Using Activity Recognition for Building Planning Action Models

الملخص EN

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.