Document Type
Conference Proceeding
Publication Date
2022
Abstract
Theory of Mind is often characterized as the ability to recognize desires, beliefs, and intentions of others. In this position paper, I look at the literature on modeling Theory of Mind in machines and find that, to date, intention is not usually a focus. I define what I mean by intention—choice with commitment—following prior work. Intention has a long history of research in some communities, and I offer one theoretical framework for modeling intention as a starting point. I take inspiration from how children learn intention through joint attention with others and how that leads to Theory of Mind. I argue that though models of machine Theory of Mind need not follow the same learning progression as children, intention is an aspect of Theory of Mind that should be more explicit.
Copyright Statement
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. https://doi.org/10.1109/RO-MAN53752.2022.9900783
Publication Information
Kennington, Casey. (2022). "Understanding Intention for Machine Theory of Mind: A Position Paper". 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 450-453. https://doi.org/10.1109/RO-MAN53752.2022.9900783