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- W3186312207 abstract "Author(s): Wang, Haoliang; Vul, Ed; Polikarpova, Nadia; Fan, Judith E. | Abstract: What computations enable humans to leap from mere obser- vations to rich explanatory theories? Prior work has focused on stochastic algorithms that rely on random, local perturba- tions to model the search for satisfactory theories. Here we introduce a new approach inspired by the practice of ‘debug- ging’ from computer programming, whereby learners use past experience to constrain future proposals, and are thus able to consider large leaps in their current theory to fix specific de- ficiencies. We apply our ‘debugging’ algorithm to the mag- netism domain introduced by (Ullman, Goodman, a Tenen- baum, 2010) and compare its efficiency and accuracy to their stochastic-search algorithm. We find that our algorithm not only requires fewer iterations to find a solution, but that the solutions it finds more reliably recover the correct latent theo- ries, and are more robust to sparse data. Our findings suggest the promise of such constraint-based approaches to emulate the way humans efficiently navigate large, discrete hypothesis spaces." @default.
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- W3186312207 date "2021-01-01" @default.
- W3186312207 modified "2023-09-22" @default.
- W3186312207 title "Theory Acquisition as Constraint-Based Program Synthesis" @default.
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