Logic Programming

Recent functional logic languages such as Curry and Toy combine lazy functional programming with logic programming features including logic variables, non-determinism, unification, narrowing, fair search, concurrency, and residuation. In this paper, we show how to extend a conventional interpreter for a lazy functional language to handle these features by adding support for reference cells, process-like and thread-like concurrency mechanisms, and a novel form of multi-versioned store. Our interpretation scheme is practical, and can be easily extended to perform compilation. The language specified by our interpreter is designed so that programs are deterministic in a novel and useful sense.

A formal model for similarity-based fuzzy unification in multi-adjoint logic programs is presented. On this computational model, a similarity-based unification approach is constructed by simply adding axioms of fuzzy similarities and using classical crisp unification which provides a semantic framework for logic programming with different notions of similarity. Open access under CC BY-NC-ND license.

In this paper, we investigate the notion of error correction memory applied to technical texts. The main purpose is to introduce flexibility and context sensitivity in the detection and the correction of errors related to Constrained Natural Language (CNL) principles. This is realized by enhancing error detection paired with relatively generic correction patterns and contextual correction recommendations. Patterns are induced from previous corrections made by technical writers for a given type of text. The impact of such an error correction memory is also investigated from the point of view of the technical writer's cognitive activity. The notion of error correction memory is developed within the framework of the LELIE project an experiment is carried out on the case of fuzzy lexical items and negation, which are both major problems in technical writing. Language processing and knowledge representation aspects are developed together with evaluation directions.

In this paper, we investigate the notion of error correction memory applied to technical texts. The main purpose is to introduce flexibility and context sensitivity in the detection and the correction of errors related to Constrained Natural Language (CNL) principles. This is realized by enhancing error detection paired with relatively generic correction patterns and contextual correction recommendations. Patterns are induced from previous corrections made by technical writers for a given type of text. The impact of such an error correction memory is also investigated from the point of view of the technical writer's cognitive activity. The notion of error correction memory is developed within the framework of the LELIE project an experiment is carried out on the case of fuzzy lexical items and negation, which are both major problems in technical writing. Language processing and knowledge representation aspects are developed together with evaluation directions.

We discuss how over the last 30 to 50 years, Artificial Intelligence (AI) systems that focused only on data have been handicapped, and how knowledge has been critical in developing smarter, intelligent, and more effective systems. In fact, the vast progress in AI can be viewed in terms of the three waves of AI as identified by DARPA. During the first wave, handcrafted knowledge has been at the center-piece, while during the second wave, the data-driven approaches supplanted knowledge. Now we see a strong role and resurgence of knowledge fueling major breakthroughs in the third wave of AI underpinning future intelligent systems as they attempt human-like decision making, and seek to become trusted assistants and companions for humans. We find a wider availability of knowledge created from diverse sources, using manual to automated means both by repurposing as well as by extraction. Using knowledge with statistical learning is becoming increasingly indispensable to help make AI systems more transparent and auditable. We will draw a parallel with the role of knowledge and experience in human intelligence based on cognitive science, and discuss emerging neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining capabilities of the data-intensive statistical AI systems with those of symbolic AI systems, resulting in more capable AI systems that support more human-like intelligence.

The problem of forward abstract interpretation of normal logic programs has not been formally addressed in the literature although negation as failure is dealt with through the built-in predicate ! in the way it is implemented in Prolog. This paper proposes a solution to this problem by deriving two generic fixed-point abstract semantics F ♭ P and F ⋄ P for forward abstract interpretation of normal logic programs. F ♭ P is intended for inferring data descriptions for edges in the program graph where an edge denotes the possibility that the control of execution transfers from its source program point to its destination program point. F ⋄ P is derived from F ♭ P and is intended for inferring data descriptions for textual program points.

