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Computing That Serves

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

Iris Seaman
MS Thesis Proposal

Friday, February 9, 10:00 AM
3314 TMCB
Advisor: David Wingate
 

 

As autonomous agents (such as unmanned aerial vehicles (UAVs), or self-driving cars) 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 proposal, we describe increasingly complex theory of mind models of a UAV pursuing an intruder, and discuss how (1) there is a natural Bayesian formulation to reasoning about the uncertainty inherent in our estimate of another agent's mental state, and why (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. 
 




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