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Taylor Olson

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

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AAMAS Conference 2025 Conference Paper

Reasoning and Planning with Dynamic Social Norms

Taylor Olson
Roberto Salas-Damian
Kenneth D. Forbus

To safely interact with humans, AI systems must both have knowledge of our norms and consider norms in their planning processes. However, norm-guided planning has been less explored, only within communities of artificial agents and ignoring the dynamic nature of norms. This paper presents an approach to guiding planning with dynamically changing norms in a human-AI setting. This yields adaptive guard rails for the actions of AI systems.

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IJCAI Conference 2024 Conference Paper

Normative Testimony and Belief Functions: A Formal Theory of Norm Learning

Taylor Olson
Kenneth D. Forbus

The ability to learn another’s moral beliefs is necessary for all social agents. It allows us to predict their behavior and is a prerequisite to correcting their beliefs if they are incorrect. To make AI systems more socially competent, a formal theory for learning internal normative beliefs is thus needed. However, to the best of our knowledge, a philosophically justified formal theory for this process does not yet exist. This paper begins the development of such a theory, focusing on learning from testimony. We make four main contributions. First, we provide a set of axioms that any such theory must satisfy. Second, we provide justification for belief functions, as opposed to traditional probability theory, for modeling norm learning. Third, we construct a novel learning function that satisfies these axioms. Fourth, we provide a complexity analysis of this formalism and proof that deontic rules are sound under its semantics. This paper thus serves as a theoretical contribution towards modeling learning norms from testimony, paving the road towards more social AI systems.

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AAAI Conference 2023 Conference Paper

Mitigating Adversarial Norm Training with Moral Axioms

Taylor Olson
Kenneth D. Forbus

This paper addresses the issue of adversarial attacks on ethical AI systems. We investigate using moral axioms and rules of deontic logic in a norm learning framework to mitigate adversarial norm training. This model of moral intuition and construction provides AI systems with moral guard rails yet still allows for learning conventions. We evaluate our approach by drawing inspiration from a study commonly used in moral development research. This questionnaire aims to test an agent's ability to reason to moral conclusions despite opposed testimony. Our findings suggest that our model can still correctly evaluate moral situations and learn conventions in an adversarial training environment. We conclude that adding axiomatic moral prohibitions and deontic inference rules to a norm learning model makes it less vulnerable to adversarial attacks.

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