First-Order Open-Universe POMDPs
S. Srivastava, S. Russell, P. Ruan

Citation
S. Srivastava, S. Russell, P. Ruan. "First-Order Open-Universe POMDPs". Conference on Uncertainty in Artificial Intelligence (UAI), 2014.

Abstract
Interest in relational and first-order languages for probability models has grown rapidly in recent years, and with it the possibility of extending such languages to handle decision processes---both fully and partially observable. We examine the problem of extending a first-order, open-universe language to describe POMDPs and identify non-trivial representational issues in describing an agent's capability for observation and action---issues that were avoided in previous work only by making strong and restrictive assumptions. We present a method for representing actions and observations that respects formal specifications of the sensors and actuators available to an agent, and show how to handle cases---such as seeing an object and picking it up---that could not previously be represented. Finally, we argue that in many cases open-universe POMDPs require belief-state policies rather than automata policies. We present an algorithm and experimental results for evaluating such policies for open-universe POMDPs.

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  • HTML
    S. Srivastava, S. Russell, P. Ruan. <a
    href="http://robotics.eecs.berkeley.edu/pubs/23.html"
    >First-Order Open-Universe POMDPs</a>, Conference
    on Uncertainty in Artificial Intelligence (UAI), 2014.
  • Plain text
    S. Srivastava, S. Russell, P. Ruan. "First-Order
    Open-Universe POMDPs". Conference on Uncertainty in
    Artificial Intelligence (UAI), 2014.
  • BibTeX
    @inproceedings{SrivastavaRussellRuan14_FirstOrderOpenUniversePOMDPs,
        author = {S. Srivastava and S. Russell and P. Ruan},
        title = {First-Order Open-Universe POMDPs},
        booktitle = {Conference on Uncertainty in Artificial
                  Intelligence (UAI)},
        year = {2014},
        abstract = {Interest in relational and first-order languages
                  for probability models has grown rapidly in recent
                  years, and with it the possibility of extending
                  such languages to handle decision processes---both
                  fully and partially observable. We examine the
                  problem of extending a first-order, open-universe
                  language to describe POMDPs and identify
                  non-trivial representational issues in describing
                  an agent's capability for observation and
                  action---issues that were avoided in previous work
                  only by making strong and restrictive assumptions.
                  We present a method for representing actions and
                  observations that respects formal specifications
                  of the sensors and actuators available to an
                  agent, and show how to handle cases---such as
                  seeing an object and picking it up---that could
                  not previously be represented. Finally, we argue
                  that in many cases open-universe POMDPs require
                  belief-state policies rather than automata
                  policies. We present an algorithm and experimental
                  results for evaluating such policies for
                  open-universe POMDPs.},
        URL = {http://robotics.eecs.berkeley.edu/pubs/23.html}
    }
    

Posted by Ehsan Elhamifar on 9 Jun 2014.
Groups: ehumans
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