A generic framework for task selection driven by synthetic emotions

TL;DR

This paper introduces TAES, a task selection framework for humanized agents that uses emotional stationarity to align task choices with an agent's character, enabling adaptive and emotionally consistent behavior.

Contribution

It proposes a novel emotion-based task selection method that optimizes long-term emotional experience to match an agent's character, without relying on explicit utility calculations.

Findings

Emotion-based task selection achieves stable emotional states.

Agents can adapt tasks to match their character over time.

The framework promotes emotionally consistent behavior.

Abstract

Given a certain complexity level, humanized agents may select from a wide range of possible tasks, with each activity corresponding to a transient goal. In general there will be no overarching credit assignment scheme allowing to compare available options with respect to expected utilities. For this situation we propose a task selection framework that is based on time allocation via emotional stationarity (TAES). Emotions are argued to correspond to abstract criteria, such as satisfaction, challenge and boredom, along which activities that have been carried out can be evaluated. The resulting timeline of experienced emotions is then 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 the individual tasks. Upon optimization, the statistics of emotion experience becomes stationary.