150 citations · 619 across the 52 of their papers we have counts for
60 papers
Solving QBF with Counterexample Guided Refinement
Mikoláš Janota, William Klieber, Joao Marques-Silva +1
We propose two novel approaches for using Counterexample-Guided Abstraction Refinement (CEGAR) in Quantified Boolean Formula (QBF) solvers. The first approach develops a recursive…
Solving QBF by Clause Selection
Mikoláš Janota, Joao Marques-Silva
Algorithms based on the enumeration of implicit hitting sets find a growing number of applications, which include maximum satisfiability and model based diagnosis, among others. Th…
A Refutation of Shapley Values for Explainability
Xuanxiang Huang, Joao Marques-Silva
Recent work demonstrated the existence of Boolean functions for which Shapley values provide misleading information about the relative importance of features in rule-based explanat…
Explainability is NOT a Game
Joao Marques-Silva, Xuanxiang Huang
Explainable artificial intelligence (XAI) aims to help human decision-makers in understanding complex machine learning (ML) models. One of the hallmarks of XAI are measures of rela…
On Logic-Based Explainability with Partially Specified Inputs
Ramón Béjar, António Morgado, Jordi Planes +1
In the practical deployment of machine learning (ML) models, missing data represents a recurring challenge. Missing data is often addressed when training ML models. But missing dat…
Delivering Inflated Explanations
Yacine Izza, Alexey Ignatiev, Peter Stuckey +1
In the quest for Explainable Artificial Intelligence (XAI) one of the questions that frequently arises given a decision made by an AI system is, ``why was the decision made in this…