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 article in proceedingsScientificpeer-review


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 machine-learning 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
ISBN (Electronic)978-0-9992411-0-3
Publication statusPublished - Aug 2017
MoE publication typeA4 Conference publication
EventInternational Joint Conference on Artificial Intelligence - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017
Conference number: 26


ConferenceInternational Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI
Internet address


  • Machine Learning: Semi-Supervised Learning
  • Planning and Scheduling: Planning under Uncertainty
  • Planning and Scheduling: Robot Planning
  • Uncertainty in AI


Dive into the research topics of 'Decreasing Uncertainty in Planning with State Prediction'. Together they form a unique fingerprint.

Cite this