Freund, MichaelMichaelFreundSchraudner, DanielDanielSchraudnerSchmid, SebastianSebastianSchmidStade, ChristophChristophStadeWehr, ThomasThomasWehrHarth, AndreasAndreasHarth2024-09-302024-09-302024https://publica.fraunhofer.de/handle/publica/4758612-s2.0-85203605597We present the Simple Planning Annotation (spa) ontology for modeling preconditions and effects of interaction affordances within Web of Things Thing Descriptions and propose a mapping to the Planning Domain Definition Language to enable robot-device interaction. We use encoded semantic knowledge to generate a planning problem that can be used within existing AI planning algorithms to dynamically plan interaction sequences to achieve specified goals without the extensive pre-programming traditionally required, as demonstrated by the validation of our approach through a prototypical implementation. The scalability evaluation shows that when the number of input Web of Things actions increases by a factor of 1, 000, the runtime of the implemented prototype increases by about 14.3% and the memory consumption by about 9.4%, indicating vertical scalability.enActionable Knowledge RepresentationAI planningWeb of Thingsconference paper