The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

The state of a POMDP can often be factored into a tuple of n state variables. The corresponding flat model, with size exponential in n, may be intractably large. We present a novel method called conditionally irrelevant variable abstraction (CIVA) for losslessly compressing the factored model, which is then expanded into an exponentially smaller flat model in a representation compatible with many existing POMDP solvers. We applied CIVA to previously intractable problems from a robotic exploration domain. We were able to abstract, expand, and approximately solve POMDPs that had up to 10^24 states in the uncompressed flat representation.

Notes

Text Reference

Trey Smith, David R. Thompson, and David Wettergreen, "Generating Exponentially Smaller POMDP Models Using Conditionally Irrelevant Variable Abstraction," Proc. Int. Conf. on Applied Planning and Scheduling (ICAPS), , 2007

BibTeX Reference

@article{Smith_2007_5938,
   author = "Trey Smith and David R Thompson and David Wettergreen",
   title = "Generating Exponentially Smaller POMDP Models Using Conditionally Irrelevant Variable Abstraction",
   journal = "Proc. Int. Conf. on Applied Planning and Scheduling (ICAPS)",
   year = "2007",
}

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