Matthew A. Grimm, Gilbert L. Peterson, Michael E. Miller
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Imitating human responses via a Dual-Process Model
Advancements in autonomy are leading to an increased need for machines capable of collaborative effort with humans to achieve team goals. One way of enhancing these human-autonomous system work arrangements leverages the concept of a shared mental model. The idea being that when the human and autonomous teammate have aligned models, the team is more productive due to an increase in trust, predictiveness, and apparent understanding. An open issue is how to have autonomous teammates learn a user aligned mental model. This research presents a dual-process learning model that leverages multivariate normal probability density functions (DPL-MN) to extrapolate state-responses into system 2. By leveraging dual-process learning concepts, an autonomous teammate is able to rapidly align with a user and extrapolate their consistencies into longer term memory. Evaluation of DPLM with user responses from a game called Space Navigator shows that DPL-MN accurately responds to situations similarly to each unique user.
期刊介绍:
Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial.
The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition.
Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.