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arXiv · 2109.07436

Computing Policies That Account For The Effects Of Human Agent Uncertainty During Execution In Markov Decision Processes

Sriram Gopalakrishnan·Mudit Verma·Subbarao Kambhampati
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Abstract

When humans are given a policy to execute, there can be policy execution errors and deviations in policy if there is uncertainty in identifying a state. This can happen due to the human agent's cognitive limitations and/or perceptual errors. So an algorithm that computes a policy for a human to execute ought to consider these effects in its computations. An optimal Markov Decision Process (MDP) policy that is poorly executed (because of a human agent) maybe much worse than another policy that is suboptimal in the MDP, but considers the human-agent's execution behavior. In this paper we consider two problems that arise from state uncertainty; these are erroneous state-inference, and extra-sensing actions that a person might take as a result of their uncertainty. We present a framework to model the human agent's behavior with respect to state uncertainty, and can be used to compute MDP policies that accounts for these problems. This is followed by a hill climbing algorithm to search for good policies given our model of the human agent. We also present a branch and bound algorithm which can find the optimal policy for such problems. We show experimental results in a Gridworld domain, and warehouse-worker domain. Finally, we present human-subject studies that support our human model assumptions.

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Sriram Gopalakrishnan, Mudit Verma, Subbarao Kambhampati. 2021-09-15. Computing Policies That Account For The Effects Of Human Agent Uncertainty During Execution In Markov Decision Processes. https://arxiv.org/abs/2109.07436

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