@article{Amiri_Shokrolah Shirazi_Zhang_2020, title={Learning and Reasoning for Robot Sequential Decision Making under Uncertainty}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/5659}, DOI={10.1609/aaai.v34i03.5659}, abstractNote={<p>Robots frequently face complex tasks that require more than one action, where sequential decision-making (<span style="font-variant: small-caps;">sdm</span>) capabilities become necessary. The key contribution of this work is a robot <span style="font-variant: small-caps;">sdm</span> framework, called <span style="font-variant: small-caps;">lcorpp</span>, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment.</p>}, number={03}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Amiri, Saeid and Shokrolah Shirazi, Mohammad and Zhang, Shiqi}, year={2020}, month={Apr.}, pages={2726-2733} }