Much of the focus on explanation in the field of artificial intelligence has focused on machine
learning methods and, in particular, concepts produced by advanced methods such as …

Decision 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 understand …

Quantified Constraints in Twenty Seventeen Page 1 Quantified Constraints in Twenty Seventeen
Barnaby Martin School of Engineering and Computing Sciences, University of Durham, UK …

The research field of optimization under uncertainty, even though already established in the
middle of the last century, gained much attention in the last two decades. The necessity to …

Interactive constraint systems often suffer from infeasibility (no solution) due to conflicting
user constraints. A common approach to recover infeasibility is to eliminate the constraints …

Decision 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 …

We introduce an enhanced notion of unsatisfiable cores for QBF in prenex CNF that allows
to weaken universal quantifiers to existential quantifiers in addition to the traditional removal …

This thesis develops the first application of counterfactual explanations to optimal solutions
of discrete optimization problems. The techniques studied respond to a contrastive question …

Game theory is concerned with reasoning about strategic interaction between self-interested
entities. This theory was not originally meant for computational settings, but researchers …