Publisher Summary The past few years have seen enormous progress in the performance of
Boolean satisfiability (SAT) solvers. Despite the worst-case exponential run time of all known …

The past five years have seen dramatic advances in planning algorithms, with an emphasis
on propositional methods such as GRAPHPLAN and compilers that convert planning …

Stochastic resetting is prevalent in natural and man-made systems, giving rise to a long
series of nonequilibrium phenomena. Diffusion with stochastic resetting serves as a …

We propose UTSP, an Unsupervised Learning (UL) framework for solving the Travelling
Salesman Problem (TSP). We train a Graph Neural Network (GNN) using a surrogate loss …

First passage under restart has recently emerged as a conceptual framework suitable for the
description of a wide range of phenomena, but the endless variety of ways in which restart …

The set of novelties introduced with the SAT solver Glucose is now considered as a standard
for practical SAT solving. In this paper, we review the different strategies and technologies …

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 …

Constraint programming is a powerful paradigm for solving combinatorial search problems
that draws on a wide range of techniques from artificial intelligence, computer science …

Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and
reduce the realm of possibilities to effectively focus on the possible, allowing for a natural …

Handbook of Knowledge Representation describes the essential foundations of Knowledge
Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up …