The field of computational models of argument is emerging as an important aspect of
artificial intelligence research. The reason for this is based on the recognition that if we are …

Decision-focused learning (DFL) is an emerging paradigm in machine learning which trains
a model to optimize decisions, integrating prediction and optimization in an end-to-end …

We present NeuroSAT, a message passing neural network that learns to solve SAT
problems after only being trained as a classifier to predict satisfiability. Although it is not …

A new approach for defining causality and such related notions as degree of responsibility,
degrees of blame, and causal explanation. Causality plays a central role in the way people …

This book provides an introduction to the mathematical and algorithmic foundations of data
science, including machine learning, high-dimensional geometry, and analysis of large …

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 …

Answer set programming (ASP) is a form of declarative programming oriented towards
difficult search problems. As an outgrowth of research on the use of nonmonotonic …

Knowledge-based Configuration incorporates knowledge representation formalisms to
capture complex product models and reasoning methods to provide intelligent interactive …

To endow computers with common sense is one of the major long-term goals of artificial
intelligence research. One approach to this problem is to formalize commonsense reasoning …

Scheduling problems have been investigated since the late fifties. Two types of applications
have mainly motivated research in this area: project planning and machine scheduling …