Abstraction for non-ground answer set programs

Saribatur, Zeynep Gozen; Eiter, Thomas; Schüller, Peter

doi:10.1016/j.artint.2021.103563

Cited by 7 publications

(4 citation statements)

References 56 publications

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“…This heuristic promotes that there are no major differences in terminology and the way information is presented between the real and virtual world. This finding suggests that the development of PHRs has so far focused on this approach, reducing the cognitive load [ 108 ]. This is relevant, as PHRs are intended for a wide audience, and their use should be as simple as possible [ 97 ].…”

Section: Discussionmentioning

confidence: 99%

Automated Workflow for Usability Audits in the PHR Realm

García‐Berná

Sobrino-Duque

Gea

et al. 2022

IJERPH

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Teleworking is an everyday reality in today’s world. Many companies are opting for this way of working remotely, as it improves employers’ satisfaction along with the work–life balance and it does not lead to any significant reduction in performance. In general, the comfortable use of software applications is a priority, and quality aspects such as usability are of vital importance. In this paper, a novel workflow is proposed. It consists of the following components: (1) a tool to manage usability evaluations based on expert reviews, called Usevalia; (2) a set of heuristics to be considered when performing the evaluations; (3) a catalogue of usability requirements to guide the evaluations; (4) a checklist related to the heuristics to verify that the evaluations have been performed correctly by all the auditors; and (5) a set of tasks to know in advance the functionality of the applications to be audited. Personal health records (PHRs) were taken as a case study. This methodology made it possible to involve a total of 31 working groups that evaluated the usability of four PHRs. Concerning the main results, the evaluators agreed that with Usevalia the usability evaluations could go a step further because they are coordinated and can work together remotely. In addition, analyses of usability scores provided insight into current proposals for improving usability of applications.

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Section: Discussionmentioning

confidence: 99%

Automated Workflow for Usability Audits in the PHR Realm

García‐Berná

Sobrino-Duque

Gea

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…For instance, Gebser et al (2008) use a meta-programming technique to explain why a given model is not an answer set of a given program. More recently, Eiter et al (2019) considered the explanation of ASP programs that have no answer sets in terms of the concept of abstraction (Saribatur et al 2021). This allows spotting which parts of a given domain are actually relevant for rising the unsatisfiability of the problem.…”

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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“…Answer-sets of this program contain an interpretation which is not an answer-set and the reasons why. Saribatur et al [33] employed Ouroboros in their abstraction method and used it to generate explanations for inconsistent instances in certain problem domains. Further debugging approaches are DWASP [7] and stepping [28].…”

Section: Introductionmentioning

confidence: 99%

Explainable Answer-set Programming

Geibinger

2023

Electron. Proc. Theor. Comput. Sci.

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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.

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Abstraction for non-ground answer set programs

Cited by 7 publications

References 56 publications

Automated Workflow for Usability Audits in the PHR Realm

Automated Workflow for Usability Audits in the PHR Realm

Model Explanation via Support Graphs

Explainable Answer-set Programming

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