Martín Diéguez · 2014
Answer Set Programming (ASP) has become a popular way for representing different kinds of scenarios from knowledge representation in Artificial Intelligence. Frequently, these scenarios involve a temporal component which must be considered. In ASP, time is usually represented as a variable whose values are defined by an extensional predicate with a finite domain. Dealing with a finite temporal interval has some disadvantages. First, checking the existence of a plan is not possible and second, it also makes difficult to decide whether two programs are strongly equivalent. If we extend the syntax of Answer Set Programming by using temporal operators from temporal modal logics, then infinite time can be considered, so the aforementioned disadvantages can be overcome. This extension constitutes, in fact, a formalism called Temporal Equilibrium Logic, which is based on Equilibrium Logic (a logical characterisation of ASP). Although recent contributions have shown promising results, Temporal Equilibrium Logic is still a novel paradigm and there are many gaps to fill. Our goal is to keep developing this paradigm, filling those gaps and turning it into a suitable framework for temporal reasoning.
Cited by 1
Tobias Geibinger · Electronic Proceedings in Theoretical Computer Science · 2023
The interest in explainability in artificial intelligence (AI) is growing vastly due to the near ubiquitous state of AI in our lives and the increasing complexity of AI systems. Answer-set Programming (ASP) is used in many areas, among them are industrial optimisation, knowledge management or life sciences, and thus of great interest in the context of explainability. To ensure the successful application of ASP as a problem-solving paradigm in the future, it is thus crucial to investigate explanations for ASP solutions. Such an explanation generally tries to give an answer to the question of why something is, respectively is not, part of the decision produced or solution to the formulated problem. Although several explanation approaches for ASP exist, almost all of them lack support for certain language features that are used in practice. Most notably, this encompasses the various ASP extensions that have been developed in the recent years to enable reasoning over theories, external computations, or neural networks. This project aims to fill some of these gaps and contribute to the state of the art in explainable ASP. We tackle this by extending the language support of existing approaches but also by the development of novel explanation formalisms, like contrastive explanations.
Cited by 1Open access
Alex Brik, Jeffrey B. Remmel · Annals of Pure and Applied Logic · 2013
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Gharib, Mona · publish.UP (University of Potsdam) · 2008
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Stashuk, Oleksandr · reposiTUm (TU Wien) · 2020
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Domenico Corapi, Alessandra Russo, Emil Lupu · Lecture notes in computer science · 2012
Cited by 69
Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2008
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Erich Teppan, Gerhard Friedrich · Frontiers in artificial intelligence and applications · 2016
Constraint answer set programming (CASP) is a family of hybrid approaches integrating answer set programming (ASP) and constraint programming (CP). These hybrid approaches have already proven to be very successful in various domains. In this paper we present first evaluation results for the CASP solver ASCASS, which provides novel methods for defining and exploiting problem-dependent search heuristics. Beyond the possibility of using already built-in problem-independent heuristics, ASCASS allows on the ASP level the definition of problem-dependent variable selection, value selection and pruning strategies, which guide the search of the CP solver. The proof-of-concept evaluation was carried out on benchmark instances of the real world Partner Units Problem (PUP). Due to a sophisticated heuristic, which cannot be represented by other ASP or CASP solvers, ASCASS shows superior performance.
Cited by 7Open access
Mario Alviano, Ly Ly Trieu, Tran Cao Son et al. · Electronic Proceedings in Theoretical Computer Science · 2023
The paper presents an enhancement of xASP, a system that generates explanation graphs for Answer Set Programming (ASP). Different from xASP, the new system, xASP2, supports different clingo constructs like the choice rules, the constraints, and the aggregates such as #sum, #min. This work formalizes and presents an explainable artificial intelligence system for a broad fragment of ASP, capable of shrinking as much as possible the set of assumptions and presenting explanations in terms of directed acyclic graphs.
Cited by 2Open access
Luis A. Montiel, Claudia Zepeda, Pedro Wesche et al. · 2014
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Kathrin Konczak · publish.UP (University of Potsdam) · 2007
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Lierler, Yuliya · 2010
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Abeer Dyoub, Stefania Costantini, Giovanni De Gasperis · The Knowledge Engineering Review · 2018
Abstract In this paper, we discuss the potential role of answer set programming (ASP) in the context of approaches to the development of agents and multi-agent systems especially in the realm of Computational Logic. After shortly recalling the main (computational-logic-based) agent-oriented frameworks, we introduce ASP; then, we discuss the usefulness of a potential integration of the two paradigms in a modular heterogeneous framework, and the feasibility of such integration. This also in the more general view of improving and empowering flexibility of agent-oriented frameworks. Relevant literature will be mentioned and discussed. Possible future directions and potential developments will be outlined.
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Door Stijn Heymans, Promotor Prof, Stijn Heymans et al. · 2005
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Vladimir Lifschitz · 2008
Answer set programming (ASP) is a form of declarative programming oriented towards difficult search problems. As an outgrowth of research on the use of nonmonotonic reasoning in knowledge representation, it is particularly useful in knowledge-intensive applications. ASP programs consist of rules that look like Prolog rules, but the computational mechanisms used in ASP are different: they are based on the ideas that have led to the creation of fast satisfiability solvers for propositional logic.
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Selim T. Erdoğan, Vladimir Lifschitz · Lecture notes in computer science · 2003
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Peter Schüller · KI - Künstliche Intelligenz · 2018
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Jorge Fandinno, Seemran Mishra, Javier Romero et al. · Lecture notes in computer science · 2023
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Tomi Janhunen, Ilkka Niemelä · AI Magazine · 2016
In this article, we give an overview of the answer set programming paradigm, explain its strengths, and illustrate its main features in terms of examples and an application problem.
Cited by 44Open access
Jeroen Janssen, Steven Schockaert, Dirk Vermeir et al. · Atlantis computational intelligence systems · 2012
The study of extensions of classical ASP has received a great deal of attention over the past years, including the efforts of the European Working Group on Answer Set Programming (WASP) (Niemelä 2003). The main objectives of such a study are (1) researching the complexity and additional expressivity which certain extensions bring; (2) investigating whether extensions can be compiled to a core language that is easy to implement, or is already implemented. Certain interesting links have been brought to light in this research. For example, it has been shown that nested expressions can be translated to disjunctive logic programs (Pearce et al. 2002) and that aggregates can be translated to normal logic programs (Pelov et al. 2003). Next to these general extensions of ASP, the translation of other frameworks to ASP has also been studied. For example DLV supports abduction with penalization (Perri et al. 2005) through its front-end by compiling this framework to a logic program with weak constraints (Buccafurri et al. 2000). For preferences in ASP a common implementation method is to use a meta-formalism and first generate all answer sets for a program, and then filter the most preferred ones. Though the preference extensions have a higher complexity, this method ensures that programs with preferences can still be solved using off the shelf ASP solvers such as Smodels (Simons and Niemelä 2000) and DLV (Faber and Pfeifer 2005).
Cited by 1