Emotionally-Informed Decisions: Bringing Gut's Feelings into Self-adaptive and Co-adaptive Software Systems

TL;DR

This paper explores integrating human-like emotional decision-making into self-adaptive software systems, proposing algorithms and architectures that leverage emotions to improve uncertainty handling and adaptation.

Contribution

It introduces novel algorithms and architectures that incorporate emotional insights into software decision-making, bridging human emotional intelligence with autonomous systems.

Findings

Algorithms for emotion-based decision-making in software.

Architectures utilizing emotions to quantify uncertainty.

Enhanced adaptability through emotional integration.

Abstract

Software systems now complement an incredibly vast number of human activities, and much effort has been deployed to make them quasi-autonomous with the build-up of increasingly performant self-adaptive capabilities, so that the burden of failure, interruption and functional loss requiring expert intervention is fewer and far in between. Even as software systems are rapidly gaining skills that beat humans', humans retain greatly superior adaptability, especially in the context of emotionally-informed decisions and decisions under uncertainty; that is to say, self-adaptive and co-adaptive software systems have yet to acquire a "gut's feeling". This provides the double opportunity to conceptualize human-inspired processes of decision-making under uncertainty in the self-adaptive part of a software, as well as to source human unique emotional competences in co-adaptive architectures. In this paper, some algorithms are discussed that can provide software systems with realistic decision-making, and some architectures are conceptualized that resort to human emotions to quantify uncertainty and to contribute in the software's adaptation process.