Learning logic programs by combining programs

• URL: http://arxiv.org/abs/2206.01614v3
• Date: Thu, 17 Aug 2023 09:43:54 GMT
• Title: Learning logic programs by combining programs
• Authors: Andrew Cropper and C\'eline Hocquette
• Abstract summary: We introduce an approach where we learn small non-separable programs and combine them. We implement our approach in a constraint-driven ILP system. Our experiments on multiple domains, including game playing and program synthesis, show that our approach can drastically outperform existing approaches.
• Score: 24.31242130341093
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The goal of inductive logic programming is to induce a logic program (a set of logical rules) that generalises training examples. Inducing programs with many rules and literals is a major challenge. To tackle this challenge, we introduce an approach where we learn small non-separable programs and combine them. We implement our approach in a constraint-driven ILP system. Our approach can learn optimal and recursive programs and perform predicate invention. Our experiments on multiple domains, including game playing and program synthesis, show that our approach can drastically outperform existing approaches in terms of predictive accuracies and learning times, sometimes reducing learning times from over an hour to a few seconds.

Related papers

• Learning logic programs by finding minimal unsatisfiable subprograms [24.31242130341093]
  We introduce an ILP approach that identifies minimal unsatisfiable subprograms (MUSPs) Our experiments on multiple domains, including program synthesis and game playing, show that our approach can reduce learning times by 99%.
  arXiv  Detail & Related papers  (2024-01-29T18:24:16Z)
• Learning MDL logic programs from noisy data [19.749004264961492]
  We introduce an approach that learns minimal description length programs from noisy data. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches.
  arXiv  Detail & Related papers  (2023-08-18T08:49:30Z)
• Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs [58.94569213396991]
  We propose a hierarchical programmatic reinforcement learning framework to produce program policies. By learning to compose programs, our proposed framework can produce program policies that describe out-of-distributionally complex behaviors. The experimental results in the Karel domain show that our proposed framework outperforms baselines.
  arXiv  Detail & Related papers  (2023-01-30T14:50:46Z)
• Relational program synthesis with numerical reasoning [18.27510863075184]
  We introduce an inductive logic programming approach which combines relational learning with numerical reasoning. Our approach, which we call NUMSYNTH, uses satisfiability modulo theories solvers to efficiently learn programs with numerical values. Our experiments on four diverse domains, including game playing and program synthesis, show that our approach can (i) learn programs with numerical values from linear arithmetical reasoning, and (ii) outperform existing approaches in terms of predictive accuracies and learning times.
  arXiv  Detail & Related papers  (2022-10-03T08:41:04Z)
• Learning from Self-Sampled Correct and Partially-Correct Programs [96.66452896657991]
  We propose to let the model perform sampling during training and learn from both self-sampled fully-correct programs and partially-correct programs. We show that our use of self-sampled correct and partially-correct programs can benefit learning and help guide the sampling process. Our proposed method improves the pass@k performance by 3.1% to 12.3% compared to learning from a single reference program with MLE.
  arXiv  Detail & Related papers  (2022-05-28T03:31:07Z)
• Learning logic programs through divide, constrain, and conquer [22.387008072671005]
  We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our experiments on three domains (classification, inductive general game playing, and program synthesis) show that our approach can increase predictive accuracies and reduce learning times.
  arXiv  Detail & Related papers  (2021-09-16T09:08:04Z)
• Searching for More Efficient Dynamic Programs [61.79535031840558]
  We describe a set of program transformations, a simple metric for assessing the efficiency of a transformed program, and a search procedure to improve this metric. We show that in practice, automated search can find substantial improvements to the initial program.
  arXiv  Detail & Related papers  (2021-09-14T20:52:55Z)
• Learning to Synthesize Programs as Interpretable and Generalizable Policies [25.258598215642067]
  We present a framework that learns to synthesize a program, which details the procedure to solve a task in a flexible and expressive manner. Experimental results demonstrate that the proposed framework not only learns to reliably synthesize task-solving programs but also outperforms DRL and program synthesis baselines.
  arXiv  Detail & Related papers  (2021-08-31T07:03:06Z)
• Latent Programmer: Discrete Latent Codes for Program Synthesis [56.37993487589351]
  In many sequence learning tasks, such as program synthesis and document summarization, a key problem is searching over a large space of possible output sequences. We propose to learn representations of the outputs that are specifically meant for search: rich enough to specify the desired output but compact enough to make search more efficient. We introduce the emphLatent Programmer, a program synthesis method that first predicts a discrete latent code from input/output examples, and then generates the program in the target language.
  arXiv  Detail & Related papers  (2020-12-01T10:11:35Z)
• BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration [72.88493072196094]
  We present a new synthesis approach that leverages learning to guide a bottom-up search over programs. In particular, we train a model to prioritize compositions of intermediate values during search conditioned on a set of input-output examples. We show that the combination of learning and bottom-up search is remarkably effective, even with simple supervised learning approaches.
  arXiv  Detail & Related papers  (2020-07-28T17:46:18Z)
• The ILASP system for Inductive Learning of Answer Set Programs [79.41112438865386]
  Our system learns Answer Set Programs, including normal rules, choice rules and hard and weak constraints. We first give a general overview of ILASP's learning framework and its capabilities. This is followed by a comprehensive summary of the evolution of the ILASP system.
  arXiv  Detail & Related papers  (2020-05-02T19:04:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.