Learning and Reasoning for Robot Sequential Decision Making under Uncertainty


  • Saeid Amiri SUNY Binghamton
  • Mohammad Shokrolah Shirazi University of Indianapolis
  • Shiqi Zhang SUNY Binghamton




Robots frequently face complex tasks that require more than one action, where sequential decision-making (sdm) capabilities become necessary. The key contribution of this work is a robot sdm framework, called lcorpp, 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.




How to Cite

Amiri, S., Shokrolah Shirazi, M., & Zhang, S. (2020). Learning and Reasoning for Robot Sequential Decision Making under Uncertainty. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2726-2733. https://doi.org/10.1609/aaai.v34i03.5659



AAAI Technical Track: Knowledge Representation and Reasoning