Goal Recognition via Variational Causality

Recent advances in Goal Recognition have yielded a new class of approaches capable of solving goal recognition problems without relying on predefined domain theories, defined as Model-Free Goal Recognition. Most existing approaches rely on neural networks, probabilistic theories or approximated domain theories to recognize goals without relying on explicitly defined domain knowledge. However, these approaches often neglect the causal relationships contained in the data used for their process. This oversight overlooks an opportunity to make their goal recognition process more accurate, explainable and robust. We propose a novel Model-Free Goal Recognition approach that integrates causality through Variational Inference, which, to the best of our knowledge, is an entirely novel class of techniques for goal recognition. The method encompasses three key stages: Causal Discovery, Counterfactual Inference, and decision-making grounded in Trajectory Likelihood. Our approach outperforms the existing state-of-the-art methods in all tested domains. Moreover, its strong noise resilience ensures that its performance in noisy environments is nearly indistinguishable from standard conditions, fully showcasing its robustness.