Emotions as Abstract Evaluation Criteria in Biological and Artificial Intelligences.

Claudius Gros

doi:10.3389/fncom.2021.726247

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.

Highlights

Humans draw their motivations from short- and long term objectives evolving continuously with new experiences (Huang and Bargh, 2014)
Other state variables could be derived from utility optimization, like water and energy uptake, or appraisal concepts (Moerland et al, 2018), with the latter being examples for the abstract evaluation criteria used in the time allocation via emotional stationarity (TAES) framework
TAES serves in this context as an example for the working of emotions in terms of abstract evaluation criteria

Summary

Introduction

Humans draw their motivations from short- and long term objectives evolving continuously with new experiences (Huang and Bargh, 2014). We argue that this strategy is dictated in particular by the fact that the amount of information an agent disposes about the present and the future state of the world is severely constraint, given that forecasting is intrinsically limited by successively stronger complexity barriers (Gros, 2012). Corresponding limitations hold for the time available for decision making and for the computational power of the supporting hard- or wetware (Zenon et al, 2019; Lieder and Griffiths, 2020), independently of whether the acting agent is synthetic or biological. Nature disposed us with an emotional control system. It is argued, in consequence, that an improved understanding of the functional role of emotions is essential for theories of the mind

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Conclusion