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检索条件"主题词=Answer Set Programming"

共 869 条记录，以下是171-180 订阅

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Exploiting answer set programming with External Sources for Meta-Interpretive Learning

Exploiting Answer Set Programming with External Sources for ...

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34th International Conference on Logic programming (ICLP)

作者： Kaminski, Tobias Eiter, Thomas Inoue, Katsumi Tech Univ Vienna TU Wien Vienna Austria Natl Inst Informat Tokyo Japan

Meta-Interpretive Learning (MIL) learns logic programs from examples by instantiating meta-rules, which is implemented by the Metagol system based on Prolog. Viewing MIL-problems as combinatorial search problems, they can alternatively be solved by employing answer set programming (ASP), which may result in performance gains as a result of efficient conflict propagation. However, a straightforward ASP-encoding of MIL results in a huge search space due to a lack of procedural bias and the need for grounding. To address these challenging issues, we encode MIL in the HEX-formalism, which is an extension of ASP that allows us to outsource the background knowledge, and we restrict the search space to compensate for a procedural bias in ASP. This way, the import of constants from the background knowledge can for a given type of meta-rules be limited to relevant ones. Moreover, by abstracting from term manipulations in the encoding and by exploiting the HEX interface mechanism, the import of such constants can be entirely avoided in order to mitigate the grounding bottleneck. An experimental evaluation shows promising results.

关键词： Inductive Logic programming Meta-Interpretive Learning answer set programming

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Nurse Scheduling via answer set programming 14th

Nurse Scheduling via Answer Set Programming

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14th International Conference on Logic programming and Nonmonotonic Reasoning (LPNMR)

作者： Dodaro, Carmine Maratea, Marco Univ Genoa DIBRIS Genoa Italy

ISBN: (纸本)9783319616605;9783319616599

The Nurse Scheduling problem (NSP) is a combinatorial problem that consists of assigning nurses to shifts according to given practical constraints. In previous years, several approaches have been proposed to solve different variants of the NSP. In this paper, an ASP encoding for one of these variants is presented, whose requirements have been provided by an Italian hospital. We also design a second encoding for the computation of "optimal" schedules. Finally, an experimental analysis has been conducted on real data provided by the Italian hospital using both encodings. Results are very positive: the state-of-the-art ASP system CLINGO is able to compute one year schedules in few minutes, and it scales well even when more than one hundred nurses are considered.

关键词： answer set programming Scheduling Nurse Scheduling Problem

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Comparing Planning Domain Models Using answer set programming 18th

Comparing Planning Domain Models Using Answer Set Programmin...

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18th European Conference on Logics in Artificial Intelligence (JELIA)

作者： Chrpa, Lukas Dodaro, Carmine Maratea, Marco Mochi, Marco Vallati, Mauro Czech Tech Univ Prague Czech Republic Univ Calabria Arcavacata Di Rende Italy Univ Genoa Genoa Italy Univ Huddersfield Huddersfield England

ISBN: (纸本)9783031436185;9783031436192

Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A critical aspect of domain-independent planning is the domain model, that encodes a formal representation of domain knowledge needed to reason upon a given problem. Despite the crucial role of domain models in automated planning, there is lack of tools supporting knowledge engineering process by comparing different versions of the models, in particular, determining and highlighting differences the models have. In this paper, we build on the notion of strong equivalence of domain models and formalise a novel concept of similarity of domain models. To measure the similarity of two models, we introduce a directed graph representation of lifted domain models that allows to formulate the domain model similarity problem as a variant of the graph edit distance problem. We propose an answer set programming approach to optimally solve the domain model similarity problem, that identifies the minimum number of modifications the models need to become strongly equivalent, and we demonstrate the capabilities of the approach on a range of benchmark models.

关键词： Automated Planning answer set programming Domain Model

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An answer set programming Based Framework for High-Utility Pattern Mining Extended with Facets and Advanced Utility Functions 5th

An Answer Set Programming Based Framework for High-Utility P...

