Implementing Ordered Choice Logic Programming Using Answer Set Solvers

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

Database Technologies ◽

10.4018/978-1-60566-058-5.ch137 ◽

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 ◽

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).

onlineSPARC: A Programming Environment for Answer Set Programming

10.1017/s1471068418000509 ◽

2018 ◽

Vol 19 (2) ◽

pp. 262-289 ◽

Cited By ~ 1

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 ◽

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.

Paracoherent Answer Set Semantics meets Argumentation Frameworks

10.1017/s1471068419000139 ◽

2019 ◽

Vol 19 (5-6) ◽

pp. 688-704

Author(s):

GIOVANNI AMENDOLA ◽

FRANCESCO RICCA

Keyword(s):

Logic Programming ◽

Logic Program ◽

Great Success ◽

Argumentation Framework ◽

Computational Costs ◽

Abstract Argumentation ◽

Answer Set Semantics ◽

Answer Sets ◽

AbstractIn the last years, abstract argumentation has met with great success in AI, since it has served to capture several non-monotonic logics for AI. Relations between argumentation framework (AF) semantics and logic programming ones are investigating more and more. In particular, great attention has been given to the well-known stable extensions of an AF, that are closely related to the answer sets of a logic program. However, if a framework admits a small incoherent part, no stable extension can be provided. To overcome this shortcoming, two semantics generalizing stable extensions have been studied, namely semi-stable and stage. In this paper, we show that another perspective is possible on incoherent AFs, called paracoherent extensions, as they have a counterpart in paracoherent answer set semantics. We compare this perspective with semi-stable and stage semantics, by showing that computational costs remain unchanged, and moreover an interesting symmetric behaviour is maintained.

From answer set logic programming to circumscription via logic of GK

Artificial Intelligence ◽

10.1016/j.artint.2010.04.001 ◽

2011 ◽

Vol 175 (1) ◽

pp. 264-277 ◽

Cited By ~ 8

Author(s):

Fangzhen Lin ◽

Yi Zhou

Keyword(s):

Logic Programming ◽

Justifying answer sets using argumentation

10.1017/s1471068414000702 ◽

2015 ◽

Vol 16 (1) ◽

pp. 59-110 ◽

Cited By ~ 15

Author(s):

CLAUDIA SCHULZ ◽

FRANCESCA TONI

Keyword(s):

Logic Programming ◽

Logic Program ◽

Argumentation Theory ◽

Attack Tree ◽

Attack Trees ◽

Answer Sets ◽

AbstractAn answer set is a plain set of literals which has no further structure that would explain why certain literals are part of it and why others are not. We show how argumentation theory can help to explain why a literal is or is not contained in a given answer set by defining two justification methods, both of which make use of the correspondence between answer sets of a logic program and stable extensions of the assumption-based argumentation (ABA) framework constructed from the same logic program.Attack Treesjustify a literal in argumentation-theoretic terms, i.e. using arguments and attacks between them, whereasABA-Based Answer Set Justificationsexpress the same justification structure in logic programming terms, that is using literals and their relationships. Interestingly, an ABA-Based Answer Set Justification corresponds to an admissible fragment of the answer set in question, and an Attack Tree corresponds to an admissible fragment of the stable extension corresponding to this answer set.

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

10.1017/s1471068421000429 ◽

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 ◽

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.

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 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.

Preferences and Nonmonotonic Reasoning

AI Magazine ◽

10.1609/aimag.v29i4.2179 ◽

2008 ◽

Vol 29 (4) ◽

pp. 69 ◽

Cited By ~ 24

Author(s):

Gerhard Brewka ◽

Ilkka Niemela ◽

Miroslaw Truszczynski

Keyword(s):

Logic Programming ◽

Nonmonotonic Reasoning ◽

Nonmonotonic Logic ◽

Preference Information ◽

The World ◽

Extended Logic Programming ◽

Answer Set Semantics ◽

Nonmonotonic Logics ◽

We give an overview of the multifaceted relationship between nonmonotonic logics and preferences. We discuss how the nonmonotonicity of reasoning itself is closely tied to preferences reasoners have on models of the world or, as we often say here, possible belief sets. Selecting extended logic programming with the answer-set semantics as a "generic" nonmonotonic logic, we show how that logic defines preferred belief sets and how preferred belief sets allow us to represent and interpret normative statements. Conflicts among program rules (more generally, defaults) give rise to alternative preferred belief sets. We discuss how such conflicts can be resolved based on implicit specificity or on explicit rankings of defaults. Finally, we comment on formalisms which explicitly represent preferences on properties of belief sets. Such formalisms either build preference information directly into rules and modify the semantics of the logic appropriately, or specify preferences on belief sets independently of the mechanism to define them.

The Seventh Answer Set Programming Competition: Design and Results

10.1017/s1471068419000061 ◽

2019 ◽

Vol 20 (2) ◽

pp. 176-204 ◽

Cited By ~ 5

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 ◽

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).