On the one hand, the olive oils’ quality is fixed by the region in which it is produced, the olive variety, the year of production, the degree of maturation, the extraction and the preservation processes, i.e., the olive oils` quality is assessed by two types of analyzes, the organoleptic and the biochemical ones. On the other hand, there are no studies linking analytical parameters to sensory data due to the nonlinearity that exists between these two types of variables. The present study aims to provide an answer to these problems by modeling the causal processes used at different types of olive and olive oil production. To this end, we called at an Artificial Neural Network approach to problem solving once it provides a way to handle the various stages of ripeness of the olives and olive oil production, by allowing one to deal with incomplete, unknown or even self-contradictory information or knowledge

Gavrila Nathania Panjaitan (4233311004) Mastri Angely Br. Sitanggang (4233111061) Najwa Azzahra (4233111045) Rfki Aidil Fikri (4233111018) Sevrianna Simarmata (4232411001) KELAS: PSPM 23 D PENDIDIKAN MATEMATIKA FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNIVERSITAS NEGERI MEDAN 2025 i Puji dan syukur kami panjatkan ke hadirat Tuhan Yang Maha Esa atas rahmat dan karunia-Nya sehingga kami dapat menyelesaikan makalah ini yang membahas analisis while loop dalam validasi input pengguna. Makalah ini disusun sebagai bagian dari mata kuliah Bahasa Pemrograman untuk memahami bagaimana struktur perulangan while dapat digunakan secara efektif dalam memastikan validitas data yang dimasukkan oleh pengguna dalam suatu program. Dalam makalah ini, kami akan mengulas konsep dasar while loop, cara penerapannya dalam validasi input, serta beberapa contoh kasus yang menunjukkan bagaimana teknik ini digunakan dalam pemrograman. Pemahaman yang baik tentang perulangan while sangat penting bagi pengembang perangkat lunak, karena validasi input yang baik akan meningkatkan keandalan dan keamanan aplikasi yang dibuat. Kami menyadari bahwa dalam penyusunan makalah ini masih terdapat kekurangan, baik dalam materi maupun penyajiannya. Oleh karena itu, kami mengharapkan saran dan masukan yang membangun agar makalah ini dapat lebih baik di masa depan. Semoga makalah ini bermanfaat bagi pembaca, khususnya mahasiswa yang sedang mempelajari konsep perulangan dalam pemrograman.

Puji dan syukur penulis panjatkan kepada Tuhan Yang Maha Esa, karena atas berkat dan rahmat-Nya yang diberikan kepada penulis sehingga penulis dapat menyelesaikan tugas mini riset ini dengan baik sesuai arahan dan tepat pada waktunya. Laporan ini dibuat untuk memenuhi tugas mini riset pada mata kuliah Bahasa Pemrograman. Tak lupa penulis ucapkan terimakasih kepada dosen pengampu mata kuliah Bahasa Pemrograman, Bapak Denny Haris, S.Si., M.Pd. yang telah memberikan bimbingan kepada penulis dalam menyelesaikan tugas ini. Penulis menyadari dalam penyusunan laporan ini, masih terdapat banyak kekurangan sehingga hasil yang diperoleh masih jauh dari sempurna. Oleh sebab itu, penulis mengharapkan saran dan kritik yang membangun dari pembaca. Akhir kata penulis ucapkan terima kasih, semoga laporan ini dapat memberikan manfaat dan bisa menambah pengetahuan kepada penulis dan pembaca.

Puji dan syukur penulis panjatkan kepada Tuhan Yang Maha Esa, karena atas berkat dan rahmat-Nya yang diberikan kepada penulis sehingga penulis dapat menyelesaikan tugas mini riset ini dengan baik sesuai arahan dan tepat pada waktunya. Laporan ini dibuat untuk memenuhi tugas mini riset pada mata kuliah Bahasa Pemrograman. Tak lupa penulis ucapkan terimakasih kepada dosen pengampu mata kuliah Bahasa Pemrograman, Bapak Denny Haris, S.Si., M.Pd. yang telah memberikan bimbingan kepada penulis dalam menyelesaikan tugas ini. Penulis menyadari dalam penyusunan laporan ini, masih terdapat banyak kekurangan sehingga hasil yang diperoleh masih jauh dari sempurna. Oleh sebab itu, penulis mengharapkan saran dan kritik yang membangun dari pembaca. Akhir kata penulis ucapkan terima kasih, semoga laporan ini dapat memberikan manfaat dan bisa menambah pengetahuan kepada penulis dan pembaca.

Global SLS-resolution is a well-known procedural semantics for top-down computation of queries under the well-founded model. It inherits from SLDNF-resolution the linearity property of derivations, which makes it easy and efficient to implement using a simple stack-based memory structure. However, like SLDNF-resolution it suffers from the problem of infinite loops and redundant computations. To resolve this problem, in this paper we develop a new procedural semantics, called SLTNF-resolution, by enhancing Global SLS-resolution with loop cutting and tabling mechanisms. SLTNFresolution is sound and complete w.r.t. the well-founded semantics for logic programs with the bounded-term-size property, and is superior to existing linear tabling procedural semantics such as SLT-resolution.

Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they generate cyclic influences, which are disallowed in Bayesian networks. Therefore, in existing PLP approaches logic programs with recursive loops are considered to be problematic and thus are excluded. In this paper, we propose an approach that makes use of recursive loops to build a stationary dynamic Bayesian network. Our work stems from an observation that recursive loops in a logic program imply a time sequence and thus can be used to model a stationary dynamic Bayesian network without using explicit time parameters. We introduce a Bayesian knowledge base with logic clauses of the form A ← A 1 , ..., A l , true, Context, T ypes, which naturally represents the knowledge that the A i s have direct influences on A in the context Context under the type constraints T ypes. We then use the wellfounded model of a logic program to define the direct influence relation and apply * A preliminary version appears in the 15th International Conference on Inductive Logic Programming. SLG-resolution to compute the space of random variables together with their parental connections. We introduce a novel notion of influence clauses, based on which a declarative semantics for a Bayesian knowledge base is established and algorithms for building a two-slice dynamic Bayesian network from a logic program are developed.

We introduce a method of verifying termination of logic programs with respect to concrete queries (instead of abstract query patterns). A necessary and sufficient condition is established and an algorithm for automatic verification is developed. In contrast to existing query pattern-based approaches, our method has the following features: (1) It applies to all general logic programs with non-floundering queries. (2) It is very easy to automate because it does not need to search for a level mapping or a model, nor does it need to compute an interargument relation based on additional mode or type information. (3) It bridges termination analysis with loop checking, the two problems that have been studied separately in the past despite their close technical relation with each other.

We present a new characterization of termination of general logic programs. Most existing termination analysis approaches rely on some static information about the structure of the source code of a logic program, such as modes/types, norms/level mappings, models/interargument relations, and the like. We propose a dynamic approach that employs some key dynamic features of an infinite (generalized) SLDNF-derivation, such as repetition of selected subgoals and recursive increase in term size. We also introduce a new formulation of SLDNF-trees, called generalized SLDNF-trees. Generalized SLDNF-trees deal with negative subgoals in the same way as Prolog and exist for any general logic programs.

Infinite loops and redundant computations are long recognized open problems in Prolog. Two ways have been explored to resolve these problems: loop checking and tabling. Loop checking can cut infinite loops, but it cannot be both sound and complete even for function-free logic programs. Tabling seems to be an effective way to resolve infinite loops and redundant computations. However, existing tabulated resolutions, such as OLDT-resolution, SLG-resolution, and Tabulated SLS-resolution, are non-linear because they rely on the solution-lookup mode in formulating tabling. The principal disadvantage of non-linear resolutions is that they cannot be implemented using a simple stack-based memory structure like that in Prolog. Moreover, some strictly sequential operators such as cuts may not be handled as easily as in Prolog. In this paper, we propose a hybrid method to resolve infinite loops and redundant computations. We combine the ideas of loop checking and tabling to establish a linear tabulated resolution called TP-resolution. TP-resolution has two distinctive features: (1) It makes linear tabulated derivations in the same way as Prolog except that infinite loops are broken and redundant computations are reduced. It handles cuts as effectively as Prolog. (2) It is sound and complete for positive logic programs with the bounded-term-size property. The underlying algorithm can be implemented by an extension to any existing Prolog abstract machines such as WAM or ATOAM.

Previously we have proposed a logic, called priority logic 18, 20], where a theory consists of a collection of logic programming-like inference rules (without default negation) and a priority constraint among them. We showed that nonmonotonic reasoning in general can be viewed as selecting monotonic inferences that satisfy the speci ed priority constraints. The goal of this paper is to investigate how semantics of disjunctive programs can be understood as selecting monotonic inferences. We focus on credulous semantics. We show that the stable semantics of a disjunctive program can be represented by priority logic. The highlight of this paper is an interesting new semantics for disjunctive programs, discoved during this study, which has an abductive interpretation in the sense of Eshghi and Kowalski, whose abductive procedure can be adopted to serve as a credulous proof procedure for the new semantics.

