TY - GEN
T1 - Expectation-aware planning
T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020
AU - Sreedharan, Sarath
AU - Chakraborti, Tathagata
AU - Muise, Christian
AU - Kambhampati, Subbarao
N1 - Funding Information:
We thank Dan Weld for helpful comments on a previous draft. This research is supported in part by ONR grants N00014-16-1-2892, N00014-18-1-2442, and N00014-18-1-2840, AFOSR grant FA9550-18-1-0067, NASA grant NNX17AD06G and a JP Morgan AI Faculty Research grant.
Publisher Copyright:
Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2020
Y1 - 2020
N2 - In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human’s expectations about an agent may differ from the agent’s own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences like explanations (Chakraborti et al. 2017) and explicability (Kulkarni et al. 2019), but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to planning with diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over our earlier approaches that rely on search in the space of models.
AB - In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human’s expectations about an agent may differ from the agent’s own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences like explanations (Chakraborti et al. 2017) and explicability (Kulkarni et al. 2019), but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to planning with diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over our earlier approaches that rely on search in the space of models.
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M3 - Conference contribution
AN - SCOPUS:85093467725
T3 - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
SP - 2518
EP - 2526
BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
PB - AAAI press
Y2 - 7 February 2020 through 12 February 2020
ER -