Efficient planning under uncertainty with incremental refinement
Juan Carlos Saborio, Joachim Hertzberg
In Proc., (UAI-2019), 22.7.-25.7.2019, Tel Aviv, AAAI Press, 2019.

Zusammenfassung (Abstract) :

Online planning under uncertainty on robots and similar agents has very strict performance requirements in order to achieve reasonable behavior in complex domains with limited re- sources. The underlying process of decision- making and information gathering is correctly modeled by POMDP’s, but their complexity makes many interesting and challenging prob- lems virtually intractable. We address this is- sue by introducing a method to estimate rel- evance values for elements of a planning do- main, that allow an agent to focus on promis- ing features. This approach reduces the ef- fective dimensionality of problems, allowing an agent to plan faster and collect higher re- wards. Experimental validation was performed on two challenging POMDP’s that resemble real-world robotic task planning, where it is crucial to interleave planning and acting in an efficient manner.

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