Modeling Social Common Sense for Seamless Human Machine Teaming: Inverting the Intuitive Game Engine with Probabilistic Programming

Number of Papers published in peer-reviewed journals: Number of Papers published in non peer-reviewed journals: Final Report: Modeling Social Common Sense for Seamless Human-Machine Teaming: Inverting the "Intuitive Game Engine" with Probabilistic Programming" Report Title Humans use commonsense knowledge of agents and the physical world to solve problems interactively. We model people’s knowledge of agents and the world using rich “game-engine”-based models of 3D physical scenes with realistic physical dynamics and agents autonomously acting and interacting with others, based on individual mental states and shared tasks. Cast as a probabilistic program, this intuitive game engine can be inverted to support inferences about other’s beliefs, desires, goals, and tasks, which are vital for successful social interaction. We show the promise of this approach in application to modeling human inferences of the targets of the observed reaching actions of others. (a) Papers published in peer-reviewed journals (N/A for none) Enter List of papers submitted or published that acknowledge ARO support from the start of the project to the date of this printing. List the papers, including journal references, in the following categories: (b) Papers published in non-peer-reviewed journals (N/A for none) (c) Presentations 07/05/2017 Received Paper