QUICKXPLAIN: Preferred Explanations and Relaxations for Over-Constrained Problems

By Ulrich Junker. AAAI Conference on Artificial Intelligence, 2004

512 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dee653e324b42311f93adf83611a985028f8b54c

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Citations in the corpus

Constraint-based recommender systems: technologies and research issues (2008, methodology): “The calculation of such repairs is based on the concepts of ModelBased Diagnosis [30] which is used to resolve conflicts [19].”
Recommender Systems (2016, unknown): “Methods for handing empty results in constraint-based systems, such as fast discovery of small conflicting sets, and minimal relaxations are discussed in [198, 199, 273, 274, 289, 419, 574].”
Automated feedback generation for introductory programming assignments (2012, methodology): “t applicable in our setting as such redundancy is not present in introductory programming problems. 6.3 Automated Debugging and Fault localization Techniques like Delta Debugging [30] and QuickXplain [19] aim to simplify a failing test case to a minimal test case that still exhibits the same failure”
Effective Algorithms for the Satisfiability of Quantifier-Free Formulas Over Linear Real and Integer Arithmetic (2014, methodology): “Using the variant of QuickXplain in [72], minimizing the conflict may require up to 2k · log |E | k + 2k additional consistency checks (where k is the number of basic variables in the minimal conflict).”
The Sat4j library, release 2.2 (2010, background): “In that case, the optimization function should minimize Σ ni =1 w i l i where l i is the literal in the unit clause C i .”
A systematic review and taxonomy of explanations in decision support and recommender systems (2017, background): “A few other relevant studies (Nunes et al. 2014; Junker 2004; Carenini andMoore 2001) were also”