Initial state prediction in planning
Krivic, Senka and Cashmore, Michael and Ridder, Bram and Magazzeni, Daniele and Szedmak, Sandor and Piater, Justus; (2017) Initial state prediction in planning. In: The AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning - Technical Report. AAAI Press, Menlo Park, US-CA.. ISBN 9781577357865
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Abstract
While recent advances in offline reasoning techniques and online execution strategies have made planning under uncertainty more robust, the application of plans in partially-known environments is still a difficult and important topic. In this paper we present an approach for predicting new information about a partially-known initial state, represented as a multi- graph utilizing Maximum-Margin Multi-Valued Regression. We evaluate this approach in four different domains, demonstrating high recall and accuracy.
ORCID iDs
Krivic, Senka, Cashmore, Michael ORCID: https://orcid.org/0000-0002-8334-4348, Ridder, Bram, Magazzeni, Daniele, Szedmak, Sandor and Piater, Justus;-
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Item type: Book Section ID code: 69975 Dates: DateEvent5 February 2017Published18 November 2016AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science Department: Faculty of Science > Computer and Information Sciences Depositing user: Pure Administrator Date deposited: 27 Sep 2019 08:21 Last modified: 20 Sep 2024 00:27 URI: https://strathprints.strath.ac.uk/id/eprint/69975