  Maximum Causal Entropy (MCE) Inverse Optimal Control (IOC) has become an
effective tool for modelling human behaviour in many control tasks. Its
advantage over classic techniques for estimating human policies is the
transferability of the inferred objectives: Behaviour can be predicted in
variations of the control task by policy computation using a relaxed optimality
criterion. However, exact policy inference is often computationally intractable
in control problems with imperfect state observation. In this work, we present
a model class that allows modelling human control of two tasks of which only
one be perfectly observed at a time requiring attention switching. We show how
efficient and exact objective and policy inference via MCE can be conducted for
these control problems. Both MCE-IOC and Maximum Causal Likelihood (MCL)-IOC, a
variant of the original MCE approach, as well as Direct Policy Estimation (DPE)
are evaluated using simulated and real behavioural data. Prediction error and
generalization over changes in the control process are both considered in the
evaluation. The results show a clear advantage of both IOC methods over DPE,
especially in the transfer over variation of the control process. MCE and MCL
performed similar when training on a large set of simulated data, but differed
significantly on small sets and real data.
