Goal Recognition as Reinforcement Learning


  • Leonardo Amado Pontifical Catholic University of Rio Grande do Sul
  • Reuth Mirsky Bar Ilan University The University of Texas at Austin
  • Felipe Meneguzzi University of Aberdeen Pontifical Catholic University of Rio Grande do Sul




Planning, Routing, And Scheduling (PRS), Multiagent Systems (MAS), Machine Learning (ML)


Most approaches for goal recognition rely on specifications of the possible dynamics of the actor in the environment when pursuing a goal. These specifications suffer from two key issues. First, encoding these dynamics requires careful design by a domain expert, which is often not robust to noise at recognition time. Second, existing approaches often need costly real-time computations to reason about the likelihood of each potential goal. In this paper, we develop a framework that combines model-free reinforcement learning and goal recognition to alleviate the need for careful, manual domain design, and the need for costly online executions. This framework consists of two main stages: Offline learning of policies or utility functions for each potential goal, and online inference. We provide a first instance of this framework using tabular Q-learning for the learning stage, as well as three measures that can be used to perform the inference stage. The resulting instantiation achieves state-of-the-art performance against goal recognizers on standard evaluation domains and superior performance in noisy environments.




How to Cite

Amado, L., Mirsky, R., & Meneguzzi, F. (2022). Goal Recognition as Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 36(9), 9644-9651. https://doi.org/10.1609/aaai.v36i9.21198



AAAI Technical Track on Planning, Routing, and Scheduling