$Q$-Mapping: Learning User-Preferred Operation Mappings With Operation-Action Value Function

User interfaces have been designed to fit typical users and their usage styles as assumed by designers. However, it is impossible to cover all the possible use cases. To address this problem, we propose
$Q$
-Mapping, which is a method for user interfaces to acquire the operation mapping, or mapping from user operations to their effects.
$Q$
-Mapping has an advantage over previous techniques in that it can acquire operation mapping interactively. The core idea of
$Q$
-Mapping is that what a user selects as an ideal action has a tendency to be the same as the action that has the highest
$Q$
-value. On the basis of this concept, we defined the operation-action value function, which can be calculated from the value that a user expects to gain when a particular mapping is given in that state and is updated each time an operation occurs. We conducted a simulation experiment and a user study to investigate the
$Q$
-Mapping performance and the effects of the acquisition of interactive operation mapping. The simulation results showed that the changeability of operation mapping could be controlled by a coefficient called the balancing parameter. As for the user study, we found that
$Q$
-Mapping with a balancing parameter that decays with time was able to acquire operation mapping that was easy for users to understand. These results demonstrate the importance of balancing consistency and adaptability in the interactive acquisition of operation mapping.