The design of efficient and generic algorithms for solving combinatorial optimization
problems has been an active field of research for many years. Standard exact solving …

In this paper, we present MachSMT, an algorithm selection tool for Satisfiability Modulo
Theories (SMT) solvers. MachSMT supports the entirety of the SMT-LIB language. It employs …

This paper presents MachSMT, an algorithm selection tool for Satisfiability Modulo Theories
(SMT) solvers. MachSMT supports the entirety of the SMT-LIB language and standardized …

This paper applies machine learning (ML) to solve quantified satisfiability modulo theories
(SMT) problems more efficiently. The motivating idea is that the solver should learn from …

Abstract Machine learning and logical reasoning have been the two foundational pillars of
Artificial Intelligence (AI) since its inception, and yet, until recently the interactions between …

Abstract We introduce Monte Carlo Forest Search (MCFS), a class of reinforcement learning
(RL) algorithms for learning policies in tree MDPs, for which policy execution involves …

With artificial intelligence (AI) being well established within the daily lives of research
communities, we turn our gaze toward an application area that appears intuitively unsuited …

Over the last two decades, we have seen a dramatic improvement in the efficiency of conflict-
driven clause-learning Boolean satisfiability (CDCL SAT) solvers over industrial problems …

Automated reasoning (AR) and machine learning (ML) are two of the foundational pillars of
artificial intelligence (AI) and yet have developed largely independently. The integration of …

The efficiency of" conflict-based Clause-Learning Boolean Fulfilment (CDCLSAT)" solvers
on engineering problems from several fields has been seeing notable modifications during …