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Plan, Activity, and Intent Recognition by Robert P. Goldman, David Pynadath, Hung Hai Bui, Christopher Geib, Gita Sukthankar

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Chapter 7

Modeling Human Plan Recognition Using Bayesian Theory of Mind

Chris L. Baker and Joshua B. Tenenbaum,    Massachusetts Institute of Technology, Cambridge, MA, USA

Abstract

The human brain is the most powerful plan-recognition system we know. Central to the brain’s remarkable plan-recognition capacity is a theory of mind (ToM): our intuitive conception of other agents’ mental states—chiefly, beliefs and desires—and how they cause behavior. We present a Bayesian framework for ToM-based plan recognition, expressing generative models of belief- and desire-dependent planning in terms of partially observable Markov decision processes (POMDPs), and reconstructing an agent’s joint belief state and reward function using Bayesian inference, ...

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