— This paper presents a case study of learning to select behavioral primitives and generate subgoals from observation and practice. Our approach uses local features to generalize...
Darrin C. Bentivegna, Christopher G. Atkeson, Gord...
—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
Despite the recent resurgence of interest in learning methods for planning, most such efforts are still focused exclusively on classical planning problems. In this work, we invest...
A key difficulty in the maintenance and evolution of complex software systems is to recognize and understand the implicit dependencies that define contracts that must be respecte...
Bayesian learning, widely used in many applied data-modeling problems, is often accomplished with approximation schemes because it requires intractable computation of the posterio...