Using Activity Recognition for Building Planning Action Models

Automated Planning has been successfully used in many domains like robotics or transportation logistics. However, building an action model is a difficult and time-consuming task even for domain experts. This paper presents a system, ASRA-AMLA, for automatically generating planning action models from...

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Bibliographic Details
Published in:International journal of distributed sensor networks 2013-06, Vol.2013 (-), p.1-10
Main Authors: Ortiz, Javier, García-Olaya, Angel, Borrajo, Daniel
Format: Article
Language:English
Subjects:
Automation
Buildings
Construction
Sensors
Tasks
Time series
Transportation
Transportation models
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Summary:Automated Planning has been successfully used in many domains like robotics or transportation logistics. However, building an action model is a difficult and time-consuming task even for domain experts. This paper presents a system, ASRA-AMLA, for automatically generating planning action models from sensor readings. Activity recognition is used to extract the actions that a user performs and the states produced by those actions. Then, the sequences of actions and states are used to infer a planning action model. With this approach, the system can automatically build an action model related to human-centered activities. It allows us to automatically build an assistance system for guiding humans to complete a task using Automated Planning. To test our approach, a new dataset from a kitchen domain has been generated. The tests performed show that our system is capable of extracting actions and states correctly from sensor time series and creating a planning domain used to guide a human to complete a task correctly.
ISSN:1550-1329
1550-1477
1550-1477
DOI:10.1155/2013/942347