Bayesian models of human action understanding

TitleBayesian models of human action understanding
Publication TypeJournal Article
Year of Publication2006
AuthorsBaker, C. L., Tenenbaum J. B., & Saxe R. R.
JournalAdvances in Neural Information Processing Systems
Volume18
Pagination99 - 106
Date Published01/2006
Abstract

We present a Bayesian framework for explaining how people reason about and predict the actions of an intentional agent, based on observing its behavior. Action-understanding is cast as a problem of inverting a probabilistic generative model, which assumes that agents tend to act rationally in order to achieve their goals given the constraints of their environment. Working in a simple sprite-world domain, we show how this model can be used to infer the goal of an agent and predict how the agent will act in novel situations or when environmental constraints change. The model provides a qualitative account of several kinds of inferences that preverbal infants have been shown to perform, and also fits quantitative predictions that adult observers make in a new experiment.

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