Learning and Simulating Human Behaviour with Relational Decision Trees

The recognition of activities performed by humans is crucial in human-robot interaction. However, relying solely on the assumption of rational human behaviour may be flawed given the large influence of individual preferences on decision-making. This paper proposes a novel approach to understanding human behaviour by examining how individuals select actions to tackle various tasks. Central to this approach is the concept of a Relational Decision Tree, which encapsulates human behaviour patterns. Then, when a new planning problem is posed, this relational decision tree is used to generate a plan that follows the learned human behaviour. This methodology enables proactive anticipation of individual needs and tailored responses. Validation across five diverse domains and several behaviours demonstrates the efficacy of this method.