This paper studies the semantics for the class of all defeasible (inheritance) networks, including cyclic and inconsistent networks using a transformation approach. First we show that defeasible networks can be translated, tractably, to default theories while preserving Horty's offpath credulous semantics for all consistent networks. Using the existing methods in dealing with the semantics of default logic, we are able to provide a tractable skeptical semantics, the well-founded semantics, and a new credulous semantics, the regular semantics, both of which are defined for any defeasible network. Furthermore, we show that these semantics are based on the same principle of specificity used by Horty in defining his credulous semantics of defeasible networks. 1 Introduction Two fundamental problems are to be addressed in this paper. First, the semantics of defeasible networks has previously been studied mainly under the assumption that such a network is acyclic and consis...

Infinite loops and redundant computations are long recognized open problems in Prolog. Two ways have been explored to resolve these problems: loop checking and tabling. Loop checking can cut infinite loops, but it cannot be both sound and complete even for function-free logic programs. Tabling seems to be an effective way to resolve infinite loops and redundant computations. However, existing tabulated resolutions, such as OLDT-resolution, SLG-resolution, and Tabulated SLS-resolution, are non-linear because they rely on the solution-lookup mode in formulating tabling. The principal disadvantage of non-linear resolutions is that they cannot be implemented using a simple stack-based memory structure like that in Prolog. Moreover, some strictly sequential operators such as cuts may not be handled as easily as in Prolog. In this paper, we propose a hybrid method to resolve infinite loops and redundant computations. We combine the ideas of loop checking and tabling to establish a linear tabulated resolution called TP-resolution. TP-resolution has two distinctive features: (1) It makes linear tabulated derivations in the same way as Prolog except that infinite loops are broken and redundant computations are reduced. It handles cuts as effectively as Prolog. (2) It is sound and complete for positive logic programs with the bounded-term-size property. The underlying algorithm can be implemented by an extension to any existing Prolog abstract machines such as WAM or ATOAM.

Two complete loop checking mechanisms have been presented in the literature for logic programs with functions: OS-check and EVA-check. OS-check is computationally e cient but quite unreliable in that it often mis-identiÿes inÿnite loops, whereas EVA-check is reliable for a majority of cases but quite expensive. In this paper, we develop a series of new complete loop checking mechanisms, called VAF-checks. The key technique we introduce is the notion of expanded variants, which captures a key structural characteristic of inÿnite loops. We show that our approach is superior to both OS-check and EVA-check in that it is as e cient as OS-check and as reliable as EVA-check.

We present a new characterization of termination of general logic programs. Most existing termination analysis approaches rely on some static information about the structure of the source code of a logic program, such as modes/types, norms/level mappings, models/interargument relations, and the like. We propose a dynamic approach that employs some key dynamic features of an infinite (generalized) SLDNF-derivation, such as repetition of selected subgoals and recursive increase in term size. We also introduce a new formulation of SLDNF-trees, called generalized SLDNF-trees. Generalized SLDNF-trees deal with negative subgoals in the same way as Prolog and exist for any general logic programs.

In the current practice of Answer Set Programming (ASP), evaluable functions are represented as special kinds of relations. This often makes the resulting program unnecessarily large when instantiated over a large domain. The extra constraints needed to enforce the relation as a function also make the logic program less transparent. In this paper, we consider adding evaluable functions to answer set logic programs. The class of logic programs that we consider here is that of weight constraint programs, which are widely used in ASP. We propose an answer set semantics to these extended weight constraint programs and define loop completion to characterize the semantics. Computationally, we provide a translation from loop completions of these programs to instances of the Constraint Satisfaction Problem (CSP) and use the off-the-shelf CSP solvers to compute the answer sets of these programs. A main advantage of this approach is that global constraints implemented in such CSP solvers become available to ASP. The approach also provides a new encoding for CSP problems in the style of weight constraint programs. We have implemented a prototype system based on these results, and our experiments show that this prototype system competes well with the state-of-the-art ASP solvers. In addition, we illustrate the utilities of global constraints in the ASP context. This is an extension of a preliminary version that appeared in the Proceedings of LPNMR'09 drawing some results from .

