Uninformed-to-informed exploration in unstructured real-world environments

Allan Axelrod, Girish Chowdhary

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


Conventionally, the process of learning the model (exploration) is initialized as either an uninformed or informed policy, where the latter leverages observations to guide future exploration. Informed exploration is ideal as it may allow a model to be learned in fewer samples. However, informed exploration cannot be implemented from the onset when a-priori knowledge on the sensing domain statistics are not available; such policies would only sample the first set of locations, repeatedly. Hence, we present a theoretically-derived bound for transitioning from uninformed exploration to informed exploration for unstructured real-world environments which may be partially-observable and time-varying. This bound is used in tandem with a sparsified Bayesian nonparametric Poisson Exposure Process, which is used to learn to predict the value of information in partially- observable and time-varying domains. The result is an uninformed-to-informed exploration policy which outperforms baseline algorithms in real-world data-sets.

Original languageEnglish (US)
Title of host publicationSelf-Confidence in Autonomous Systems - Papers from the AAAI 2015 Fall Symposium, Technical Report
PublisherAI Access Foundation
Number of pages2
ISBN (Electronic)9781577357513
StatePublished - Jan 1 2015
Externally publishedYes
EventAAAI 2015 Fall Symposium - Arlington, United States
Duration: Nov 12 2015Nov 14 2015

Publication series

NameAAAI Fall Symposium - Technical Report


OtherAAAI 2015 Fall Symposium
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Engineering(all)


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