scholarly journals First-order Answer Set Programming as Constructive Proof Search

2018 ◽  
Vol 18 (3-4) ◽  
pp. 673-690
Author(s):  
ALEKSY SCHUBERT ◽  
PAWEŁ URZYCZYN
Keyword(s):  
Logic Programming ◽  
Intuitionistic Logic ◽  
Proof Theory ◽  
Answer Set Programming ◽  
Theory And Practice ◽  
Close Similarity ◽  
Constructive Proof ◽  
First Order ◽  
Proof Search ◽  
Answer Set

AbstractWe propose an interpretation of the first-order answer set programming (FOASP) in terms of intuitionistic proof theory. It is obtained by two polynomial translations between FOASP and the bounded-arity fragment of the Σ1 level of the Mints hierarchy in first-order intuitionistic logic. It follows that Σ1 formulas using predicates of fixed arity (in particular unary) is of the same strength as FOASP. Our construction reveals a close similarity between constructive provability and stable entailment, or equivalently, between the construction of an answer set and an intuitionistic refutation. This paper is under consideration for publication in Theory and Practice of Logic Programming

scholarly journals Meta-Interpretive Learning Using HEX-Programs

2019 ◽  
Author(s):  
Tobias Kaminski ◽  
Thomas Eiter ◽  
Katsumi Inoue
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Answer Set Programming ◽  
Search Space ◽  
Recent Approach ◽  
Performance Gains ◽  
Pruning Techniques ◽  
Answer Set

Meta-Interpretive Learning (MIL) is a recent approach for Inductive Logic Programming (ILP) implemented in Prolog. Alternatively, MIL-problems can be solved by using Answer Set Programming (ASP), which may result in performance gains due to efficient conflict propagation. However, a straightforward MIL-encoding results in a huge size of the ground program and search space. To address these challenges, we encode MIL in the HEX-extension of ASP, which mitigates grounding issues, and we develop novel pruning techniques.

A Roadmap on Updates

2009 ◽  
pp. 2261-2267
Author(s):  
Fernando Zacarías Flores ◽  
Dionicio Zacarías Flores ◽  
Rosalba Cuapa Canto ◽  
Luis Miguel Guzmán Muñoz
Keyword(s):  
Standard Model ◽  
Logic Programming ◽  
Structural Properties ◽  
Relational Databases ◽  
Logic Program ◽  
Answer Set Programming ◽  
Central Issue ◽  
The Standard Model ◽  
Answer Set

Updates, is a central issue in relational databases and knowledge databases. In the last years, it has been well studied in the non-monotonic reasoning paradigm. Several semantics for logic program updates have been proposed (Brewka, Dix, & Knonolige 1997), (De Schreye, Hermenegildo, & Pereira, 1999) (Katsumo & Mendelzon, 1991). However, recently a set of proposals has been characterized to propose mechanisms of updates based on logic and logic programming. All these mechanisms are built on semantics based on structural properties (Eiter, Fink, Sabattini & Thompits, 2000) (Leite, 2002) (Banti, Alferes & Brogi, 2003) (Zacarias, 2005). Furthermore, all these semantic ones coincide in considering the AGM proposal as the standard model in the update theory, for their wealth in properties. The AGM approach, introduced in (Alchourron, Gardenfors & Makinson, 1985) is the dominating paradigm in the area, but in the context of monotonic logic. All these proposals analyze and reinterpret the AGM postulates under the Answer Set Programming (ASP) such as (Eiter, Fink, Sabattini & Thompits, 2000). However, the majority of the adapted AGM and update postulates are violated by update programs, as shown in(De Schreye, Hermenegildo, & Pereira, 1999).

scholarly journals onlineSPARC: A Programming Environment for Answer Set Programming

2018 ◽  
Vol 19 (2) ◽  
pp. 262-289 ◽  
Author(s):  
ELIAS MARCOPOULOS ◽  
YUANLIN ZHANG
Keyword(s):  
High School Students ◽  
Logic Programming ◽  
Answer Set Programming ◽  
Programming Environment ◽  
Logic Programs ◽  
School Students ◽  
Major Barrier ◽  
Simple Interface ◽  
Answer Sets ◽  
Answer Set

AbstractRecent progress in logic programming (e.g. the development of the answer set programming (ASP) paradigm) has made it possible to teach it to general undergraduate and even middle/high school students. Given the limited exposure of these students to computer science, the complexity of downloading, installing, and using tools for writing logic programs could be a major barrier for logic programming to reach a much wider audience. We developed onlineSPARC, an online ASP environment with a self-contained file system and a simple interface. It allows users to type/edit logic programs and perform several tasks over programs, including asking a query to a program, getting the answer sets of a program, and producing a drawing/animation based on the answer sets of a program.

scholarly journals Graphs and colorings for answer set programming

2006 ◽  
Vol 6 (1-2) ◽  
pp. 61-106 ◽  
Author(s):  
KATHRIN KONCZAK ◽  
THOMAS LINKE ◽  
TORSTEN SCHAUB
Keyword(s):  
Proof Theory ◽  
Answer Set Programming ◽  
Logic Programs ◽  
Algorithmic Approach ◽  
Dependency Graphs ◽  
Basic Strategy ◽  
Content System ◽  
Formal Properties ◽  
Answer Sets ◽  
Answer Set

