Emotions as abstract evaluation criteria in biological and artificial intelligences

TL;DR

The paper explores how emotions function as abstract evaluation criteria in biological and artificial intelligences, proposing a framework that mimics emotional evaluation to guide goal pursuit and decision-making.

Contribution

It introduces a novel framework called time allocation via emotional stationarity (TAES) that models emotions as abstract criteria for evaluating activities in AI systems.

Findings

Emotions serve as essential value attribution mechanisms in intelligent agents.

The TAES framework enables AI to optimize task selection based on emotional state statistics.

The model aligns emotional experience with agent character through long-term optimization.

Abstract

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.