Preface to 1990 edition iii Preface to 2003 edition v CONTENTS datatype SENT = Prop of string | Not of SENT | And of (SENT * SENT) | Or of (SENT * SENT) | Imp of (SENT * SENT) | Eq of (SENT * SENT) | Forall of (TERM * SENT) fun Neg (Not(sent)) = sent | Neg sent = Not(sent); fun alpha(And(s,t)) = true | alpha(Not(Imp(s,t))) =true | alpha(Not(Or(s,t)

Answer Set Programming (ASP) is a declarative programming paradigm with roots in logic programming, knowledge representation, and non-monotonic reasoning. The ASP competition series aims at assessing and promoting the evolution of ASP systems and applications. Its growing range of challenging application-oriented benchmarks inspires and showcases continuous advancements of the state of the art in ASP.

Answer Set Programming (ASP) is a well-known paradigm of declarative programming with roots in logic programming and non-monotonic reasoning. Similar to other closely related problemsolving technologies, such as SAT/SMT, QBF, Planning and Scheduling, advancements in ASP solving are assessed in competition events. In this paper, we report about the design and results of the Sixth ASP Competition, which was jointly organized by the University of Calabria (Italy), Aalto University (Finland), and the University of Genoa (Italy), in affiliation with the 13th International Conference on Logic Programming and Non-Monotonic Reasoning. This edition maintained some of the design decisions introduced in 2014, e.g., the conception of sub-tracks, the scoring scheme, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. On the other hand, it featured also some novelties, like a benchmark selection stage classifying instances according to their empirical hardness, and a "Marathon" track where the topperforming systems are given more time for solving hard benchmarks.

Chapters ["Statistical Statements in Probabilistic Logic Programming" and "Efficient Computation of Answer Sets via SAT Modulo Acyclicity and Vertex Elimination"] were previously published non-open access. They have now been changed to open access under a CC BY 4.0 license and the copyright holder updated to 'The Author(s)'. The book has also been updated with these changes.

Answer Set Programming (ASP) is a declarative programming paradigm with roots in logic programming, knowledge representation, and non-monotonic reasoning. The ASP competition series aims at assessing and promoting the evolution of ASP systems and applications. Its growing range of challenging application-oriented benchmarks inspires and showcases continuous advancements of the state of the art in ASP.

The optimization of daily operating room surgery schedule can be problematic because of many constraints, like to determine the start time of different surgeries and allocating the required resources, including the availability of beds in different units. Recently, Answer Set Programming (ASP) has been successfully employed for addressing and solving real-life scheduling and planning problems in the health-care domain. In this paper we present an enhanced solution using ASP for scheduling and rescheduling operating rooms taking explicitly into considerations the availability of beds for intensive and post-anesthesia care units. We tested our solution on different benchmarks with realistic parameters. The results of our experiments show that ASP is a suitable methodology for solving also such enhanced problem.

The Operating Room Scheduling (ORS) problem refers to the task of assigning patients to operating rooms. An automated solution able to solve the ORS problem in real world scenarios should also be “explainable” to be fully acceptable. Answer Set Programming (ASP) has been successfully applied to solve the ORS problem. However, when the available resources are not enough to satisfy user’s requirements (e.g., insufficient number of free beds) the system cannot compute a schedule, and also cannot provide an explanation for that “negative” result. In this work, we present an extension of the aforementioned ASP-based approach to the ORS problem that is also able to provide explanations (in terms of input facts) that caused the absence of solutions. The explanation computation technique builds on the ideas employed by the ASPIDE debugger for ASP programs. Preliminary experimental results show the viability of the approach.

Answer Set Programming (ASP) is a prominent knowledge representation language with roots in logic programming and non-monotonic reasoning. Biennial ASP competitions are organized in order to furnish challenging benchmark collections and assess the advancement of the state of the art in ASP solving. In this paper, we report on the design and results of the Seventh ASP Competition, jointly organized by the University of Calabria (Italy), the University of Genova (Italy), and the University of Potsdam (Germany), in affiliation with the 14th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2017).

