Best-Effort Inductive Logic Programming via Fine-grained Cost-based Hypothesis Generation
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We describe the Inspire system which participated in the first competition on Inductive Logic Programming (ILP). Inspire is based on Answer Set Programming (ASP), its most important feature is an ASP encoding for hypothesis space generation: given a set of facts representing the mode bias, and a set of cost configuration parameters, each answer set of this encoding represents a single rule that is considered for finding a hypothesis that entails the given examples. Compared with state-of-the-art methods that use the length of the rule body as a metric for rule complexity, our approach permits a much more fine-grained specification of the shape of hypothesis candidate rules. Similar to the ILASP system, our system iteratively increases the rule cost limit until it finds a suitable hypothesis. Different from ILASP, our approach generates a new search space for each cost limit. The Inspire system searches for a hypothesis that entails a single example at a time, utilizing a simplification of the ASP encoding used in the XHAIL system. To evaluate ASP we use Clingo. We perform experiments with the development and test set of the ILP competition. For comparison we also adapted the ILASP system to process competition instances. Experimental results show, that Inspire performs better than ILASP, and that cost parameters for the hypothesis search space are an important factor for finding suitable hypotheses efficiently.
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