Learning large logic programs by going beyond entailment
| Autor: | Andrew Cropper, Sebastijan Dumancic |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Theoretical computer science Binary decision diagram Computer science Computer Science - Artificial Intelligence media_common.quotation_subject String (computer science) ASCII art 02 engineering and technology Logical consequence Machine Learning (cs.LG) Artificial Intelligence (cs.AI) Inductive logic programming 020204 information systems visual_art 0202 electrical engineering electronic engineering information engineering Key (cryptography) visual_art.visual_art_medium 020201 artificial intelligence & image processing Function (engineering) Program synthesis media_common |
| Zdroj: | IJCAI |
| Popis: | A major challenge in inductive logic programming (ILP) is learning large programs. We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search. This approach is limited because entailment is a binary decision: a hypothesis either entails an example or does not, and there is no intermediate position. To address this limitation, we go beyond entailment and use \emph{example-dependent} loss functions to guide the search, where a hypothesis can partially cover an example. We implement our idea in Brute, a new ILP system which uses best-first search, guided by an example-dependent loss function, to incrementally build programs. Our experiments on three diverse program synthesis domains (robot planning, string transformations, and ASCII art), show that Brute can substantially outperform existing ILP systems, both in terms of predictive accuracies and learning times, and can learn programs 20 times larger than state-of-the-art systems. IJCAI2020 paper |
| Databáze: | OpenAIRE |
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