This paper explores the use of filtering heuristics based on action models for automated planning to augment sequence mining techniques.
We present an approach that converts human advice into synthetic or imagined training experiences, serving to scaffold the low-level representations of simple, reactive learning systems such as reinforcement learners.
We consider the use of an advanced cryptographic technique called secure multi-party computation to enable coalition members to achieve joint objectives while still meeting privacy requirements.
We present an explanation framework based on the notion of explanation drivers —i.e., the intent or purpose behind agent explanations. We focus on explanations meant to reconcile expectation violations and enumerate a set of triggers for proactive explanation.
We discuss an approach in which the virtual environment is semantically instrumented in order to allow for the tracking of and reasoning about open-ended learner activity therein.
This paper reports on an approach to creating solution models for automated skill assessment using an example-based methodology, specifically targeting domains for which solution models must support robustness to learner mistakes.
This paper presents an approach to automated assessment for online training based on approximate graph matching.
This paper presents an approach to training in VEs that directly addresses these challenges and summarizes its application to a weapons maintenance task.
This paper reports on a concept validation study that provides an empirical basis for the design of solution authoring frameworks based on end-user programming techniques.