### Topics

Artificial intelligence, machine learning, predictive analytic modeling, chess, decision making, bounded rational choice, general issues in scientific modeling and statistical fitting, cheating detection.

### Description

I have designed a predictive-analytic model that projects the probabilities that humans will choose various decision options, given hindsight values of their worth. Plugging in values given to chess moves by strong computer programs makes the model work for chess, but that is the only chess-specific content. What else besides chess can be done with a model whose general task is "Converting Utilities Into Probabilities"? Why has it been so effective as to be deemed useful in court testimony in chess-cheating cases and covered by the New York Times, NPR, and the Wall Street Journal? Chess may be complex, but on mass scale, human players still follow simple mathematical laws---perhaps we will discover some more.
The seminar aims to relate this research to other Machine Learning applications that have been researched in the Department, and to explore issues in their common methodology. This includes comparing the many different statistical fitting methods (Bayesian, max-likelihood, simple frequentist, and more) that can be used and judged within this same model. No background in chess is assumed---the basics of chess programs will be covered in the initial series of lectures by me.

Here are a
two-page description and a longer
overview of the research, the latter with some mathematical details.
My homepage
links my public anti-cheating site, papers, talks,
New York Times article, and other pages; students in the seminar will be given access to my private sites where testing is done. The last section of the overview includes some possible seminar topics and projects within this research, but students will be equally welcome to give presentations relating it to machine-learning related topics they have had in other courses.

Students are expected to participate in discussions and give at least two hours
of presentations. Grading is S/U, 1--3 credits.