Answer Set Programming (ASP) is a powerful language for knowledge representation and reasoning. ASP is exploited in real-world applications and is also attracting the interest of industry thanks to the availability of efficient implementations. ASP systems compute solutions relying on two modules: an instantiator (or grounder) that produces, by removing variables from the rules, a ground program equivalent to the input one; and a model generator (or solver) that computes the solutions of such propositional program. In this paper we make a first step toward the exploitation of automated selection techniques to the grounding module. We rely on two well-known ASP grounders, namely the grounder of the DLV system and GRINGO, and we leverage on automated classification algorithms to devise and implement an automatic procedure for selecting the "best" grounder for each problem instance. An experimental analysis, conducted on benchmarks and solvers from the 3rd ASP Competition, shows that our approach improves the evaluation performance independently from the solver associated with our grounder selector. ⋆ This paper is an early version of the work submitted at the 13rd Conference of the Italian Association for Artificial Intelligence (AI*IA 2013).

ME-ASP is a multi-engine solver for ground ASP programs. It exploits algorithm selection techniques based on classification to select one among a set of out-of-the-box heterogeneous ASP solvers used as black-box engines. In this paper we report on (i) a new optimized implementation of ME-ASP; and (ii) an attempt of applying algorithm selection to non-ground programs. An experimental analysis reported in the paper shows that (i) the new implementation of ME-ASP is substantially faster than the previous version; and (ii) the multi-engine recipe can be applied to the evaluation of nonground programs with some benefits.

Competitive native solvers for Answer Set Programming (ASP) perform a backtracking search by assuming the truth of literals. The choice of literals (the heuristic) is fundamental for the performance of these systems. Most of the efficient ASP systems employ a heuristic based on look-ahead, that is, a literal is tentatively assumed and its heuristic value is based on its deterministic consequences. However, looking ahead is a costly operation, and indeed look-ahead often accounts for the majority of time taken by ASP solvers. For Satisfiability (SAT), a radically different approach, called look-back heuristic, proved to be quite successful: Instead of looking ahead, one uses information gathered during the computation performed so far, thus looking back. In this approach, atoms which have been frequently involved in inconsistencies are preferred. In this paper, we carry over this approach to the framework of disjunctive ASP. We design a number of look-back heuristics exploiting peculiarities of ASP and implement them in the ASP system DLV. We compare their performance on a collection of hard ASP programs both structured and randomly generated. These experiments indicate that a very basic approach works well, outperforming all of the prominent disjunctive ASP systems -DLV (with its traditional heuristic), GnT, and CModels3 -on many of the instances considered. Supported by M.I.U.R. within projects "Potenziamento e Applicazioni della Programmazione Logica Disgiuntiva" and "Sistemi basati sulla logica per la rappresentazione di conoscenza: estensioni e tecniche di ottimizzazione."

Answer set programming is a new programming paradigm proposed in [1] and [2], and based on the answer set semantics of Prolog [3]. It is well known that an answer set for a logic program is also a model of the program's completion [4]. The converse is true when the logic program is "tight" [6, 5]. Lin and Zhao [7] showed that for non-tight programs the models of completion which do not correspond to answer sets can be eliminated by adding to the completion what they called "loop formulas". Nevertheless, their solver ASSAT 1 has some disadvantages: it can work only with basic rules, and it can compute only one answer set. Answer set solver CMODELS-1 2 [12] is a system that computes answer sets for logic programs that are tight or can be transformed into tight programs, and does not suffer from these limitations. We are going to present a new system CMODELS-2 , that is able to fix ASSAT's disadvantages. Another attractive feature of the new system is that it organizes the search process more efficiently then ASSAT, because it does not explore the same part of the search tree more than once. In the rest of the paper we will omit number 2 in the name of the system.

Answer Set Programming (ASP) is a well-known paradigm of declarative programming with roots in logic programming and non-monotonic reasoning. Similar to other closely related problem-solving technologies, such as SAT/SMT, QBF, Planning and Scheduling, advancements in ASP solving are assessed in competition events. In this paper, we report about the design and results of the Sixth ASP Competition, which was jointly organized by the University of Calabria (Italy), Aalto University (Finland), and the University of Genoa (Italy), in affiliation with the 13th International Conference on Logic Programming and Non-Monotonic Reasoning. This edition maintained some of the design decisions introduced in 2014, e.g., the conception of sub-tracks, the scoring scheme, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. On the other hand, it featured also some novelties, like a benchmark selection stage classifying instances according to their e...

