Learning and Recognizing Human Behaviour with Relational Decision Trees

Stanislav Sitanskiy, Laura Sebastia, Eva Onaindia

Keywords: recognition, identification, behaviour, learning, features, relational decision tree, rdt

TL;DR: method to learn agent or human behaviour, based on observed plan, using extracted features adn RDT

Abstract: 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.