scholarly journals Generating Concurrent Programs From Sequential Data Structure Knowledge Using Answer Set Programming

2021 ◽  
Vol 345 ◽  
pp. 219-233
Author(s):  
Sarat Chandra Varanasi ◽  
Neeraj Mittal ◽  
Gopal Gupta
Keyword(s):  
Data Structure ◽  
Answer Set Programming ◽  
Concurrent Programs ◽  
Sequential Data ◽  
Structure Knowledge ◽  
Answer Set

scholarly journals Synthesizing Concurrent Programs Using Answer Set Programming

2012 ◽  
Vol 120 (3-4) ◽  
pp. 205-229 ◽  
Author(s):  
Emanuele De Angelis ◽  
Alberto Pettorossi ◽  
Maurizio Proietti
Keyword(s):  
Answer Set Programming ◽  
Concurrent Programs ◽  
Answer Set

Justifications for logic programs under answer set semantics

2009 ◽  
Vol 9 (1) ◽  
pp. 1-56 ◽  
Author(s):  
ENRICO PONTELLI ◽  
TRAN CAO SON ◽  
OMAR ELKHATIB
Keyword(s):  
Data Structure ◽  
Answer Set Programming ◽  
Basic Data ◽  
Logic Programs ◽  
Computation Model ◽  
On Line ◽  
Truth Value ◽  
Answer Set Semantics ◽  
Answer Set

AbstractThe paper introduces the notion of offline justification for answer set programming (ASP). Justifications provide a graph-based explanation of the truth value of an atom with respect to a given answer set. The paper extends also this notion to provide justification of atoms during the computation of an answer set (on-line justification) and presents an integration of online justifications within the computation model of Smodels. Offline and online justifications provide useful tools to enhance understanding of ASP, and they offer a basic data structure to support methodologies and tools for debugging answer set programs. A preliminary implementation has been developed in – .

scholarly journals Open answer set programming with guarded programs

2008 ◽  
Vol 9 (4) ◽  
pp. 1-53 ◽  
Author(s):  
Stijn Heymans ◽  
Davy Van Nieuwenborgh ◽  
Dirk Vermeir
Keyword(s):  
Answer Set Programming ◽  
Answer Set

scholarly journals Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming

2013 ◽  
Vol 29 (18) ◽  
pp. 2320-2326 ◽  
Author(s):  
Carito Guziolowski ◽  
Santiago Videla ◽  
Federica Eduati ◽  
Sven Thiele ◽  
Thomas Cokelaer ◽  
...  
Keyword(s):  
Answer Set Programming ◽  
Signaling Network ◽  
Logic Models ◽  
Answer Set

CoreALMlib: An library translated from the Component Library

2016 ◽  
Vol 16 (5-6) ◽  
pp. 800-816 ◽  
Author(s):  
DANIELA INCLEZAN
Keyword(s):  
Natural Language ◽  
Answer Set Programming ◽  
Natural Language Understanding ◽  
Inference Engine ◽  
Lexical Resources ◽  
Language Understanding ◽  
Commonsense Knowledge ◽  
Component Library ◽  
Action Language ◽  
Answer Set

AbstractThis paper presents CoreALMlib, an $\mathscr{ALM}$ library of commonsense knowledge about dynamic domains. The library was obtained by translating part of the Component Library (CLib) into the modular action language $\mathscr{ALM}$. CLib consists of general reusable and composable commonsense concepts, selected based on a thorough study of ontological and lexical resources. Our translation targets CLibstates (i.e., fluents) and actions. The resulting $\mathscr{ALM}$ library contains the descriptions of 123 action classes grouped into 43 reusable modules that are organized into a hierarchy. It is made available online and of interest to researchers in the action language, answer-set programming, and natural language understanding communities. We believe that our translation has two main advantages over its CLib counterpart: (i) it specifies axioms about actions in a more elaboration tolerant and readable way, and (ii) it can be seamlessly integrated with ASP reasoning algorithms (e.g., for planning and postdiction). In contrast, axioms are described in CLib using STRIPS-like operators, and CLib's inference engine cannot handle planning nor postdiction.

scholarly journals Grounding and Solving in Answer Set Programming

AI Magazine ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 25-32 ◽  
Author(s):  
Benjamin Kaufmann ◽  
Nicola Leone ◽  
Simona Perri ◽  
Torsten Schaub
Keyword(s):  
Problem Solving ◽  
Propositional Logic ◽  
Logic Program ◽  
Answer Set Programming ◽  
Key Issues ◽  
Answer Sets ◽  
Answer Set

Answer set programming is a declarative problem solving paradigm that rests upon a workflow involving modeling, grounding, and solving. While the former is described by Gebser and Schaub (2016), we focus here on key issues in grounding, or how to systematically replace object variables by ground terms in a effective way, and solving, or how to compute the answer sets of a propositional logic program obtained by grounding.

scholarly journals Reasoning about Cardinal Directions between 3-Dimensional Extended Objects using Answer Set Programming

2020 ◽  
Vol 20 (6) ◽  
pp. 942-957
Author(s):  
Yusuf Izmirlioglu ◽  
Esra Erdem
Keyword(s):  
Nonmonotonic Reasoning ◽  
Answer Set Programming ◽  
3 Dimensional ◽  
3D Space ◽  
Formal Framework ◽  
Different Types ◽  
New Type ◽  
Cardinal Directions ◽  
Answer Set

AbstractWe propose a novel formal framework (called 3D-NCDC-ASP) to represent and reason about cardinal directions between extended objects in 3-dimensional (3D) space, using Answer Set Programming (ASP). 3D-NCDC-ASP extends Cardinal Directional Calculus (CDC) with a new type of default constraints, and NCDC-ASP to 3D. 3D-NCDC-ASP provides a flexible platform offering different types of reasoning: Nonmonotonic reasoning with defaults, checking consistency of a set of constraints on 3D cardinal directions between objects, explaining inconsistencies, and inferring missing CDC relations. We prove the soundness of 3D-NCDC-ASP, and illustrate its usefulness with applications.

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