As the tasks of autonomous manipulation robots get more complex, the tasking of the robots using natural-language instructions becomes more important. Executing such instructions in the way they are intended often requires robots to infer missing, and disambiguate given information using lots of common and commonsense knowledge. During my research work, I proposed the concept of Probabilistic Action Cores (PRAC) – an activity-centric probabilistic knowledge base for interpretation, disambiguation and completion of underspecified and vaguely stated instructions in natural language.
This package consists of an implementation of probabilistic knowledge services for natural-language instruction interpretation as a Python module (prac) that you can use to work with these services in your own Python scripts. For an introduction into using PRAC in your own scripts, see API-Specification.
- Release 1.0.0 (19.12.2017)
- Initial Release
Daniel Nyga ()
This work is supported in part by the EU FP7 projects RoboHow (grant number 288533), ACAT (grant number 600578) and CoTeSys cluster of excellence (Cognition for Technical Systems), part of the Excellence Initiative of the German Research Foundation (DFG):