Decreasing uncertainty in planning with state prediction

Krivic, Senka and Cashmore, Michael and Magazzeni, Daniele and Ridder, Bram and Szedmak, Sandor and Piater, Justus; Sierra, Carles, ed. (2017) Decreasing uncertainty in planning with state prediction. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence IJCAI-17. IJCAI, AUS, pp. 2032-2038. ISBN 9780999241103

Preview

Text (Krivic-etal-IJCAI2017-Decreasing-uncertainty-planning-state-prediction)
Krivic_etal_IJCAI2017_Decreasing_uncertainty_planning_state_prediction.pdf
Accepted Author Manuscript
Download (473kB)| Preview

Official URL: https://doi.org/10.24963/ijcai.2017/282

Abstract

In real world environments the state is almost never completely known. Exploration is often expensive. The application of planning in these environments is consequently more difficult and less robust. In this paper we present an approach for predicting new information about a partially-known state. The state is translated into a partially-known multigraph, which can then be extended using machinelearning techniques. We demonstrate the effectiveness of our approach, showing that it enhances the scalability of our planners, and leads to less time spent on sensing actions.

Creators(s):	Krivic, Senka, Cashmore, Michael ORCID: https://orcid.org/0000-0002-8334-4348, Magazzeni, Daniele, Ridder, Bram, Szedmak, Sandor and Piater, Justus; Sierra, Carles
Item type:	Book Section
ID code:	69972
Keywords:	state prediction, uncertainty, machine learning, semi-supervised learning, planning and scheduling, planning under uncertainty, robot planning, Electronic computers. Computer science, Computer Science(all)
Subjects:	Science > Mathematics > Electronic computers. Computer science
Department:	Faculty of Science > Computer and Information Sciences
Depositing user:	Pure Administrator
Date deposited:	26 Sep 2019 14:21
Last modified:	28 Mar 2020 01:17
URI:	https://strathprints.strath.ac.uk/id/eprint/69972
Export data:

CORE (COnnecting REpositories)