  Given a certain complexity level, humanized agents may select from a wide
range of possible tasks, with each activity corresponding to a transient goal.
In general there will be no overarching credit assignment scheme allowing to
compare available options with respect to expected utilities. For this
situation we propose a task selection framework that is based on time
allocation via emotional stationarity (TAES). Emotions are argued to correspond
to abstract criteria, such as satisfaction, challenge and boredom, along which
activities that have been carried out can be evaluated. The resulting timeline
of experienced emotions is then compared with the `character' of the agent,
which is defined in terms of a preferred distribution of emotional states. The
long-term goal of the agent, to align experience with character, is achieved by
optimizing the frequency for selecting the individual tasks. Upon optimization,
the statistics of emotion experience becomes stationary.
