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 f...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/942347 |
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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. |
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| ISSN: | 1550-1477 |