Continual Planning in Golog. Hofmann, T., Niemueller, T., Claßen, J., & Lakemeyer, G. In Schuurmans, D. & Wellman, M., editors, Thirtieth AAAI Conference on Artificial Intelligence (AAAI), pages 3346-3353, Phoenix, AZ, USA, 2016. AAAI Press, AAAI Press. Paper Demo abstract bibtex 1 download To solve ever more complex and longer tasks, mobile robots need to generate more elaborate plans and must handle dynamic environments and incomplete knowledge. We address this challenge by integrating two seemingly different approaches – PDDL-based planning for efficient plan generation and GOLOG for highly expressive behavior specification – in a coherent framework that supports continual planning. The latter allows to interleave plan generation and execution through assertions, which are placeholder actions that are dynamically expanded into conditional sub-plans (using classical planners) once a replanning condition is satisfied. We formalize and implement continual planning in GOLOG which was so far only supported in PDDL-based systems. This enables combining the execution of generated plans with regular GOLOG programs and execution monitoring. Experiments on autonomous mobile robots show that the approach supports expressive behavior specification combined with efficient sub-plan generation to handle dynamic environments and incomplete knowledge in a unified way.
@InProceedings {ContinualPlanningGolog,
author = {Till Hofmann and Tim Niemueller and Cla{\ss}en, Jens and Lakemeyer, Gerhard},
title = {{Continual Planning in Golog}},
booktitle = {Thirtieth AAAI Conference on Artificial Intelligence (AAAI)},
year = {2016},
pages = {3346-3353},
publisher = {AAAI Press},
organization = {AAAI Press},
address = {Phoenix, AZ, USA},
abstract = {To solve ever more complex and longer tasks, mobile
robots need to generate more elaborate plans and
must handle dynamic environments and incomplete
knowledge. We address this challenge by integrating
two seemingly different approaches {\textendash}
PDDL-based planning for efficient plan generation
and GOLOG for highly expressive behavior
specification {\textendash} in a coherent framework
that supports continual planning. The latter allows
to interleave plan generation and execution through
assertions, which are placeholder actions that are
dynamically expanded into conditional sub-plans
(using classical planners) once a replanning
condition is satisfied. We formalize and implement
continual planning in GOLOG which was so far only
supported in PDDL-based systems. This enables
combining the execution of generated plans with
regular GOLOG programs and execution
monitoring. Experiments on autonomous mobile robots
show that the approach supports expressive behavior
specification combined with efficient sub-plan
generation to handle dynamic environments and
incomplete knowledge in a unified way.},
url = {https://kbsg.rwth-aachen.de/~hofmann/papers/continual-planning-golog.pdf},
url_Demo = {https://youtu.be/qLIYJ2NiGq0},
attachments = {https://kbsg.rwth-aachen.de/~hofmann/papers/continual-planning-golog.pdf},
editor = {Dale Schuurmans and Michael Wellman}
}
Downloads: 1
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