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.

Electronic downloads

• http://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-243.html

• 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
For additional information, see the Publications FAQ or contact webmaster at robotics eecs berkeley edu.