144:(A Learning Engine for Proposing Hypotheses) is an inductive logic programming system introduced by Ashwin Srinivasan in 2001. As of 2022 it is still one of the most widely used inductive logic programming systems. It is based on the earlier system
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in a breadth-first manner, with tunable parameters to bound the maximal clause size and proof depth. It scores each clause using one of 13 different evaluation metrics, as chosen in advance by the user.
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As output it returns a logic program which, together with the background knowledge, entails all of the positive examples and none of the negative examples.
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Burnside, Elizabeth S.; Davis, Jesse; Costa, Vítor Santos; de Castro Dutra, Inês; Kahn, Charles E.; Fine, Jason; Page, David (2005).
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in a top-down manner, using the bottom clause constructed in the preceding step to bound the search from below. It searches the
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It then adds the new clause to the hypothesis program and removes all examples that are covered by the new clause.
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It chooses a positive example to generalise; if none are left, it aborts and outputs the current hypothesis.
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It then searches for a generalisation of the bottom clause that scores better on the chosen metric.
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Then it constructs the bottom clause, that is, the most specific
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that is allowed by the mode declarations and covers the example.
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Starting with an empty hypothesis, Aleph proceeds as follows:
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The input to Aleph is background knowledge, specified as a
319:"Inductive Logic Programming At 30: A New Introduction"
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Cropper, Andrew; Dumančić, Sebastijan (2022-06-15).
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42:Ashwin Srinivasan, Fabrizio Riguzzi
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16:Inductive logic programming system
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83:https://github.com/friguzzi/aleph
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372:Inductive logic programming
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105:Inductive logic programming
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57:5 / May 16, 2007
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329:: 766–850.
127:/activities
366:Categories
336:2008.07912
286:: 96–100.
268:References
89:Written in
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64:2007-05-16
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