We aim to reduce the burden of programming and deploying autonomous systems
to work in concert with people in time-critical domains, such as military field
operations and disaster response. Deployment plans for these operations are
frequently negotiated on-the-fly by teams of human planners. A human operator
then translates the agreed upon plan into machine instructions for the robots.
We present an algorithm that reduces this translation burden by inferring the
final plan from a processed form of the human team's planning conversation. Our
approach combines probabilistic generative modeling with logical plan
validation used to compute a highly structured prior over possible plans. This
hybrid approach enables us to overcome the challenge of performing inference
over the large solution space with only a small amount of noisy data from the
team planning session. We validate the algorithm through human subject
experimentation and show we are able to infer a human team's final plan with
83% accuracy on average. We also describe a robot demonstration in which two
people plan and execute a first-response collaborative task with a PR2 robot.
To the best of our knowledge, this is the first work that integrates a logical
planning technique within a generative model to perform plan inference.Comment: Appears in Proceedings of the Twenty-Seventh AAAI Conference on
Artificial Intelligence (AAAI-13