This paper describes the recently developed genetic programming paradigm which genetically breeds populations of computer programs to solve problems. In genetic programming, the i...
Automatically acquiring control-knowledge for planning, as it is the case for Machine Learning in general, strongly depends on the training examples. In the case of planning, examp...
Our research goal is to design systems that enable humans to teach tedious, repetitive, simple tasks to a computer. We propose here a learner/problem solver architecture for such ...
In the information regularization framework by Corduneanu and Jaakkola (2005), the distributions of labels are propagated on a hypergraph for semi-supervised learning. The learnin...
The purpose of the current study was to test whether we could create a system where students can learn by teaching a live machine-learning agent. SimStudent is a computer agent tha...
Noboru Matsuda, Victoria Keiser, Rohan Raizada, Ga...