@inproceedings{cc3018d011f746809c4bba0653b10240,
title = "Inferring user intent with Bayesian inverse planning: Making sense of multi-UAS mission management",
abstract = "The goal of intent inference is to use observed behavior to predict underlying mental states and causal processes that are likely to have generated the behavior. A potentially powerful technique for inferring intent uses Bayesian inference in structured generative models for planning. We describe our adaptation of the Bayesian inverse planning framework to the multi-unmanned systems mission planning domain. We describe three experiments that elucidate the space of planning priorities in the domain, infer users' goals and priorities given their actions with a planning user interface, and predict users' next planning actions using inferences about their goals and priorities.",
keywords = "Bayesian inference, Intent, Inverse reinforcement learning, Markov decision processes, Unmanned aerial systems",
author = "Brian Riordan and Sylvain Brimi and Nathan Schurr and Jared Freeman and Gabriel Ganberg and Cooke, {Nancy J.} and Noel Rima",
year = "2011",
language = "English (US)",
isbn = "9781618390745",
series = "20th Annual Conference on Behavior Representation in Modeling and Simulation 2011, BRiMS 2011",
pages = "49--56",
booktitle = "20th Annual Conference on Behavior Representation in Modeling and Simulation 2011, BRiMS 2011",
note = "20th Annual Conference on Behavior Representation in Modeling and Simulation 2011, BRiMS 2011 ; Conference date: 21-03-2011 Through 24-03-2011",
}