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  • Exploiting Probabilistic Knowledge under Uncertain Sensing for Efficient Robot Behaviour

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    HanheidePUB423.pdf (933.2Kb)
    Author(s)
    Hanheide, M.
    Gretton, C.
    Dearden, R.
    Hawes, N.
    Wyatt, J.
    Pronobis, A.
    Aydemir, A.
    G¨obelbecker, M.
    Zender, H.
    Griffith University Author(s)
    Gretton, Charles
    Year published
    2011
    Metadata
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    Abstract
    Robots must perform tasks efficiently and reliably while acting under uncertainty. One way to achieve efficiency is to give the robot commonsense knowledge about the structure of the world. Reliable robot behaviour can be achieved by modelling the uncertainty in the world probabilistically. We present a robot system that combines these two approaches and demonstrate the improvements in efficiency and reliability that result. Our first contribution is a probabilistic relational model integrating common-sense knowledge about the world in general, with observations of a particular environment. Our second contribution ...
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    Robots must perform tasks efficiently and reliably while acting under uncertainty. One way to achieve efficiency is to give the robot commonsense knowledge about the structure of the world. Reliable robot behaviour can be achieved by modelling the uncertainty in the world probabilistically. We present a robot system that combines these two approaches and demonstrate the improvements in efficiency and reliability that result. Our first contribution is a probabilistic relational model integrating common-sense knowledge about the world in general, with observations of a particular environment. Our second contribution is a continual planning system which is able to plan in the large problems posed by that model, by automatically switching between decision-theoretic and classical procedures. We evaluate our system on object search tasks in two different real-world indoor environments. By reasoning about the trade-offs between possible courses of action with different informational effects, and exploiting the cues and general structures of those environments, our robot is able to consistently demonstrate efficient and reliable goal-directed behaviour.
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    Conference Title
    Twenty-Second International Joint Conference on Artificial Intelligence Proceedings
    Publisher URI
    http://ijcai.org/papers11/contents.php
    DOI
    https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-407
    Copyright Statement
    © 2011 International Joint Conference on Artificial Intelligence. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the Conference's website for access to the definitive, published version.
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/46509
    Collection
    • Conference outputs

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