Abstract. We present a new approach for developing robust software applications that breaks dependences on the failed parts of an application’s execution to allow the rest of the...
—Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not complet...
Eric K. Garcia, Sergey Feldman, Maya R. Gupta, San...
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
Abstract. [Context and motivation] With increasing use of software, quality attributes grow in relative importance. Robustness is a software quality attribute that has not received...
As knowledge bases move into the landscape of larger ontologies and have terabytes of related data, we must work on optimizing the performance of our tools. We are easily tempted t...