EAGER: Automatically Generating Formal Human-computer Interface Designs from Task Analytic Models
Sponsor: The National Science Foundation - Cyber-Human Systems
The concurrent nature of human-computer interaction can result in situations unanticipated by designers where usability is not properly maintained or human operators may not be able to complete the task goals the system was designed to support. Mathematical tools and techniques called formal methods exist for modeling and providing proof-based evaluations of different elements of HCI including the human-computer interface, the human operator's task analytic behavior, and usability. Unfortunately, these approaches require engineers to create formal models of the interface designs, something that is not standard practice and prone to modeling error. This work strives to address this problem by showing that a machine learning approach can be used to automatically generate formal human-computer interface designs guaranteed to always adhere to usability properties and support human operator tasks.
Publications
Bolton, M. L., Ebrahimi, S. (ND). An approach to generating human-computer interfaces from task models. Submitted to the AAAI 2014 Symposium on Modeling in Human-machine Systems: Challenges for Formal Verification. (pp. 92-97). Palo Alto: AAAI.
Bolton, M. L, Ebrahimi, S., & Li, M. (2014, March 23) An Approach to Generating Human-computer Interfaces from Task Models. Poster presented at the AAAI 2014 Symposium on Modeling in Human-machine Systems: Challenges for Formal Verification. Palo Alto.
All material is based upon work supported by the National Science Foundation under Grant No. 1429910