Competitive native solvers for Answer Set Programming (ASP) perform a backtracking search by assuming the truth of literals. The choice of literals (the heuristic) is fundamental for the performance of these systems. Most of the efficient ASP systems employ a heuristic based on look-ahead, that is, a literal is tentatively assumed and its heuristic value is based on its deterministic consequences. However, looking ahead is a costly operation, and indeed look-ahead often accounts for the majority of time taken by ASP solvers. For Satisfiability (SAT), a radically different approach, called look-back heuristic, proved to be quite successful: Instead of looking ahead, one uses information gathered during the computation performed so far, thus looking back. In this approach, atoms which have been frequently involved in inconsistencies are preferred. In this paper, we carry over this approach to the framework of disjunctive ASP. We design a number of look-back heuristics exploiting peculiarities of ASP and implement them in the ASP system DLV. We compare their performance on a collection of hard ASP programs both structured and randomly generated. These experiments indicate that a very basic approach works well, outperforming all of the prominent disjunctive ASP systems -DLV (with its traditional heuristic), GnT, and CModels3 -on many of the instances considered. Supported by M.I.U.R. within projects "Potenziamento e Applicazioni della Programmazione Logica Disgiuntiva" and "Sistemi basati sulla logica per la rappresentazione di conoscenza: estensioni e tecniche di ottimizzazione."

Answer Set Programming (ASP) is a well-established paradigm of declarative programming that has been developed in the field of logic programming and nonmonotonic reasoning. Advances in ASP solving technology are customarily assessed in competition events, as it happens for other closely-related problemsolving technologies like SAT/SMT, QBF, Planning and Scheduling. ASP Competitions are (usually) biennial events; however, the Fifth ASP Competition departs from tradition, in order to join the FLoC Olympic Games at the Vienna Summer of Logic 2014, which is expected to be the largest event in the history of logic. This edition of the ASP Competition series is jointly organized by the University of Calabria (Italy), the Aalto University (Finland), and the University of Genova (Italy), and is affiliated with the 30th International Conference on Logic Programming (ICLP 2014). It features a completely re-designed setup, with novelties involving the design of tracks, the scoring schema, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. Benchmark domains are taken from past editions, and best system packages submitted in 2013 are compared with new versions and solvers.

Answer set programming is a logic programming paradigm that has increased in popularity over the past decade and found applications in a wide variety of elds. Even so, manuals written by the designers of answer set solvers usually described the semantics of the input languages of their systems using examples and informal comments that appeal to the users’ intuition, without references to any precise semantics. We describe a precise semantics for the input language of the grounder gringo, which serves as the front end for several answer set solvers. The semantics represents gringo rules as innitary propositional formulas. We develop methods for using this semantics to prove properties of gringo programs, such as verifying program correctness.

Penelitian ini menganalisis efektivitas repeat-until dalam validasi input dengan membandingkannya dengan while loop melalui studi kasus program kata sandi. Hasil menunjukkan bahwa repeat-until lebih efektif dalam memastikan input minimal satu kali, sementara while loop lebih efisien jika input awal benar. Dari segi efisiensi kode, repeat-until lebih sederhana, sedangkan while loop lebih fleksibel dalam menghindari eksekusi yang tidak perlu. Untuk meningkatkan keamanan, disarankan penerapan batasan percobaan guna mencegah infinite loop. Pemilihan struktur perulangan yang tepat dapat meningkatkan efisiensi dan efektivitas program.

Uncertainty in logic programming has been widely investigated in the last decades, leading to multiple extensions of the classical LP paradigm. However, few of these are designed as extensions of the well-established and powerful CLP scheme for Constraint Logic Programming. In a previous work we have proposed the SQCLP (proximity-based qualified constraint logic programming) scheme as a quite expressive extension of CLP with support for qualification values and proximity relations as generalizations of uncertainty values and similarity relations, respectively. In this paper we provide a transformation technique for transforming SQCLP programs and goals into semantically equivalent CLP programs and goals, and a practical Prolog-based implementation of some particularly useful instances of the SQCLP scheme. We also illustrate, by showing some simple-and working-examples, how the prototype can be effectively used as a tool for solving problems where qualification values and proximity relations play a key role. Intended use of SQCLP includes flexible information retrieval applications.