Help or Hinder: Bayesian Models of Social Goal Inference

Abstract

Everyday social interactions are heavily influenced by our snap judgments about others' goals. Even young infants can infer the goals of intentional agents from observing how they interact with objects and other agents in their environment e.g., that one agent is "helping" or "hindering" another's attempt to get up a hill or open a box. We propose a model for how people can infer these social goals from actions, based on inverse planning in multiagent Markov decision problems (MDPs). The model infers the goal most likely to be driving an agent's behavior by assuming the agent acts approximately rationally given environmental constraints and its model of other agents present. We also present behavioral evidence in support of this model over a simpler, perceptual cue-based alternative.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2009
Accession Number
ADA537574

Entities

People

  • Chris L. Baker
  • Joshua B. Tenenbaum
  • Noah D. Goodman
  • Owain Evans
  • Owen Macindoe
  • Tomer D. Ullman

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Bayesian Networks
  • Causal Reasoning
  • Cognition
  • Cognitive Science
  • Contrast
  • Environment
  • Information Processing
  • Information Systems
  • Judgment
  • Models
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Psychological Theory
  • Reasoning

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Systems Analysis and Design

Technology Areas

  • AI & ML