scholarly journals A Planning System Based on Markov Decision Processes to Guide People With Dementia Through Activities of Daily Living

2006 ◽  
Vol 10 (2) ◽  
pp. 323-333 ◽  
Author(s):  
J. Boger ◽  
J. Hoey ◽  
P. Poupart ◽  
C. Boutilier ◽  
G. Fernie ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Markov Decision Processes ◽  
Daily Living ◽  
Decision Processes ◽  
Planning System ◽  
People With Dementia ◽  
Markov Decision

Automatically Generated Explanations for Markov Decision Processes

2012 ◽  
pp. 144-163 ◽  
Author(s):  
Omar Zia Khan ◽  
Pascal Poupart ◽  
James P. Black
Keyword(s):  
Undergraduate Students ◽  
Markov Decision Processes ◽  
User Study ◽  
Decision Processes ◽  
Course Selection ◽  
Minimal Set ◽  
People With Dementia ◽  
Markov Decision ◽  
Domain Independent ◽  
Sequential Nature

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.

Extreme-point solutions in Markov decision processes

1983 ◽  
Vol 20 (04) ◽  
pp. 835-842
Author(s):  
David Assaf
Keyword(s):  
Convex Function ◽  
Extreme Point ◽  
Markov Decision Processes ◽  
Convex Functions ◽  
Sufficient Conditions ◽  
Decision Processes ◽  
Markov Decision ◽  
Full Solution

The paper presents sufficient conditions for certain functions to be convex. Functions of this type often appear in Markov decision processes, where their maximum is the solution of the problem. Since a convex function takes its maximum at an extreme point, the conditions may greatly simplify a problem. In some cases a full solution may be obtained after the reduction is made. Some illustrative examples are discussed.

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