Active Learning of Greedy Algorithms by Means of Interactive Experimentation

摘要

Greedy algorithms are one of the most common algorithm design techniques. Despite their apparent simplicity, their design is a demanding task. As a consequence, they are usually taught and learnt in a passive way. In this paper, we make a new proposal aimed at active learning of greedy algorithms. The paper contains two main contributions. First, we introduce a novel approach to their active learning, based on experimentation with and evaluation of alternative greedy strategies for a given problem. Second, we present a family of interactive assistants designed to support this approach. The assistants were evaluated for their usability in real lab situations, having obtained high scores from students as well as useful information to enhance them.