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Biological as well as advanced artificial intelligences (AIs) need to decidewhich goals to pursue. We review nature's solution to the time allocationproblem, which is based on a continuously readjusted categorical weightingmechanism we experience introspectively as emotions. One observesphylogenetically that the available number of emotional states increases handin hand with the cognitive capabilities of animals and that raising levels ofintelligence entail ever larger sets of behavioral options. Our ability toexperience a multitude of potentially conflicting feelings is in this view nota leftover of a more primitive heritage, but a generic mechanism forattributing values to behavioral options that can not be specified at birth. Inthis view, emotions are essential for understanding the mind.  For concreteness, we propose and discuss a framework which mimics emotions ona functional level. Based on time allocation via emotional stationarity (TAES),emotions are implemented as abstract criteria, such as satisfaction, challengeand boredom, which serve to evaluate activities that have been carried out. Theresulting timeline of experienced emotions is compared with the `character' ofthe agent, which is defined in terms of a preferred distribution of emotionalstates. The long-term goal of the agent, to align experience with character, isachieved by optimizing the frequency for selecting individual tasks. Uponoptimization, the statistics of emotion experience becomes stationary.

Emotions as abstract evaluation criteria in biological and artificial intelligences