We investigate the usage of rule dependency graphs and their colorings for characterizing and computing answer sets of logic programs. This approach provides us with insights into the interplay between rules when inducing answer sets. We start with different characterizations of answer sets in terms of totally colored dependency graphs that differ in graph-theoretical aspects. We then develop a series of operational characterizations of answer sets in terms of operators on partial colorings. In analogy to the notion of a derivation in proof theory, our operational characterizations are expressed as (non-deterministically formed) sequences of colorings, turning an uncolored graph into a totally colored one. In this way, we obtain an operational framework in which different combinations of operators result in different formal properties. Among others, we identify the basic strategy employed by the noMoRe system and justify its algorithmic approach. Furthermore, we distinguish operations corresponding to Fitting's operator as well as to well-founded semantics.

scholarly journals Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

2021 ◽  
pp. 1-25
Author(s):  
NIKOS KATZOURIS ◽  
GEORGIOS PALIOURAS ◽  
ALEXANDER ARTIKIS
Keyword(s):  
Probabilistic Reasoning ◽  
Answer Set Programming ◽  
Predictive Performance ◽  
Theory And Practice ◽  
Markov Logic ◽  
Event Calculus ◽  
Maritime Surveillance ◽  
Novel Approach ◽  
Combine Uncertainty ◽  
Answer Set

Abstract Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a number of state-of-the-art batch learning algorithms on CER data sets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel approach, both in terms of efficiency and predictive performance. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).

scholarly journals Here and There with Arithmetic

2021 ◽  
pp. 1-15
Author(s):  
VLADIMIR LIFSCHITZ
Keyword(s):  
Programming Language ◽  
Propositional Logic ◽  
Answer Set Programming ◽  
The Other ◽  
Arithmetic Operations ◽  
First Order ◽  
The Relationship ◽  
Answer Set

Abstarct In the theory of answer set programming, two groups of rules are called strongly equivalent if, informally speaking, they have the same meaning in any context. The relationship between strong equivalence and the propositional logic of here-and-there allows us to establish strong equivalence by deriving rules of each group from rules of the other. In the process, rules are rewritten as propositional formulas. We extend this method of proving strong equivalence to an answer set programming language that includes operations on integers. The formula representing a rule in this language is a first-order formula that may contain comparison symbols among its predicate constants, and symbols for arithmetic operations among its function constants. The paper is under consideration for acceptance in TPLP.

scholarly journals Determining Action Reversibility in STRIPS Using Answer Set and Epistemic Logic Programming

2021 ◽  
pp. 1-17
Author(s):  
WOLFGANG FABER ◽  
MICHAEL MORAK ◽  
LUKÁŠ CHRPA
Keyword(s):  
Logic Programming ◽  
Epistemic Logic ◽  
Answer Set Programming ◽  
Reasoning About Actions ◽  
Original State ◽  
Computational Problem ◽  
Compare And Contrast ◽  
Answer Set

Abstract In the context of planning and reasoning about actions and change, we call an action reversible when its effects can be reverted by applying other actions, returning to the original state. Renewed interest in this area has led to several results in the context of the PDDL language, widely used for describing planning tasks. In this paper, we propose several solutions to the computational problem of deciding the reversibility of an action. In particular, we leverage an existing translation from PDDL to Answer Set Programming (ASP), and then use several different encodings to tackle the problem of action reversibility for the STRIPS fragment of PDDL. For these, we use ASP, as well as Epistemic Logic Programming (ELP), an extension of ASP with epistemic operators, and compare and contrast their strengths and weaknesses.

scholarly journals A Case for Query-driven Predicate Answer Set Programming

10.29007/ngm2 ◽  
2018 ◽  
Author(s):  
Gopal Gupta ◽  
Elmer Salazar ◽  
Kyle Marple ◽  
Zhuo Chen ◽  
Farhad Shakerin
Keyword(s):  
Logic Programming ◽  
Common Sense ◽  
Automated Reasoning ◽  
Answer Set Programming ◽  
Stable Model ◽  
Common Sense Reasoning ◽  
Knowledge Based ◽  
Full Power ◽  
Programming Systems ◽  
Answer Set

Answer Set Programming (ASP) has emerged as a successful paradigm for developing intelligent applications. ASP is based on adding negation as failure to logic programming under the stable model semantics regime. ASP allows for sophisticated reasoning mechanisms that are employed by humans to be modeled elegantly. We argue that being able to model common sense reasoning as used by humans is critical for success of automated reasoning. We also argue that extending answer programming systems to general predicates is critical to realizing the full power of ASP. Goal-directed predicate ASP systems are needed to make the ASP technology practical for building large, scalable knowledge-based applications.

scholarly journals The Seventh Answer Set Programming Competition: Design and Results

2019 ◽  
Vol 20 (2) ◽  
pp. 176-204 ◽  
Author(s):  
MARTIN GEBSER ◽  
MARCO MARATEA ◽  
FRANCESCO RICCA
Keyword(s):  
Knowledge Representation ◽  
Logic Programming ◽  
State Of The Art ◽  
Answer Set Programming ◽  
The State ◽  
International Conference ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
The University ◽  
Answer Set

AbstractAnswer 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).

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