Answer-Set Programs for Repair Updates and Counterfactual Interventions
Leopoldo Bertossi
TL;DR
This paper discusses various answer-set programs with annotations used for database repairs, query answering, causality, and explanations in machine learning, illustrated through simple examples.
Contribution
It provides a unified overview of answer-set programming approaches for diverse database and AI tasks, highlighting their applications and differences.
Findings
Answer-set programs effectively specify database repairs and consistent query answering.
They enable modeling of secrecy views and counterfactual interventions.
Applications include causality analysis and explanations in machine learning.
Abstract
We briefly describe -- mainly through very simple examples -- different kinds of answer-set programs with annotations that have been proposed for specifying: database repairs and consistent query answering; secrecy view and query evaluation with them; counterfactual interventions for causality in databases; and counterfactual-based explanations in machine learning.
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Topic Modeling · Explainable Artificial Intelligence (XAI)