While there has been a significant amount of theoretical and empirical research on the multiple-instance learning model, most of this research is for concept learning. However, f...
Constraint Satisfaction Problems are ubiquitous in Artificial Intelligence. Over the past decade significant advances have been made in terms of the size of problem instance tha...
Margarita Razgon, Barry O'Sullivan, Gregory M. Pro...
Data mining techniques have become central to many applications. Most of those applications rely on so called supervised learning algorithms, which learn from given examples in th...
Abstract. This paper proposes a novel approach to discover options in the form of conditionally terminating sequences, and shows how they can be integrated into reinforcement learn...
Here we advocate an approach to learning hardware based on induction of finite state machines from temporal logic constraints. The method involves training on examples, constraint...
Marek A. Perkowski, Alan Mishchenko, Anatoli N. Ch...