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Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision

(CS 528)

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Bayesian models of human learning and reasoning

Josh Tenenbaum, MIT

March 7, 2005, 4:15PM

TCSeq 200

`http://graphics.stanford.edu/ba-colloquium/`

#### Abstract

In the last decade, Bayesian methods have revolutionized major areas
of artificial intelligence, machine learning, and natural language
processing. In contrast, Bayesian methods have not yet achieved
nearly the same success among cognitive scientists trying to explain
how humans learn, reason and communicate. In this talk I will sketch
some of the challenges and prospects for Bayesian models in cognitive
science, and also draw some lessons for advancing the state of the art
in probabilistic approaches to artificial intelligence.
I will focus on everyday reasoning tasks where people can routinely
draw successful generalizations from very limited evidence. These
generalizations can be modeled as Bayesian inferences constrained by
people's intuitive theories about the causal structure of the world.
I will present several case studies drawn from task domains such as
diagnostic reasoning, predicting the duration of events, inferring the
properties of biological species, and learning physical laws. Time
permitting, I will also talk about some recent work on how people
might learn their abstract theories about the structure of these
domains, and some applications of our models to problems in machine
learning such as semi-supervised classification and relational
clustering.

#### About the Speaker

I study the
computational basis of human learning and inference. Through a
combination of mathematical modeling, computer simulation, and
behavioral experiments, I try to uncover the logic behind our everyday
inductive leaps: constructing perceptual representations, separating
"style" and "content" in perception, learning concepts and words,
judging similarity or representativeness, inferring causal
connections, noticing coincidences, predicting the future. I approach
these topics with a range of empirical methods -- primarily,
behavioral testing of adults, children, and machines -- and formal
tools -- drawn chiefly from Bayesian statistics and probability
theory, but also from geometry, graph theory, and linear algebra. My
work is driven by the complementary goals of trying to achieve a
better understanding of human learning in computational terms and
trying to build computational systems that come closer to the
capacities of human learners.

*Contact: *`bac-coordinators@cs.stanford.edu`
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