Learning as program induction
- Neil Bramley, Psychology and Data Science, NYU, New York, New York, United States
- Eric Schulz, Psychology, Harvard, Boston, Massachusetts, United States
- Fei Xu, Psychology, UC Berkeley, Berkeley, California, United States
- Josh Tenenbaum, Brain and Cognitive Sciences, MIT, Cambridge, Massachusetts, United States
AbstractThis workshop will cover new work that casts human learning as program induction - i.e. learning of programs from data. The notion that the mind approximates rational (Bayesian) inference has had a strong influence on thinking in psychology since the 1950s. In constrained scenarios, typical of psychology experiments, people often behave in ways that approximate the dictates of probability theory. However, natural learning contexts are typically much more open-ended --- there are often no clear limits on what is possible, and initial proposals often prove inadequate. This means that coming up with the right hypotheses and theories in the first place is often much harder than ruling among them. How do people, and how can machines, expand their hypothesis spaces to generate wholly new ideas, plans and solutions? Recent work has begun to shed light on this problem via the idea that many aspects of learning can be better understood through the mathematics of program induction (Chater & Oaksford, 2013; Lake, Salakhutdinov, & Tenenbaum, 2015).
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