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Using Activity Recognition for Building Planning Action Models

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  • Javier Ortiz
  • Angel García-Olaya
  • Daniel Borrajo

Abstract

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.

Suggested Citation

  • Javier Ortiz & Angel García-Olaya & Daniel Borrajo, 2013. "Using Activity Recognition for Building Planning Action Models," International Journal of Distributed Sensor Networks, , vol. 9(6), pages 942347-9423, June.
    • Handle: RePEc:sae:intdis:v:9:y:2013:i:6:p:942347
    DOI: 10.1155/2013/942347
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