Presented at the 2nd International Workshop on Statistical Relational AI, Catalina Island, CA (August 2012).
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 solution based on ideas from modal logic, and show how to handle cases like being able to act upon an object that has been detected through one’s observations.
1 University of California, Berkeley
2 Charles River Analytics
For More Information
(Please include your name, address, organization, and the paper reference. Requests without this information will not be honored.)