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

Abstract

Humans use commonsense knowledge of agents and the physical world to solve problems interactively. We model peoples 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 others 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.

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Document Details

Document Type
Technical Report
Publication Date
Jul 06, 2017
Accession Number
AD1059216

Entities

People

  • Chris Baker
  • Emanuel Todorov
  • Joshua B. Tenenbaum
  • Tao Gao
  • Vikash Kumar

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Neural Networks
  • Bayesian Networks
  • Cognitive Science
  • Computational Science
  • Computer Programming
  • Computer Science
  • Computer Vision
  • Computers
  • Control Systems
  • Department Of Defense
  • Engineering
  • Human Behavior
  • Kalman Filters
  • Neural Networks
  • Probability
  • Probability Distributions
  • Simulations
  • Training
  • Trajectories

Readers

  • Distributed Systems and Data Platform Development
  • Educational Psychology
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML