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Creators/Authors contains: "Amiri, Saeid"

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  1. ; ; ; ; (, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence)
  2. ; ; ; ; (, UAI '23: Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence)
  3. ; ; ; ; ; (, Autonomous Robots)
    Full Text Available 
  4. ; ; (, IEEE Robotics and Automation Letters)
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  5. ; ; ; ; (, Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling)
    null (Ed.)
    Accepted Manuscript Full Text Available
  6. ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    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. 
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