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Automatically Generated Explanations for Markov Decision Processes

Automatically Generated Explanations for Markov Decision Processes

Omar Zia Khan, Pascal Poupart, James P. Black
Copyright: © 2012 |Pages: 20
ISBN13: 9781609601652|ISBN10: 1609601653|EISBN13: 9781609601676
DOI: 10.4018/978-1-60960-165-2.ch007
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MLA

Khan, Omar Zia, et al. "Automatically Generated Explanations for Markov Decision Processes." Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions, edited by L. Enrique Sucar, et al., IGI Global, 2012, pp. 144-163. https://doi.org/10.4018/978-1-60960-165-2.ch007

APA

Khan, O. Z., Poupart, P., & Black, J. P. (2012). Automatically Generated Explanations for Markov Decision Processes. In L. Sucar, E. Morales, & J. Hoey (Eds.), Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions (pp. 144-163). IGI Global. https://doi.org/10.4018/978-1-60960-165-2.ch007

Chicago

Khan, Omar Zia, Pascal Poupart, and James P. Black. "Automatically Generated Explanations for Markov Decision Processes." In Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions, edited by L. Enrique Sucar, Eduardo F. Morales, and Jesse Hoey, 144-163. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-165-2.ch007

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Abstract

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 MDPs 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. These explanations can be generated automatically at run-time with no additional effort required from the MDP designer. We demonstrate this 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 these automatically generated explanations for course-advising through a user study involving students.

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