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AGENT: A Benchmark for Core Psychological Reasoning
Tianmin Shu · Abhishek Bhandwaldar · Chuang Gan · Kevin Smith · Shari Liu · Dan Gutfreund · Elizabeth Spelke · Josh Tenenbaum · Tomer Ullman

Wed Jul 21 07:45 AM -- 07:50 AM (PDT) @ None

For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life. Intuitive psychology, the ability to reason about hidden mental variables that drive observable actions, comes naturally to people: even pre-verbal infants can tell agents from objects, expecting agents to act efficiently to achieve goals given constraints. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. Inspired by cognitive development studies on intuitive psychology, we present a benchmark consisting of a large dataset of procedurally generated 3D animations, AGENT (Action, Goal, Efficiency, coNstraint, uTility), structured around four scenarios (goal preferences, action efficiency, unobserved constraints, and cost-reward trade-offs) that probe key concepts of core intuitive psychology. We validate AGENT with human-ratings, propose an evaluation protocol emphasizing generalization, and compare two strong baselines built on Bayesian inverse planning and a Theory of Mind neural network. Our results suggest that to pass the designed tests of core intuitive psychology at human levels, a model must acquire or have built-in representations of how agents plan, combining utility computations and core knowledge of objects and physics.

Author Information

Tianmin Shu (MIT)
Abhishek Bhandwaldar (MIT-IBM Watson AI Lab)
Chuang Gan (MIT-IBM Watson AI Lab)
Kevin Smith (MIT)
Shari Liu (MIT)
Dan Gutfreund (IBM Research)
Elizabeth Spelke (Harvard University)
Josh Tenenbaum (MIT)

Joshua Brett Tenenbaum is Professor of Cognitive Science and Computation at the Massachusetts Institute of Technology. He is known for contributions to mathematical psychology and Bayesian cognitive science. He previously taught at Stanford University, where he was the Wasow Visiting Fellow from October 2010 to January 2011. Tenenbaum received his undergraduate degree in physics from Yale University in 1993, and his Ph.D. from MIT in 1999. His work primarily focuses on analyzing probabilistic inference as the engine of human cognition and as a means to develop machine learning.

Tomer Ullman (Harvard)

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