xASP: An Explanation Generation System for Answer Set Programming

Trieu, Ly Ly; Son, Tran Cao; Balduccini, Marcello

doi:10.1007/978-3-031-15707-3_28

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The formal theory of justifications was also extended to deal with nested graph-based justifications (Marynissen 2022) and is actually a more general framework that allows covering other logic programming semantics. System xASP (Trieu et al 2022) generates explanation graphs previously proposed by Pontelli and Son (2006) and also uses an ASP meta-programming encoding. In the case of s(CASP), it proceeds in a top-down manner, building the explanation as an ordered list of literals extracted from the goal-driven satisfaction of the query.…”

Section: Related Workmentioning

confidence: 99%

Model Explanation via Support Graphs

CABALAR,

MUÑIZ

2024

Theory and Practice of Logic Programming

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In this note, we introduce the notion of support graph to define explanations for any model of a logic program. An explanation is an acyclic support graph that, for each true atom in the model, induces a proof in terms of program rules represented by labels. A classical model may have zero, one or several explanations: when it has at least one, it is called a justified model. We prove that all stable models are justified, whereas, for disjunctive programs, some justified models may not be stable. We also provide a meta-programming encoding in Answer Set Programming that generates the explanations for a given stable model of some program. We prove that the encoding is sound and complete, that is, there is a one-to-one correspondence between each answer set of the encoding and each explanation for the original stable model.

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Section: Related Workmentioning

confidence: 99%

Model Explanation via Support Graphs

CABALAR,

MUÑIZ

2024

Theory and Practice of Logic Programming

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“…The emergence of XAI has brought significant attention from researchers in ASP community, resulting in numerous proposed systems aimed at addressing this issue such as xclingo [4], DiscASP [9], xASP [19], exp(ASP c ) [18]. However, these systems are incapable of handling one or more of the following scenarios: (i) false atoms can be explained by the system, (ii) the ability to support certain advanced language features.…”

Section: Introductionmentioning

confidence: 99%

“…However, these systems are incapable of handling one or more of the following scenarios: (i) false atoms can be explained by the system, (ii) the ability to support certain advanced language features. In this paper, we proposed an improvement system, called xASP2 , that takes inspiration from the approach used in xASP [19] and [16]. xASP2 are able to substantially increase scalability and breadth of supported language features while producing explanation graphs with more immediately and consistently useful to users.…”

Section: Introductionmentioning

confidence: 99%

Explanations for Answer Set Programming

Alviano,

Trieu,

Cao Son

et al. 2023

Electron. Proc. Theor. Comput. Sci.

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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.

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The XAI system for answer set programming xASP2

Alviano,

Ly Trieu,

Cao Son

et al. 2024

Journal of Logic and Computation

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Explainable artificial intelligence (XAI) aims at addressing complex problems by coupling solutions with reasons that justify the provided answer. In the context of Answer Set Programming (ASP) the user may be interested in linking the presence or absence of an atom in an answer set to the logic rules involved in the inference of the atom. Such explanations can be given in terms of directed acyclic graphs (DAGs). This article reports on the advancements in the development of the XAI system xASP by revising the main foundational notions and by introducing new ASP encodings to compute minimal assumption sets, explanation sequences, and explanation DAGs. DAGs are shown to the user in an interactive form via the xASP navigator application, also introduced in this work.

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xASP: An Explanation Generation System for Answer Set Programming

Cited by 5 publications

References 6 publications

Model Explanation via Support Graphs

Model Explanation via Support Graphs

Explanations for Answer Set Programming

The XAI system for answer set programming xASP2

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