Decreasing uncertainty in planning with state prediction

Senka Krivic, Michael Cashmore, Daniele Magazzeni, Bram Ridder, Sandor Szedmak, Justus Piater

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

3 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence IJCAI-17
EditorsCarles Sierra
Place of Publication[US-CA.]
Pages2032-2038
Number of pages7
DOIs
Publication statusPublished - 25 Aug 2017
EventTwenty-Sixth International Joint Conference on Artificial Intelligence IJCAI-17 - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017

Conference

ConferenceTwenty-Sixth International Joint Conference on Artificial Intelligence IJCAI-17
Abbreviated titleIJCAI-17
CountryAustralia
CityMelbourne
Period19/08/1725/08/17

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Keywords

  • state prediction
  • uncertainty
  • machine learning
  • semi-supervised learning
  • planning and scheduling
  • planning under uncertainty
  • robot planning

Cite this

Krivic, S., Cashmore, M., Magazzeni, D., Ridder, B., Szedmak, S., & Piater, J. (2017). Decreasing uncertainty in planning with state prediction. In C. Sierra (Ed.), Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence IJCAI-17 (pp. 2032-2038). [US-CA.]. https://doi.org/10.24963/ijcai.2017/282