Abstract
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.
Description
AGENT: A Benchmark for Core Psychological Reasoning
Links and resources
Tags
community