Enhancing Human-computer Interaction through Use of Embedded COGNET Models

Abstract

Current research suggests that embedded user models would allow more intelligent and helpful human-computer interaction. However, until recently, the cognitive modeling technology capable of generating models sufficiently complete and detailed as would be needed to drive development of embedded user models has not existed. COGNET (COGnition as a Network of Tasks) is a new cognitive modeling framework focusing on real-time, multi-tasking domains, and has been successfully applied and validated in several domains. This paper describes research to explore COGNET for embedded user models. The research took an existing COGNET model and implemented it in software as an embedded user model for an intelligent human-computer interface. The HCI used the resulting embedded model to produce significantly enhanced human-computer interface functionality. Specifically, the model was used to provide attention aiding, dynamic task prioritization, context-sensitive decision structuring, and context sensitive task automation in a complex real-time vehicle monitoring task. The software used to implement the example model also proved generalizable to other domains and COGNET models. This software has been named BATON (Blackboard Architecture for Task-Oriented Networks). Similarly, the interface architecture is broadly applicable to providing these same functions in other real-time, multi-tasking domains.