  Socially assistive robots provide physical and mental assistance for humans
via cognitive human-machine interactions. These robots should sustain long-term
engaging interactions with humans in a similar way humans interact with each
other. According to the theory of mind, in their interactions humans develop
cognitive models of each other in order to estimate their unobservable
state-of-mind, predict their behavior, and act accordingly. Based on the theory
of mind, we propose mathematical cognitive models of humans, which enable
machines to understand cognitive procedures of humans in general and as
distinct individuals. In particular, a network representation that is
formulated based on a proposed extended version of fuzzy cognitive maps is
introduced. The resulting models are identified and validated using (1)
computer-based simulations designed according to a general data set of human's
intuitive reasoning and literature and (2) real-life personalised experiments
with 15 human participants. The results of the experiments show that the
proposed cognitive models can excellently be personalised to different
participants and precisely estimate and predict their current and future
state-of-mind and expected behaviors.
