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- W2395903764 abstract "Most logic-based machine learning algorithms rely on an Occamist bias where textual simplicity of hypotheses is optimised. This approach, however, fails to distinguish between the efficiencies of hypothesised programs, such as quick sort (O(n log n)) and bubble sort (O(n2)). We address this issue by considering techniques to minimise both the resource complexity and textual complexity of hypothesised programs. We describe an algorithm proven to learn optimal resource complexity robot strategies, and we propose future work to generalise this approach to a broader class of logic programs." @default.
- W2395903764 created "2016-06-24" @default.
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- W2395903764 date "2018-04-23" @default.
- W2395903764 modified "2023-09-29" @default.
- W2395903764 title "Learning efficient logic programs" @default.
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- W2395903764 doi "https://doi.org/10.1007/s10994-018-5712-6" @default.
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