Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins...
This paper documents an experiment designed to show the value of simulation in understanding the relationship between production run lengths and overall supply chain performance. ...
David J. Parsons, Robin J. Clark, Kevin L. Payette
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
This paper examines the identification of multi-input systems. Motivated by an experiment design problem (should one excite the various inputs simultaneously or separately), we ex...
Michel Gevers, Ljubisa Miskovic, Dominique Bonvin,...
A number of today's state-of-the-art planners are based on forward state-space search. The impressive performance can be attributed to progress in computing domain independen...