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5th International Joint Conference on Rules and Reasoning (RuleML+RR)

作者： Cauteruccio, Francesco Terracina, Giorgio Univ Calabria DEMACS Arcavacata Di Rende Italy

ISBN: (纸本)9783030911676;9783030911669

In the context of pattern mining, the utility of a pattern can be described as a preference ordering over a choice set;it can be actually assessed from very different perspectives and at different abstraction levels. However, while the topic of High-Utility Pattern Mining (HUPM) has been widely studied, the basic assumption is that each item in a knowledge base is associated with one, static utility. In this paper we introduce, among others, the notion of facets for items, which allows to cope with this limitation and, moreover, we show how a more structured representation of available information, coupled with facets defined also for higher abstraction levels, paves the way to new opportunities for HUPM. In particular, the proposed framework allows to introduce some new advanced classes of utility functions in the detection process, whose relevance is also experimentally evaluated. A real use case on paper reviews is exploited to analyze the potentiality of the proposed framework in knowledge creation and discovery. Given the wide variety of analytical scenarios that can be envisioned in this new setting, we take full advantage of the capabilities of answer set programming and its extensions for a fast encoding and testing of the framework.

关键词： High-utility pattern mining answer set programming Facets Advanced utility functions

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When you must forget: Beyond strong persistence when forgetting in answer set programming

When you must forget: Beyond strong persistence when forgett...

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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： Goncalves, Ricardo Knorr, Matthias Leite, Joao Woltran, Stefan Univ Nova Lisboa NOVA LINCS Lisbon Portugal TU Wien Vienna Austria

Among the myriad of desirable properties discussed in the context of forgetting in answer set programming, strong persistence naturally captures its essence. Recently, it has been shown that it is not always possible to forget a set of atoms from a program while obeying this property, and a precise criterion regarding what can be forgotten has been presented, accompanied by a class of forgetting operators that return the correct result when forgetting is possible. However, it is an open question what to do when we have to forget a set of atoms, but cannot without violating this property. In this paper, we address this issue and investigate three natural alternatives to forget when forgetting without violating strong persistence is not possible, which turn out to correspond to the different possible relaxations of the characterization of strong persistence. Additionally, we discuss their preferable usage, shed light on the relation between forgetting and notions of relativized equivalence established earlier in the context of answer set programming, and present a detailed study on their computational complexity.

关键词： forgetting answer set programming strong equivalence relativized equivalence computational complexity

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User-Defined Smart Contracts Using answer set programming 34th

User-Defined Smart Contracts Using Answer Set Programming

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34th Australasian Joint Conference on Artificial Intelligence (AI)

作者： Purnell, Kevin Schwitter, Rolf Macquarie Univ Sydney NSW Australia

ISBN: (纸本)9783030975463;9783030975456

We present a novel approach to the creation of smart contracts that takes an existing legal document and allows it to be incrementally elaborated into a tested smart contract by domain experts. Smart contracts are currently built with compiled imperative languages, and suffer from lack of agility, elevated risks from errors and security flaws, and high development costs. This paper describes a smart editor that uses a declarative language (answer set programming (ASP)) to represent the business logic of legal documents. The document is incrementally elaborated in a fixed sequence of steps beginning with an ontology discovery step that identifies the explicit and implicit artefacts and applicable constraints. This information is used to generate ASP representations which provide the foundation required for modelling the legal logic. Furthermore, we have achieved the verbalisation of rules built during modelling, and have developed a method of representing artefacts visually which allows logic modelling, model validation and program verification to be visual. During these steps, the original legal document is enhanced with additional embedded information, which results in a tested executable ASP program which can then be used as a smart contract if modifications are made to the blockchain smart contract infrastructure.

关键词： answer set programming Legal Logic Model Validation Ontology Smart Contract Verbalisation Visualisation

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Controlled Natural Language Processing as answer set programming: An Experiment

Controlled Natural Language Processing as Answer Set Program...

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4th International Workshop on Controlled Natural Language (CNL)

作者： Schwitter, Rolf Macquarie Univ Dept Comp Sydney NSW 2109 Australia

ISBN: (纸本)9783319102238

Most controlled natural languages (CNLs) are processed with the help of a pipeline architecture that relies on different software components. We investigate in this paper in an experimental way how well answer set programming (ASP) is suited as a unifying framework for parsing a CNL, deriving a formal representation for the resulting syntax trees, and for reasoning with that representation. We start from a list of input tokens in ASP notation and show how this input can be transformed into a syntax tree using an ASP grammar and then into reified ASP rules in form of a set of facts. These facts are then processed by an ASP meta-interpreter that allows us to infer new knowledge.

