This note is about the relationship between two theories of negation as failure -- one based on program completion, the other based on stable models, or answer sets. Francois Fages showed that if a logic program satisfies a certain syntactic condition, which is now called ``tightness,'' then its stable models can be characterized as the models of its completion. We extend the definition of tightness and Fages' theorem to programs with nested expressions in the bodies of rules, and study tight logic programs containing the definition of the transitive closure of a predicate.
ISTI - CNR（意大利国家研究委员会信息科学与技术研究所）；CAIR - University of Cape Town（开普敦大学CAIR）
Lehmann and Magidor's rational closure is acknowledged as a landmark in the field of non-monotonic logics and it has also been re-formulated in the context of Description Logics (DLs). We show here how to model a rational form of entailment for expressive DLs, such as SROIQ, providing a novel reasoning procedure that compiles a non-monotone DL knowledge base into a description logic program (dl-program).
ICLP is the premier international event for presenting research in logic programming. Contributions to ICLP 2022 were sought in all areas of logic programming, including but not limited to: Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge representation. Languages issues: Concurrency, Objects, Coordination, Mobility, Higher order, Types, Modes, Assertions, Modules, Meta-programming, Logic-based domain-specific languages, Programming techniques. Programming support: Program analysis, Transformation, Validation, Verification, Debugging, Profiling, Testing, Execution visualiz…
ICLP is the premier international event for presenting research in logic programming. Contributions to ICLP 2021 were sought in all areas of logic programming, including but not limited to: Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge representation. Languages issues: Concurrency, Objects, Coordination, Mobility, Higher order, Types, Modes, Assertions, Modules, Meta-programming, Logic-based domain-specific languages, Programming techniques. Programming support: Program analysis, Transformation, Validation, Verification, Debugging, Profiling, Testing, Execution visualiz…
The CIFF Proof Procedure for Abductive Logic Programming with Constraints: Theory, Implementation and Experiments
Università di Pisa（比萨大学）；Imperial College London（帝国理工学院）；University of Amsterdam（阿姆斯特丹大学）
给出了带约束的溯因逻辑程序设计的CIFF证明过程，并证明了其正确性。CIFF是溯因逻辑编程的IFF证明过程的扩展，放松了对变量量化（允许条件）的原始限制，并结合了约束求解器来处理约束逻辑编程中的数值约束。最后，我们描述了CIFF系统，将其与最先进的溯因系统和答案集求解器进行比较，并展示了如何使用它来编程一些应用程序。(To《逻辑程序设计理论与实践》（Theory and Practice of Logic Programming，TPLP）
National and Kapodistrian University of Athens（雅典国立卡波迪斯特里安大学）；University of Ioannina（约阿尼纳大学）
Logic Programs with Ordered Disjunction (LPODs) extend classical logic programs with the capability of expressing preferential disjunctions in the heads of program rules. The initial semantics of LPODs, although simple and quite intuitive, is not purely model-theoretic. A consequence of this is that certain properties of programs appear non-trivial to formalize in purely logical terms. An example of this state of affairs is the characterization of the notion of strong equivalence for LPODs. Although the results of Faber et al. (2008) are accurately developed, they fall short of characterizing …
Recent advances in neural symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard. We propose DeepStochLog, an alternative neural symbolic framework based on stochastic definite clause grammars, a type of stochastic logic program, which defines a probability distribution over possible derivations. More specifically, we introduce neural grammar rules into stochastic definite clause grammars to cr…
Hybrid probabilistic logic programs can represent several scenarios thanks to the expressivity of Logic Programming extended with facts representing discrete and continuous distributions. The semantics for this type of programs is crucial since it ensures that a probability can be assigned to every query. Here, following one recent semantics proposal, we illustrate a concrete syntax, and we analyse the syntactic requirements needed to preserve the well-definedness.
