POMDP based Action Planning and Human Error Detection

Research output: Contribution to conference (unpublished)Paper



This paper presents a Partially Observable Markov Decision Process
(POMDP) model for action planning and human errors detection, during activities
of daily living (ADLs). This model is integrated into a sub-component of a
rehabilitation system designed for stroke survivors; it is called the Artificial Intelligent
Planning System (AIPS). Its main goal is to monitor the user’s history of
actions during a specific task, and to provide meaningful assistance when an error
is detected in his/her sequence of actions. To do so, the AIPS must cope with the
ambiguity in the outputs of the other components. In this paper, we first give an
overview of the global rehabilitation system where the AIPS is integrated, and
show how it interacts with the user to guide him/her during tea-making. We then
define the POMDP models and the Monte Carlo Algorithm used to learn how to
retrieve optimal prompts, and detect human errors under uncertainty.


Original languageEnglish
Number of pages20
Publication statusAccepted/In press - Sep 2015
Event11th Int. Conf. on Artificial Intelligence Applications and Innovations (AIAI 2015) - Bayonne/Biarritz, France
Duration: 14 Sep 201517 Sep 2015


Conference11th Int. Conf. on Artificial Intelligence Applications and Innovations (AIAI 2015)