关键词： answer set programming Controlled Natural Language Processing Meta-programming

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Synthesizing Imperative Code from answer set programming Specifications 29th

Synthesizing Imperative Code from Answer Set Programming Spe...

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29th International Conference on Logic-Based Program Synthesis and Transformation (LOPSTR)

作者： Varanasi, Sarat Chandra Salazar, Elmer Mittal, Neeraj Gupta, Gopal Univ Texas Dallas Dept Comp Sci Richardson TX 75083 USA

ISBN: (纸本)9783030452599;9783030452605

We consider the problem of obtaining an implementation of an algorithm from its specification. We assume that these specifications are written in answer set programming (ASP). ASP is an ideal formalism for writing specifications due to its highly declarative and expressive nature. To obtain an implementation from its specification, we utilize the operational semantics of ASP implemented in the s(ASP) system. This operational semantics is used to transform the declarative specification written in ASP to obtain an equivalent efficient program that uses imperative control features. This work is inspired by our overarching goal of automatically deriving efficient concurrent algorithms from declarative specifications. This paper reports our first step towards achieving that goal where we restrict ourselves to simple sequential algorithms. We illustrate our ideas through several examples. Our work opens up a new approach to logic-based program synthesis not explored before.

关键词： Program synthesis Program transformation answer set programming Partial evaluation Symbolic execution

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Deep Learning for the Generation of Heuristics in answer set programming: A Case Study of Graph Coloring 16th

Deep Learning for the Generation of Heuristics in Answer Set...

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16th International Conference on Logic programming and Non-Monotonic Reasoning (LPNMR)

作者： Dodaro, Carmine Ilardi, Davide Oneto, Luca Ricca, Francesco Univ Calabria DeMaCS Arcavacata Di Rende Italy Univ Genoa DIBRIS Genoa Italy

ISBN: (纸本)9783031157073;9783031157066

answer set programming (ASP) is a well-established declarative AI formalism for knowledge representation and reasoning. ASP systems were successfully applied to both industrial and academic problems. Nonetheless, their performance can be improved by embedding domain-specific heuristics into their solving process. However, the development of domain-specific heuristics often requires both a deep knowledge of the domain at hand and a good understanding of the fundamental working principles of the ASP solvers. In this paper, we investigate the use of deep learning techniques to automatically generate domain-specific heuristics for ASP solvers targeting the well-known graph coloring problem. Empirical results show that the idea is promising: the performance of the ASP solver WASP can be improved.

关键词： answer set programming deep learning heuristics graph coloring

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Modal Logic S5 in answer set programming with Lazy Creation of Worlds 16th

Modal Logic S5 in Answer Set Programming with Lazy Creation ...

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16th International Conference on Logic programming and Non-Monotonic Reasoning (LPNMR)

作者： Alviano, Mario Batsakis, Sotiris Baryannis, George Univ Calabria Arcavacata Di Rende Italy Tech Univ Crete Iraklion Greece Univ Huddersfield Sch Comp & Engn Huddersfield W Yorkshire England

ISBN: (纸本)9783031157073;9783031157066

Modal logic S5 is used extensively for representing knowledge that includes statements about necessity and possibility, owing to its simplicity in handling chained modal operators. Significant research effort has been devoted in developing efficient reasoning mechanisms over complex S5 formulas, resulting in various solvers taking advantage of the boolean satisfiability problem (SAT). Among them, the most performant solver implements a heuristic for identifying worlds that can be merged, hence reducing the size of SAT instances to be checked. Recently, answer set programming (ASP) has also been considered, and different ASP encodings were proposed and tested, reaching state-of-the-art performance. In particular, a heuristic for identifying the propositional atoms that are relevant in every world resulted in a performance gain in previous experiments. This work addresses the open question of whether the aforementioned two heuristics can be combined, as well as possibly enabling lazy instantiation of the resulting encodings, and what their potential impact is on the performance of the ASP-based solver. Experiments show that lazy creation of worlds provides some further performance gain to the ASP-based solver on the tested instances.

关键词： Modal Logic S5 answer set programming

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