Recent work proposed a toolkit PySAT aiming at fast and easy prototy** with propositional
satisfiability (SAT) oracles in Python, which enabled one to exploit the power of the original …

Random forests have long been considered as powerful model ensembles in machine
learning. By training multiple decision trees, whose diversity is fostered through data and …

The successes of machine learning in recent years have triggered a fast growing range of
applications. In important settings, including safety critical applications and when …

Argumentation is an active area of modern artificial intelligence (AI) research, with
connections to a range of fields, from computational complexity theory and knowledge …

This work describes Sturgeon, a system for tile-based level generation using constraints. We
present a small mid-level constraint API that can be instantiated with various low-level …

Decision trees play an important role both in Machine Learning and Knowledge
Representation. They are attractive due to their immediate interpretability. In the spirit of …

This paper introduces a new anytime algorithm for Weighted MaxSAT consisting of two main
algorithmic components. First, we propose a new efficient polarity selection heuristic and an …

EvalMaxSAT1 is a MaxSAT solver written in modern C++ language mainly using the
Standard Template Library (STL). The solver is built on top of the SAT solver Glucose [1], but …

The practical success of Boolean Satisfiability (SAT) solvers stems from the CDCL (Conflict-
Driven Clause Learning) approach to SAT solving. However, from a propositional proof …

Conflict-driven clause learning (CDCL) is at the core of the success of modern SAT solvers.
In terms of propositional proof complexity, CDCL has been shown as strong as general …