Initial state prediction in planning

Krivic, Senka and Cashmore, Michael and Ridder, Bram and Piater, Justus (2017) Initial state prediction in planning. In: The AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning. AAAI Press, Palo Alto, US-CA..

[img]
Preview
Text (Krivic-etal-AAAI-2017-Initial-state-prediction-planning)
Krivic_etal_AAAI_2017_Initial_state_prediction_planning.pdf
Final Published Version
License: All rights reserved

Download (1MB)| Preview

    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 multigraph utilizing Maximum-Margin Multi-Valued Regression. We evaluate this approach in four different domains, demonstrating high recall and accuracy.