous work, published in the proceedings of the international conference on Computer-Aided Design, we proposed and solved the problem of computing the maximum parallelism obtainable in the communication between any two endpoints while avoiding misrouting of optical signals. the underlying technology, only quickly mentioned in that paper, is Answer Set programming. In this work, we detail the Answer Set programming approach we used to solve such problem. Another important design issue is to select the wavelengths of optical carriers such that they are spread across the available spectrum, in order to reduce the likelihood that, due to imperfections in the manufacturing process, unintended routing faults arise. We show how to address such problem in Constraint logic programming on Finite Domains.

关键词： Answer set programming logic programming applications Optical networks-on-chip Constrained optimization Constraint logic programming on finite domains

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Productive corecursion in logic programming

Productive corecursion in logic programming

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作者： Komendantskaya, Ekaterina Li, Yue Heriot Watt Univ Edinburgh Midlothian Scotland

logic programming is a Turing complete language. As a consequence, designing algorithms that decide termination and non-termination of programs or decide inductive/ coinductive soundness of formulae is a challenging task. For example, the existing state-of-the-art algorithms can only semi-decide coinductive soundness of queries in logic programming for regular formulae. Another, less famous, but equally fundamental and important undecidable property is productivity. If a derivation is infinite and coinductively sound, we may ask whether the computed answer it determines actually computes an infinite formula. If it does, the infinite computation is productive. this intuition was first expressed under the name of computations at infinity in the 80s. In modern days of the Internet and stream processing, its importance lies in connection to infinite data structure processing. Recently, an algorithm was presented that semi-decides a weaker property -of productivity of logic programs. A logic program is productive if it can give rise to productive derivations. In this paper, we strengthen these recent results. We propose a method that semi-decides productivity of individual derivations for regular formulae. thus, we at last give an algorithmic counterpart to the notion of productivity of derivations in logic programming. this is the first algorithmic solution to the problem since it was raised more than 30 years ago. We also present an implementation of this algorithm.

关键词： Horn clauses (co) recursion (co) induction infinite term trees productivity

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Inspecting Maude variants with GLINTS

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thEORY AND PRACTICE OF logic programming 2017年第5-6期17卷 689-707页

作者： Alpuente, M. Escobar, S. Sapina, J. Cuenca-Ortega, A. Univ Politecn Valencia DSIC ELP Valencia Spain Univ Guayaquil Guayaquil Ecuador

this paper introduces GLINTS, a graphical tool for exploring variant narrowing computations in Maude. the most recent version of Maude, version 2.7.1, provides quite sophisticated unification features, including order-sorted equational unification for convergent theories modulo axioms such as associativity, commutativity, and identity. this novel equational unification relies on built-in generation of the set of variants of a term t, i.e., the canonical form of t sigma for a computed substitution sigma. Variant generation relies on a novel narrowing strategy called folding variant narrowing that opens up new applications in formal reasoning, theorem proving, testing, protocol analysis, and model checking, especially when the theory satisfies the finite variant property, i.e., there is a finite number of most general variants for every term in the theory. However, variant narrowing computations can be extremely involved and are simply presented in text format by Maude, often being too heavy to be debugged or even understood. the GLINTS system provides support for (i) determining whether a given theory satisfies the finite variant property, (ii) thoroughly exploring variant narrowing computations, (iii) automatic checking of node embedding and closedness modulo axioms, and (iv) querying and inspecting selected parts of the variant trees.

关键词： Rewriting logic Narrowing Variant Maude Embedding Finite Variant Property

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Applications of the theory of Fuzzy Sets in Complex Control Systems 20

Applications of the Theory of Fuzzy Sets in Complex Control ...

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20th IEEE international conference on Soft Computing and Measurements (SCM)

作者： Logvinov, S. S. Univ Manchester Math Finance Manchester Lancs England

ISBN: (纸本)9781538618103

Despite the fact that for the first time odd sets were mentioned in 1965, the theory of fuzzy sets does not lose its relevance. the fact that their values are combined into an infinite set allows more detailed description of the models and obtaining accurate data. the theory of fuzzy sets gives a lot of scope for creativity both in operator activity, namely in writing rules for logical-linguistic models and interpretation of results, and for programming, namely, writing approximations for the values of fuzzy sets. Moreover, the convenience and relative simplicity of their use help to make managerial decisions in places that are difficult to access for powerful computers.

关键词： measurement fuzzy set logic and argumentation modeling of systems

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Probabilistic Inductive logic programming on the Web 20th

Probabilistic Inductive Logic Programming on the Web

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20th international conference on Knowledge Engineering and Knowledge Management (EKAW)

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

ISBN: (纸本)9783319586946;9783319586939

Probabilistic Inductive logic programming (PILP) is gaining attention for its capability of modeling complex domains containing uncertain relationships among entities. Among PILP systems, cplint provides inference and learning algorithms competitive with the state of the art. Besides parameter learning, cplint provides one of the few structure learning algorithms for PLP, SLIPCOVER. Moreover, an online version was recently developed, cplint on SWISH, that allows users to experiment with the system using just a web browser. In this demo we illustrate cplint on SWISH concentrating on structure learning with SLIPCOVER. cplint on SWISH also includes many examples and a step-by-step tutorial.

关键词： Learning algorithms

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Inspecting Maude variants with GLINTS

<i>Inspecting Maude variants with</i> GLINTS

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作者： Alpuente, M. Escobar, S. Sapina, J. Cuenca-Ortega, A. Univ Politecn Valencia DSIC ELP Valencia Spain Univ Guayaquil Guayaquil Ecuador

关键词： Rewriting logic Narrowing Variant Maude Embedding Finite Variant Property

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20th international conference on theory and Applications of Satisfiability Testing, SAT 2017

20th International Conference on Theory and Applications of ...

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20th international conference on theory and Applications of Satisfiability Testing, SAT 2017

ISBN: (纸本)9783319662626

the proceedings contain 30 papers. the special focus in this conference is on theory and Applications of Satisfiability Testing. the topics include: Backdoor treewidth for SAT;new width parameters for model counting;hard satisfiable formulas for splittings by linear combinations;on the community structure of bounded model checking SAT problems;symmetric explanation learning;an adaptive prefix-assignment technique for symmetry reduction;an empirical study of branching heuristics through the lens of global learning rate;coverage-based clause reduction heuristics for CDCL solvers;on tackling the limits of resolution in SAT solving;improving MCS enumeration via caching;introducing Pareto minimal correction subsets;a distributed version of SYRUP;a propagation rate based splitting heuristic for divide-and-conquer solvers;shortening QBF proofs with dependency schemes;a little blocked literal goes a long way;dependency learning for QBF;a resolution-style proof system for DQBF;from DQBF to QBF by dependency elimination;theory refinement for program verification;on simplification of formulas with unconstrained variables and quantifiers;a benders decomposition approach to deciding modular linear integer arithmetic;SAT-based local improvement for finding tree decompositions of small width;a lower bound on CNF encodings of the at-most-one constraint;SAT-encodings for special treewidth and pathwidth;an extended maxSAT preprocessor and a generator of crafted benchmarks.

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