The problem of planning for a robot that operates in environments containing a large
number of objects, taking actions to move itself through the world as well as to change the …

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes
them be identified with the whole AI by many non-computer-scientists and non-technical …

Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models.
The interpretability of decision trees motivates explainability approaches by so-called …

One of the most important paradigm shifts in the use of SAT solvers for solving industrial
problems has been the introduction of clause learning. Clause learning entails adding a …

The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

The IC3 algorithm was recently introduced for proving properties of finite state reactive
systems. It has been applied very successfully to hardware model checking. We provide a …

Abstract Explanations of Machine Learning (ML) models often address a question. Such
explanations can be related with selecting feature-value pairs which are sufficient for the …

The problem of enumerating minimal unsatisfiable subsets (MUSes) of an infeasible
constraint system is challenging due first to the complexity of computing even a single MUS …

Within the last decade, abstract argumentation has emerged as a central field in Artificial
Intelligence. Besides providing a core formalism for many advanced argumentation systems …

Abstract Maximum Satisfiability (MaxSAT) is an optimization version of SAT, and many real
world applications can be naturally encoded as such. Solving MaxSAT is an important …