Learning and Recognizing Human Behaviour with Relational Decision Trees
Stanislav Sitanskiy, Laura Sebastiá, Eva Onaindía · Procedia Computer Science · 2024
The recognition of activities performed by humans is crucial in human-robot interaction. However, assuming humans always follow rational behaviour in executing activities may not be accurate since individual preferences influence their decision-making. This paper proposes a method for learning human behaviour that involves capturing how humans select actions to solve problems. This behaviour is represented by a Relational Decision Tree. We define two sets of features that can be automatically extracted from the planning domain. A behaviour library is created and used to identify the behaviour followed by a person when executing a plan in a new situation. This approach allows to anticipate the person’s needs and act accordingly. The method was tested in three different domains, showing its validity.