Abstract
AbstractDecision systems for solving real-world combinatorial problems must be able to report infeasibility in such a way that users can understand the reasons behind it, and determine how to modify the problem to restore feasibility. Current methods mainly focus on reporting one or more subsets of the problem constraints that cause infeasibility. Methods that also show users how to restore feasibility tend to be less flexible and/or problem-dependent. We describe a problem-independent approach to feasibility restoration that combines existing techniques from the literature in novel ways to yield meaningful, useful, practical, and flexible user support. We evaluated the resulting framework on three real-world applications and conducted a qualitative expert user study with participants from different application domains.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computational Theory and Mathematics,Discrete Mathematics and Combinatorics,Software
Reference38 articles.
1. Liffiton, M. H., & Sakallah, K. A. (2008). Algorithms for computing minimal unsatisfiable subsets of constraints. Journal of Automated Reasoning, 40(1), 1–33. https://doi.org/10.1007/s10817-007-9084-z
2. Guieu, O., & Chinneck, J. W. (1999). Analyzing infeasible mixed-integer and integer linear programs. INFORMS Journal on Computing, 11(1), 63–77. https://doi.org/10.1287/ijoc.11.1.63
3. Junker, U. (2004). QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems. In D. L. McGuinness, & G. Ferguson (Eds.), Proceedings of the Nineteenth National Conference on Artificial Intelligence, Sixteenth Conference on Innovative Applications of Artificial Intelligence, July 25–29, 2004, San Jose, California, USA (pp. 167–172). AAAI Press / The MIT Press. http://www.aaai.org/Library/AAAI/2004/aaai04-027.php
4. Liffiton, M. H., & Malik, A. (2013). Enumerating Infeasibility: Finding Multiple MUSes Quickly. In C. Gomes & M. Sellmann (Eds.), Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (pp. 160–175). Springer.
5. Leo, K., & Tack, G. (2017). Debugging Unsatisfiable Constraint Models. In D. Salvagnin, & M. Lombardi (Eds.), CPAIOR 2017 (pp. 77–93).