Artificial Intelligence Lunch Seminar

Tuesday, April 19, 2016 - 12:00pm to 1:00pm


3305 Newell-Simon Hall



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.

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