Yang, F, Khandelwal, P, Leonetti, M orcid.org/0000-0002-3831-2400 et al. (1 more author) (2014) Planning in answer set programming while learning action costs for mobile robots. In: Knowledge Representation and Reasoning in Robotics. AAAI 2014 Spring Symposia, 24-26 Mar 2014, Palo Alto, California USA. AAAI , pp. 71-78. ISBN 9781577356455
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014, Association for the Advancement of Artificial Intelligence. This is an author produced version of a paper published in Knowledge Representation and Reasoning in Robotics. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Aug 2016 12:02 |
Last Modified: | 11 Feb 2018 06:28 |
Published Version: | http://www.aaai.org/ocs/index.php/SSS/SSS14/paper/... |
Status: | Published |
Publisher: | AAAI |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:101400 |