Towards Natural Cognitive System Training Interactions: A Preliminary Framework
Proceedings of the AAAI 2018 Spring Symposium on the User Experience of Artificial Intelligence
2018
Abstract
Researchers have developed cognitive systems capable of human-level performance at complex tasks (e.g., Watson and AlphaGo), but constructing these systems required substantial time and expertise. To address this challenge, a new line of research has begun to coalesce around the concept of cognitive systems that users can teach rather than program. A key goal of this research is to develop natural approaches for end users to directly train these systems to perform new tasks. However, what makes training interactions natural remains an open research question that we begin to explore in this paper. To lay the foundation for this exploration, we review the human-computer interaction literature to identify characteristics of systems that have historically been natural for end users to interact with. Based on this review, we propose a framework for cognitive system training interactions that decomposes interaction into patterns, types, and modalities, all of which support the acquisition of different kinds of knowledge. Finally, we discuss how this framework characterizes existing research within this space and how it can guide future research.
BibTeX
@inproceedings{harpstead-aaai-sss-2018,
title = {Towards Natural Cognitive System Training Interactions: A Preliminary Framework},
author = {Harpstead, Erik and MacLellan, Christopher J. and Marinier, Robert P. and Koedinger, Kenneth R.},
booktitle = {Proceedings of the AAAI 2018 Spring Symposium on the User Experience of Artificial Intelligence},
year = {2018},
}