Using Machine Learning for Decreasing State Uncertainty in Planning

DOI10.1613/jair.1.11567zbMath1496.68303OpenAlexW2979901546MaRDI QIDQ5139593

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

Publication date: 9 December 2020

Published in: Journal of Artificial Intelligence Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1613/jair.1.11567

zbMATH Keywords

uncertaintyplanningrobotics

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Artificial intelligence for robotics (68T40)

Related Items (1)

StatePredictions

Uses Software

• PDDL
• ROS
• GitHub

Cites Work

• Planning and acting in partially observable stochastic domains
• Learning action models from plan examples using weighted MAX-SAT
• 10.1162/153244302760185243
• Scaling up Heuristic Planning with Relational Decision Trees
• Compiling Uncertainty Away in Conformant Planning Problems with Bounded Width
• Replanning in Domains with Partial Information and Sensing Actions
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