31 research outputs found
Towards meta-interpretive learning of programming language semantics
We introduce a new application for inductive logic programming: learning the
semantics of programming languages from example evaluations. In this short
paper, we explored a simplified task in this domain using the Metagol
meta-interpretive learning system. We highlighted the challenging aspects of
this scenario, including abstracting over function symbols, nonterminating
examples, and learning non-observed predicates, and proposed extensions to
Metagol helpful for overcoming these challenges, which may prove useful in
other domains.Comment: ILP 2019, to appea
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How does predicate invention affect human comprehensibility?
During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as that of Mitchell, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present the results of experiments testing human comprehensibility of logic programs learned with and without predicate invention. Results indicate that comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols
Logical Reduction of Metarules
International audienceMany forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called derivation reduction, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperform subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times
Abductive knowledge induction from raw data
For many reasoning-heavy tasks with raw inputs, it is challenging to design an appropriate end-to-end pipeline to formulate the problem-solving process. Some modern AI systems, e.g., Neuro-Symbolic Learning, divide the pipeline into sub-symbolic perception and symbolic reasoning, trying to utilise data-driven machine learning and knowledge-driven problem-solving simultaneously. However, these systems suffer from the exponential computational complexity caused by the interface between the two components, where the sub-symbolic learning model lacks direct supervision, and the symbolic model lacks accurate input facts. Hence, they usually focus on learning the sub-symbolic model with a complete symbolic knowledge base while avoiding a crucial problem: where does the knowledge come from? In this paper, we present Abductive Meta-Interpretive Learning (MetaAbd) that unites abduction and induction to learn neural networks and logic theories jointly from raw data. Experimental results demonstrate that MetaAbd not only outperforms the compared systems in predictive accuracy and data efficiency but also induces logic programs that can be re-used as background knowledge in subsequent learning tasks. To the best of our knowledge, MetaAbd is the first system that can jointly learn neural networks from scratch and induce recursive first-order logic theories with predicate invention
Knowledge Refactoring for Inductive Program Synthesis
Humans constantly restructure knowledge to use it more efficiently. Our goal
is to give a machine learning system similar abilities so that it can learn
more efficiently. We introduce the \textit{knowledge refactoring} problem,
where the goal is to restructure a learner's knowledge base to reduce its size
and to minimise redundancy in it. We focus on inductive logic programming,
where the knowledge base is a logic program. We introduce Knorf, a system which
solves the refactoring problem using constraint optimisation. We evaluate our
approach on two program induction domains: real-world string transformations
and building Lego structures. Our experiments show that learning from
refactored knowledge can improve predictive accuracies fourfold and reduce
learning times by half.Comment: 7 pages, 6 figure