Abstract by Iris Seaman

Personal Infomation

Presenter's Name

Iris Seaman



Degree Level




Abstract Infomation


Computer Science

Faculty Advisor

David Wingate


Probabilistic Programming for Theory of Mind for Autonomous Decision Making in a Pursuing and Interception Problem


As autonomous agents (such as unmanned aerial vehicles, or UAVs)
become more ubiquitous, they are being used for increasingly complex
tasks.  Eventually, they will have to reason about the mental state of
other agents, including those agents' beliefs, desires and goals --
so-called theory of mind -- and make decisions based on that
reasoning.  In this presentation, we describe increasingly complex theory of
mind models of a UAV pursuing an intruder, and show that (1) there is a natural
Bayesian formulation to reasoning about the uncertainty inherent in
our estimate of another agent's mental state, and that (2)
probabilistic programming is a natural way to describe models that
involve one agent reasoning about another agent, where the target
agent uses complex primitives such as path planners and saliency maps
to make decisions. We propose using nested importance sampling on a theory of mind probabilistic program 
to infer adversarial agent plans and its own future plans to increase probability
of intruder detection. We demonstrate that more complex models lead to detection rates
and that nested modeling manifests rational 
agent behavior in these complex scenarios.