Abstract
Possibility to solve the problem of planning and plan recovery for robots using probabilistic programming with optimization queries, which is being developed as a framework for AGI and cognitive architectures, is considered. Planning can be done directly by introducing a generative model for plans and optimizing an objective function calculated via plan simulation. Plan recovery is achieved almost without modifying optimization queries. These queries are simply executed in parallel with plan execution by a robot meaning that they continuously optimize dynamically varying objective functions tracking their optima. Experiments with the NAO robot showed that replanning can be naturally done within this approach without developing special plan recovery methods.
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Acknowledgements
This work was supported by Ministry of Education and Science of the Russian Federation, and by Government of Russian Federation, Grant 074-U01.
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Potapov, A., Rodionov, S., Potapova, V. (2016). Real-Time GA-Based Probabilistic Programming in Application to Robot Control. In: Steunebrink, B., Wang, P., Goertzel, B. (eds) Artificial General Intelligence. AGI 2016. Lecture Notes in Computer Science(), vol 9782. Springer, Cham. https://doi.org/10.1007/978-3-319-41649-6_10
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DOI: https://doi.org/10.1007/978-3-319-41649-6_10
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