Researchers develop logic-based system to verify human emotions from behavior
Scientists have created a computational framework that can track and verify how human emotions change in response to observable actions, using formal logic and psychology theory. The work could shape how AI systems that interact with people—from customer service bots to mental health apps—are designed and audited for unintended emotional influence.
Originaltitel: Human Emotion Verification by Action Languages via Answer Set Programming
Abstract In this paper, we introduce the action language C-MT (Mind Transition Language), built on top of answer set programs and transition systems to represent how human mental states evolve in response to sequences of observable actions. Drawing on well-established psychological theories, such as the Appraisal Theory of Emotion, we formalize mental states, such as emotions, as multi-dimensional configurations. To enable controlled agent behavior and limit undesirable effects such as undue psychological influence, we introduce a novel causal rule, forbids to cause, together with constructs tailored to mental state dynamics. These allow the specification of valid transitions as constraints and invariance properties, which are rigorously evaluated over trajectories in transition systems. The framework supports reasoning about and comparing different dynamics of mental change under varying constraints. We apply the action language to design models for emotion verification.