Siddharth Srivastava, Xiang Cheng, Stuart J. Russell and Avi Pfeffer
EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2013-243
December 25, 2013
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-243.pdf
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-unverse POMDPs.
BibTeX citation:
@techreport{Srivastava:EECS-2013-243, Author = {Srivastava, Siddharth and Cheng, Xiang and Russell, Stuart J. and Pfeffer, Avi}, Title = {First-Order Open-Universe POMDPs: Formulation and Algorithms}, Institution = {EECS Department, University of California, Berkeley}, Year = {2013}, Month = {Dec}, URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-243.html}, Number = {UCB/EECS-2013-243}, 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-unverse POMDPs.} }
EndNote citation:
%0 Report %A Srivastava, Siddharth %A Cheng, Xiang %A Russell, Stuart J. %A Pfeffer, Avi %T First-Order Open-Universe POMDPs: Formulation and Algorithms %I EECS Department, University of California, Berkeley %D 2013 %8 December 25 %@ UCB/EECS-2013-243 %U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-243.html %F Srivastava:EECS-2013-243