University of Nebraska Omaha（内布拉斯加大学奥马哈分校）；IRIT（图卢兹计算机科学研究所）；Université de Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）
In this work, we show that both logic programming and abstract argumentation frameworks can be interpreted in terms of Nelson's constructive logic N4. We do so by formalizing, in this logic, two principles that we call non-contradictory inference and strengthened closed world assumption: the first states that no belief can be held based on contradictory evidence while the latter forces both unknown and contradictory evidence to be regarded as false. Using these principles, both logic programming and abstract argumentation frameworks are translated into constructive logic in a modular way and u…
The stable model semantics had been recently generalized to non-Herbrand structures by several works, which provides a unified framework and solid logical foundations for answer set programming. This paper focuses on the expressiveness of normal and disjunctive programs under the general stable model semantics. A translation from disjunctive programs to normal programs is proposed for infinite structures. Over finite structures, some disjunctive programs are proved to be intranslatable to normal programs if the arities of auxiliary predicates and functions are bounded in a certain way. The equ…
University of Edinburgh（爱丁堡大学）；Alan Turing Institute（艾伦·图灵研究所）
Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm's superiority over another. However, when it comes to inference algorithms for probabilistic logic programs, experimental evaluations are limited to only a few programs. Existing methods to generate random logic programs are limited to propositional programs and often impose stringent syntactic restrictions. We present a novel approach to generating random logic programs and random probabilistic logic programs using constraint programming, introducing a new constraint …
Answer set programming is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing different knowledge representation formalisms. Frequently, several related and yet substantially different answer set programs exist for a given problem. Sometimes these encodings may display significantly different performance. Uncovering precise formal links between these programs is often important and yet far from trivial. This paper presents formal results carefully relating a number of interesting program rewritings. It also provides the proof of corre…
Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Contributions are sought in all areas of logic programming, including but not restricted to: Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge representation. Languages: Concurrency, Objects, Coordination, Mobility, Higher Order, Types, Modes, Assertions, Modules, Meta-programming, Logic-based domain-specific languages, Programming Techniques. Declarative programming: Declarative program development, Analysis, Type and mode infere…
Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Contributions are solicited in all areas of logic programming and related areas, including but not restricted to: - Foundations: Semantics, Formalisms, Answer-Set Programming, Non-monotonic Reasoning, Knowledge Representation. - Declarative Programming: Inference engines, Analysis, Type and mode inference, Partial evaluation, Abstract interpretation, Transformation, Validation, Verification, Debugging, Profiling, Testing, Logic-based domain-specific …
Sabanci University（萨班哲大学）；Università di Perugia（佩鲁贾大学）；Universitat Politècnica de València（瓦伦西亚理工大学）；NVIDIA Corporation（英伟达公司）
We are proud to introduce this special issue of Theory and Practice of Logic Programming (TPLP), dedicated to the regular papers accepted for the 35th International Conference on Logic Programming (ICLP). The ICLP meetings started in Marseille in 1982 and since then constitute the main venue for presenting and discussing work in the area of logic programming. Under consideration for acceptance in TPLP.
Embedding Description Logic Programs into Default Logic
Guizhou University（贵州大学）；University of Alberta（阿尔伯塔大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Technische Universität Wien（维也纳工业大学）
Eiter { \em et al.}被认为是一个突出的形式主义集成规则和本体知识库。一个令人感兴趣的问题是dl程序是否可以用非单调逻辑的一般形式来描述。本文研究了在缺省逻辑中嵌入dl程序的可能性。我们证明了强和弱答案集语义下的dl-程序可以通过结合两种翻译嵌入到缺省逻辑中，其中一种翻译从非单调dl-原子中消除了约束算子，另一种翻译将dl-程序转化为缺省理论。对于没有非单调dl原子但带有否定失败算子的dl程序，我们的嵌入是多项式的、忠实的和模块化的。此外，我们的默认逻辑编码可以以一种简单的方式扩展，以捕获最近提出的弱良好支持的答案集语义，为任意的dl程序。这些结果加强了默认逻辑可以作为一个富有成效的基础查询为基础的方法，整合本体和规则的论点。缺省逻辑以其简单的语法和直观的语义，加上可用的计算结果，可以被认为是一个有吸引力的方法集成的本体和规则。
This volume contains the papers presented at Arg-LPNMR 2016: First International Workshop on Argumentation in Logic Programming and Nonmonotonic Reasoning held on July 8-10, 2016 in New York City, NY.
Argumentative Characterizations of (Extended) Disjunctive Logic Programs
Department of Computer Science, Open Universiteit, the Netherlands（开放大学计算机科学系）；Department of Computer Science, University of Cape Town, South-Africa（开普敦大学计算机科学系）；School of Computer Science, Tel-Aviv Academic College, Israel（特拉维夫学术学院计算机科学学院）
In previous work, we proposed a logic-based framework in which computation is the execution of actions in an attempt to make reactive rules of the form if antecedent then consequent true in a canonical model of a logic program determined by an initial state, sequence of events, and the resulting sequence of subsequent states. In this model-theoretic semantics, reactive rules are the driving force, and logic programs play only a supporting role. In the canonical model, states, actions and other events are represented with timestamps. But in the operational semantics, for the sake of efficiency,…
Probabilistic logic programming is a major part of statistical relational artificial intelligence, where approaches from logic and probability are brought together to reason about and learn from relational domains in a setting of uncertainty. However, the behaviour of statistical relational representations across variable domain sizes is complex, and scaling inference and learning to large domains remains a significant challenge. In recent years, connections have emerged between domain size dependence, lifted inference and learning from sampled subpopulations. The asymptotic behaviour of stati…
