Emotions as abstract evaluation criteria in biological and artificial intelligences
This work addresses the fundamental challenge of goal selection in AI and cognitive science by framing emotions as essential for understanding the mind, offering a novel perspective that could influence AI design and cognitive theories.
The paper tackles the problem of how biological and artificial intelligences decide which goals to pursue by proposing that emotions serve as a generic mechanism for attributing values to behavioral options, and it introduces a framework called time allocation via emotional stationarity (TAES) that implements emotions as abstract criteria to optimize task selection and achieve emotional stationarity.
Biological as well as advanced artificial intelligences (AIs) need to decide which goals to pursue. We review nature's solution to the time allocation problem, which is based on a continuously readjusted categorical weighting mechanism we experience introspectively as emotions. One observes phylogenetically that the available number of emotional states increases hand in hand with the cognitive capabilities of animals and that raising levels of intelligence entail ever larger sets of behavioral options. Our ability to experience a multitude of potentially conflicting feelings is in this view not a leftover of a more primitive heritage, but a generic mechanism for attributing values to behavioral options that can not be specified at birth. In this view, emotions are essential for understanding the mind. For concreteness, we propose and discuss a framework which mimics emotions on a functional level. Based on time allocation via emotional stationarity (TAES), emotions are implemented as abstract criteria, such as satisfaction, challenge and boredom, which serve to evaluate activities that have been carried out. The resulting timeline of experienced emotions is 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 individual tasks. Upon optimization, the statistics of emotion experience becomes stationary.