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.
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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