An Affordance-Based Model of Human Action Selection in a Human–Machine Interaction System with Cognitive Interpretations

Abstract

Current technology is not sufficient to automate all desired tasks. Human–machine interaction (HMI) has thus become a key control and design factor for tasks requiring human-level decision-making or information synthesis. Such processes require a formal representation of human actions (including decision-making) when modeling HMI systems; however, successful prescriptive approaches to this end have still been elusive. This article extends the affordance-based finite state automata model, conditioning human prior experience and natural memory decay of task knowledge (or skill decay). The new model draws upon both reinforcement learning and natural memory decay for decision-making on action choice. An empirical study is carried out to specify how action choice is affected or updated by reinforcement learning based on past experience, and Wickelgren’s decay function is jointly employed to predict human decision-making behavior.

Additional information

Funding

This research was supported by the National Research Foundation of Korea (NRF), a grant funded by the Korea government (No. 2011-0028992).

Notes on contributors

Hokyoung Ryu

Hokyoung Ryu is a Professor in the Department of Arts & Technology, Hanyang University, Seoul, Korea. His current research interests are artistic thinking and innovations, cognitive systems engineering, mobile learning, and human–computer interaction, with a special focus on medical human–computer interaction.

Namhun Kim

Namhun Kim is an Assistant Professor in the Department of System Design and Control Engineering at Ulsan National Institute of Science and Technology, Korea. His research interests are human–machine interactions, agent-based modeling and simulations, and adaptive manufacturing processes.

Jangsun Lee

Jangsun Lee is a graduate student in the Department of Industrial Engineering, Hanyang University, Seoul, Korea. His research topics include human–computer interaction and information design for in-car navigation systems.

Dongmin Shin

Dongmin Shin is an Associate Professor in the Department of Industrial and Management Engineering at Hanyang University, Ansan, Korea. His research interests include human–automation interaction systems, context-aware computing, information system design, and discrete event system modeling and control.