Logical reduction of metarules

Cropper, Andrew; Tourret, Sophie

doi:10.1007/s10994-019-05834-x

Cited by 24 publications

(56 citation statements)

References 52 publications

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“…Compared to Aleph, Popper can learn optimal and recursive programs. 2 Compared to Metagol, Popper does not need metarules (Cropper and Tourret 2020), so can learn programs with any arity predicates. Compared to ∂ILP, Popper 1 Popper is named after Karl Poppper, whose idea of falsification (Popper 2005) inspired our approach, as it did Shapiro's MIS approach (Shapiro 1983).…”

Section: ⎫ ⎬ ⎭mentioning

confidence: 99%

“…Metarules (Cropper and Tourret 2020) are another popular syntactic bias used by many ILP approaches (De Raedt and Bruynooghe 1992;Wang et al 2014;Albarghouthi et al 2017;Kaminski et al 2018), including Metagol (Muggleton et al 2015;Cropper and Muggleton 2016) and, to an extent 5 , ∂ILP (Evans and Grefenstette 2018). A metarule is a higher-order clause which defines the exact form of clauses in the hypothesis space.…”

Section: Language Biasmentioning

confidence: 99%

“…Compared with predicate (and mode) declarations, metarules are a much stronger inductive bias because they specify the exact form of clauses in the hypothesis space. However, the major problem with metarules is determining which ones to use (Cropper and Tourret 2020). A user must either (i) provide a set of metarules, or (ii) use a set of metarules restricted to a certain fragment of logic, e.g.…”

Section: Language Biasmentioning

confidence: 99%

“…A user must either (i) provide a set of metarules, or (ii) use a set of metarules restricted to a certain fragment of logic, e.g. dyadic Datalog (Cropper and Tourret 2020). This limitation means that ILP systems that use metarules are difficult to use, especially when the BK contains predicate symbols with arity greater than two.…”

Section: Language Biasmentioning

confidence: 99%

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Learning programs by learning from failures

2021

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We describe an inductive logic programming (ILP) approach called learning from failures. In this approach, an ILP system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain. In the generate stage, the learner generates a hypothesis (a logic program) that satisfies a set of hypothesis constraints (constraints on the syntactic form of hypotheses). In the test stage, the learner tests the hypothesis against training examples. A hypothesis fails when it does not entail all the positive examples or entails a negative example. If a hypothesis fails, then, in the constrain stage, the learner learns constraints from the failed hypothesis to prune the hypothesis space, i.e. to constrain subsequent hypothesis generation. For instance, if a hypothesis is too general (entails a negative example), the constraints prune generalisations of the hypothesis. If a hypothesis is too specific (does not entail all the positive examples), the constraints prune specialisations of the hypothesis. This loop repeats until either (i) the learner finds a hypothesis that entails all the positive and none of the negative examples, or (ii) there are no more hypotheses to test. We introduce Popper, an ILP system that implements this approach by combining answer set programming and Prolog. Popper supports infinite problem domains, reasoning about lists and numbers, learning textually minimal programs, and learning recursive programs. Our experimental results on three domains (toy game problems, robot strategies, and list transformations) show that (i) constraints drastically improve learning performance, and (ii) Popper can outperform existing ILP systems, both in terms of predictive accuracies and learning times.

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Section: ⎫ ⎬ ⎭mentioning

confidence: 99%

Section: Language Biasmentioning

confidence: 99%

Section: Language Biasmentioning

confidence: 99%

Section: Language Biasmentioning

confidence: 99%

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Learning programs by learning from failures

2021

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“…To compute the benchmark, we set Metagol to use the same metarules for all games and tasks. This set is composed of 9 derivationally irreducible metarules [16,17], a set of metarules to allow for constants in a program, and a set of nullary metarules (to learn the terminal predicates). Full details on the metarules used can be found in the code repository.…”

Section: Metagolmentioning

confidence: 99%

Inductive general game playing

2019

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General game playing (GGP) is a framework for evaluating an agent's general intelligence across a wide range of tasks. In the GGP competition, an agent is given the rules of a game (described as a logic program) that it has never seen before. The task is for the agent to play the game, thus generating game traces. The winner of the GGP competition is the agent that gets the best total score over all the games. In this paper, we invert this task: a learner is given game traces and the task is to learn the rules that could produce the traces. This problem is central to inductive general game playing (IGGP). We introduce a technique that automatically generates IGGP tasks from GGP games. We introduce an IGGP dataset which contains traces from 50 diverse games, such as Sudoku, Sokoban, and Checkers. We claim that IGGP is difficult for existing inductive logic programming (ILP) approaches. To support this claim, we evaluate existing ILP systems on our dataset. Our empirical results show that most of the games cannot be correctly learned by existing systems. The best performing system solves only 40% of the tasks perfectly. Our results suggest that IGGP poses many challenges to existing approaches. Furthermore, because we can automatically generate IGGP tasks from GGP games, our dataset will continue to grow with the GGP competition, as new games are added every year. We therefore think that the IGGP problem and dataset will be valuable for motivating and evaluating future research.We are very grateful to the following for feedback and guidance throughout this project:

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Inductive Logic Programming

Cropper

Dumančić

Evans

et al. 1999

Lecture Notes in Computer Science

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Abstract. Rules complement and extend ontologies on the Semantic Web. We refer to these rules as onto-relational since they combine DL-based ontology languages and Knowledge Representation formalisms supporting the relational data model within the tradition of Logic Programming and Deductive Databases. Rule authoring is a very demanding Knowledge Engineering task which can be automated though partially by applying Machine Learning algorithms. In this chapter we show how Inductive Logic Programming (ILP), born at the intersection of Machine Learning and Logic Programming and considered as a major approach to Relational Learning, can be adapted to Onto-Relational Learning. For the sake of illustration, we provide details of a specific Onto-Relational Learning solution to the problem of learning rule-based definitions of DL concepts and roles with ILP.

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Logical reduction of metarules

Cited by 24 publications

References 52 publications

Learning programs by learning from failures

Learning programs by learning from failures

Inductive general game playing

Inductive Logic Programming

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