POMDP Based Action Planning and Human Error Detection - Artificial Intelligence Applications and Innovations (AIAI 2015) Access content directly
Conference Papers Year : 2015

POMDP Based Action Planning and Human Error Detection

Pia Rotshtein
  • Function : Author
  • PersonId : 991076
Martin Russell
  • Function : Author
  • PersonId : 991077

Abstract

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 an assistive 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 system’s components. In this paper, we first give an overview of the global assistive system where the AIPS is implemented, and explain 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.
Fichier principal
Vignette du fichier
978-3-319-23868-5_18_Chapter.pdf (4.13 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01385361 , version 1 (21-10-2016)

Licence

Attribution

Identifiers

Cite

Emilie D. Jean-Baptiste, Pia Rotshtein, Martin Russell. POMDP Based Action Planning and Human Error Detection. 11th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2015), Sep 2015, Bayonne, France. pp.250-265, ⟨10.1007/978-3-319-23868-5_18⟩. ⟨hal-01385361⟩
53 View
172 Download

Altmetric

Share

Gmail Facebook X LinkedIn More