Cognitive Modelling of Intention Recognition in Cocktail Mixing

Recognising the intention of a human partner is a key challenge for collaborative systems in human-robot interaction. However, existing studies of intention recognition abilities in AI system mostly focus on data-driven approaches and the recognition of direct action intentions (low-level intentions). We propose an artificial intention recognition approach that is implemented as a cognitive model in the theory-based ACT-R architecture and that infers superordinate action goals (high-level goals). We tested our approach for the recognition of cocktails from mixing sequences performed by human participants in an experimental study. Intention recognition speed of the model was evaluated and compared to human intention recognition performance. Our results indicate that the implemented model successfully recognises high-level intentions and tends to be substantially faster than humans.