Short Paper: A Universal Moral Grammar (UMG) Ontology

This paper describes an OWL ontology that is a Universal Moral Grammar (UMG). UMG has been hypothesized by students of Chomsky to play the same role in human ethics as Universal Grammar (UG) does in Linguistics. I.e., the UMG describes an innate genetic phenotype of moral reasoning just as UG describes the Language Faculty. This approach utilizes the modular view of the mind developed by Chomsky and currently utilized by many evolutionary psychology researchers. In this paper I describe the ontology and how it represents ethical choices, rules, scenarios, systems, and biological models. This includes representation of choices governed by the golden rule, utilitarianism, and Moral Foundations Theory. The foundation for the model is an Artificial Intelligence model of events, plans, and decisions. This plan model can represent what is known as Theory of Mind (TOM). The TOM model is extended to the ethical domain by an analysis of recent research in evolutionary psychology and the representation of 40 different scenarios such as the trolley problem from the philosophical, psychological, and biological literature. The ontology is an example of how semantic technology can be used to provide mathematical rigor to the study of human ethics. For example, while utilitarianism seems intuitively obvious, it turns out that simply formalizing what exactly is being maximized is non-trivial and leads to insights about some potential flaws in the theory. This version demonstrates the breadth of the UMG, that it is capable of representing many diverse examples from the literature. However, there are already meaningful results from this version such as a resolution to Hume’s Is-Ought problem. In future versions I plan to develop detailed models based on game theory that can shed insight on questions such as the evolutionary explanation for our moral sense. I also plan to integrate the UMG with AI agents to demonstrate how it can define moral guidelines and boundaries for such agents.