Chris Baker

AAAI Conference 2016 Conference Paper

Modeling Human Ad Hoc Coordination

• Peter Krafft
• Chris Baker
• Alex Pentland
• Joshua Tenenbaum

AAAI Conference 2016 Conference Paper

Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model

• Ryo Nakahashi
• Chris Baker
• Joshua Tenenbaum

Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of their actions is typically unobservable, and must be inferred in order to learn new skills by demonstration, or to assist others in completing their tasks. For example, an assistant who has learned the subgoal structure of a colleague’s task can more rapidly recognize and support their actions as they unfold. Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences. We test this model with a behavioral experiment in which humans observed different series of goal-directed actions, and inferred both the number and composition of the subgoal sequences associated with each goal. The Bayesian model predicts human subgoal inferences with high accuracy, and significantly better than several alternative models and straightforward heuristics. Motivated by this result, we simulate how learning and inference of subgoals can improve performance in an artificial user assistance task. The Bayesian model learns the correct subgoals from fewer observations, and better assists users by more rapidly and accurately inferring the goal of their actions than alternative approaches.

YNIMG Journal 2015 Journal Article

Influence of lexical status and orthographic similarity on the multi-voxel response of the visual word form area

• Annelies Baeck
• Dwight Kravitz
• Chris Baker
• Hans P. Op de Beeck

NeurIPS Conference 2009 Conference Paper

Help or Hinder: Bayesian Models of Social Goal Inference

• Tomer Ullman
• Chris Baker
• Owen Macindoe
• Owain Evans
• Noah Goodman
• Joshua Tenenbaum

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 anothers 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 agents 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.

NeurIPS Conference 2005 Conference Paper

Bayesian models of human action understanding

• Chris Baker
• Rebecca Saxe
• Joshua Tenenbaum

We present a Bayesian framework for explaining how people reason about and predict the actions of an intentional agent, based on observ- ing its behavior. Action-understanding is cast as a problem of inverting a probabilistic generative model, which assumes that agents tend to act rationally in order to achieve their goals given the constraints of their en- vironment. Working in a simple sprite-world domain, we show how this model can be used to infer the goal of an agent and predict how the agent will act in novel situations or when environmental constraints change. The model provides a qualitative account of several kinds of inferences that preverbal infants have been shown to perform, and also fits quantita- tive predictions that adult observers make in a new experiment.