[AI Seminar] AI Lunch -- Christopher MacLellan -- April 19, 2016

[Ellen Vitercik]

Dear faculty and students,

We look forward to seeing you this Tuesday, April 19th, at noon in NSH 3305
for AI lunch. To learn more about the seminar and lunch, or to volunteer to
give a talk, please visit the AI Lunch webpage
<http://www.cs.cmu.edu/~aiseminar/>. *We are looking for someone to give a
talk on May 10th*.

On Tuesday, Christopher MacLellan <http://www.christopia.net/> will give a
talk titled "Using the Apprentice Learner Architecture to model human
learning from demonstrations and feedback in digital environments."

*Abstract:* Understanding the nature of human intelligence and developing
intelligent agents capable of modeling humans are fundamental goals of
artificial intelligence research. Prior work modeling human problem solving
has explored how hand-constructed domain models (e.g., production-rule
models) can be used to explain human behavior. Typically, these models
account for how humans improve their problem-solving performance given
practice (i.e., speed-up learning), but they do not account for how humans
acquire initial domain models. One approach to acquiring domain knowledge
that has been explored in the machine learning literature is *Apprentice
Learning* (also called programming by demonstration, learning by watching,
or imitation learning). Previous work has explored how apprentice learning
can be used as an efficient, user-friendly approach to programming
computers and robots by providing them with demonstrations rather than
computer code. In the current work, we investigate the possibility that
computational approaches for apprentice learning can be used to model human
learning in digital learning environments, such as intelligent tutoring
systems. Towards this end, I present the Apprentice Learner Architecture,
which provides a framework for building models of apprentice learning in
these digital environments. Next, I show how this architecture can be used
to construct models of human learning capable of simulating and predicting
human behavior in intelligent tutors. In particular, I show how apprentice
learner models can be used to simulate human behavior in a fraction
arithmetic tutor and how these simulations can be used to accurately
predict the main experimental effects of a human study that used the
fractions tutor. Finally, I conclude with directions for future work.

Best,
Ellen and Ariel
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