Human Emotion Verification by Action Languages via Answer Set Programming

2026-01-19

AI Summary

This work addresses the modeling and verification of dynamic human emotion evolution within observable behavioral sequences by introducing C-MT, a novel action language that integrates answer set programming, transition systems, and appraisal theories of emotion. Emotions are formally represented as multidimensional psychological constructs. The approach innovatively incorporates “forbids to cause” causal rules and specialized expressions tailored to the evolution of mental states, enabling formal modeling grounded in diverse psychological principles and facilitating trajectory comparison. Through trajectory constraints and invariance analysis, the framework supports controllable reasoning, formal verification, and rationality assessment of emotional dynamics, thereby providing both theoretical foundations and computational tools for the design of emotionally aware autonomous agents.

Technology Category

• Knowledge Representation and Reasoning: Action, Change, and Causality
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this paper, we introduce the action language C-MT (Mind Transition Language). It is built on top of answer set programming (ASP) 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. With the objective to address the need for controlled agent behaviors and to restrict unwanted mental side-effects of actions, we extend the language with a novel causal rule, forbids to cause, along with expressions specialized for mental state dynamics, which enables the modeling of principles for valid transitions between mental states. These principles of mental change are translated into transition constraints, and properties of invariance, which are rigorously evaluated using transition systems in terms of so-called trajectories. This enables controlled reasoning about the dynamic evolution of human mental states. Furthermore, the framework supports the comparison of different dynamics of change by analyzing trajectories that adhere to different psychological principles. We apply the action language to design models for emotion verification. Under consideration in Theory and Practice of Logic Programming (TPLP).

Problem

• Emotion Verification
• Mental State Dynamics
• Action Language
• Transition Systems
• Appraisal Theory

Innovation

• Answer Set Programming
• Action Language
• Mental State Dynamics
• Emotion Verification
• Transition Systems