It is often thought that learning algorithms that track the best solution, as opposed to converging to it, are important only on nonstationary problems. We present three results s...
Self-explaining has been repeatedly shown to result in positive learning outcomes for students in a wide variety of disciplines. However, there are two potential accounts for why s...
In Multi-Agent learning, agents must learn to select actions that maximize their utility given the action choices of the other agents. Cooperative Coevolution offers a way to evol...
The problem of coalition formation when agents are uncertain about the types or capabilities of their potential partners is a critical one. In [3] a Bayesian reinforcement learnin...
Many learning tasks in adversarial domains tend to be highly dependent on the opponent. Predefined strategies optimized for play against a specific opponent are not likely to succ...
Achim Rettinger, Martin Zinkevich, Michael H. Bowl...