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Planning in Answer Set Programming while Learning Action Costs for Mobile Robots.
Fangkai
Yang, Piyush Khandelwal, Matteo
Leonetti, and Peter Stone.
In AAAI Spring 2014 Symposium on Knowledge
Representation and Reasoning in Robotics (AAAI-SSS), March 2014.
[PDF]643.4kB [postscript]5.3MB
For mobile robots to perform complex missions, it may be necessary for them to plan with incomplete information and reason about the indirect effects of their actions. Answer Set Programming (ASP) provides an elegant way of formalizing domains which involve indirect effects of an action and recursively defined fluents. In this paper, we present an approach that uses ASP for robotic task planning, and demonstrate how ASP can be used to generate plans that acquire missing information necessary to achieve the goal. Action costs are also incorporated with planning to produce optimal plans, and we show how these costs can be estimated from experience making planning adaptive. We evaluate our approach using a realistic simulation of an indoor environment where a robot learns to complete its objective in the shortest time.
@InProceedings{AAAISSS14-yang,
author = {Fangkai Yang and Piyush Khandelwal and Matteo Leonetti and Peter Stone},
title = {Planning in Answer Set Programming while Learning Action Costs for
Mobile Robots},
booktitle = {AAAI Spring 2014 Symposium on Knowledge Representation and Reasoning in Robotics (AAAI-SSS)},
location = {Stanford, California, USA},
month = {March},
year = {2014},
abstract = {
For mobile robots to perform complex missions, it may be necessary for them
to plan with incomplete information and reason about the indirect effects
of their actions. Answer Set Programming (ASP) provides an elegant way of
formalizing domains which involve indirect effects of an action and
recursively defined fluents. In this paper, we present an approach that
uses ASP for robotic task planning, and demonstrate how ASP can be used
to generate plans that acquire missing information necessary to achieve
the goal. Action costs are also incorporated with planning to produce
optimal plans, and we show how these costs can be estimated from
experience making planning adaptive. We evaluate our approach using a
realistic simulation of an indoor environment where a robot learns to
complete its objective in the shortest time.
},
}
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