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AIPS
2009

Minimal Sufficient Explanations for Factored Markov Decision Processes

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Minimal Sufficient Explanations for Factored Markov Decision Processes
Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies for factored MDP by populating a set of domain-independent templates. We also present a mechanism to determine a minimal set of templates that, viewed together, completely justify the policy. Our explanations can be generated automatically at run-time with no additional effort required from the MDP designer. We demonstrate our technique using the problems of advising undergraduate students in their course selection and assisting people with dementia in completing the task of handwashing. We also evaluate our explanations for courseadvising through a user study involving students.
Omar Zia Khan, Pascal Poupart, James P. Black
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2009
Where AIPS
Authors Omar Zia Khan, Pascal Poupart, James P. Black
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