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 mechanisms for 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.
|Title of host publication||AAAI-22 Technical Tracks 9|
|Publisher||Association for the Advancement of Artificial Intelligence|
|Number of pages||8|
|ISBN (Electronic)||1577358767, 9781577358763|
|State||Published - 30 Jun 2022|
|Event||36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online|
Duration: 22 Feb 2022 → 1 Mar 2022
|Name||Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022|
|Conference||36th AAAI Conference on Artificial Intelligence, AAAI 2022|
|Period||22/02/22 → 1/03/22|
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