Cognitive-Level Salience for Explainable Artificial Intelligence
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Authors
Somers, Sterling
Mitsopoulos, Konstantinos
Lebiere, Christian
Thomson, Robert
Issue Date
2019-07
Type
Conference presentations, papers, posters
Language
Keywords
computational model , saliencea , Artificial Intelligence , reinforcement learning
Alternative Title
Abstract
We present a general-purpose method for determining the salience of features in action decisions of artificial intelligent agents. Our method does not rely on a specific implementation of an AI (e.g. deep-learning, symbolic AI). The method is also amenable to features at different levels of abstraction. We present three implementations of our salience technique: two directed at explainable artificial intelligence (deep reinforcement learning agents), and a third directed at risk assessment.
Description
Citation
Somers, Sterling, K. Mitsopoulos, Christian Lebiere, and Robert Thomson. "Cognitive-level salience for explainable artificial intelligence." In Proceedings of the 17th Annual Meeting of the International conference on Cognitive Modeling, pp. 19-22. 2019.
Publisher
International Conference on Cognitive Modeling