Relational decomposition for program synthesis

2024-08-22

arXiv.org

AI Summary

This work addresses program synthesis by proposing a general relational decomposition framework: input-output examples are encoded as sets of logical facts, and the mapping between them is explicitly modeled as a logical relation. Methodologically, it formalizes program synthesis as a relational subtask decomposition problem—marking the first such formulation—and leverages inductive logic programming (ILP) for interpretable, model-agnostic relation learning and inference. Crucially, no domain-specific architecture or task customization is required, enabling cross-task generalization. Evaluated on four challenging benchmarks, the approach significantly outperforms standard sequence- and tree-based neural models. Moreover, by interfacing with off-the-shelf ILP solvers, it surpasses state-of-the-art domain-specific synthesizers across multiple benchmarks. The contribution is a novel, interpretable, modular, and model-independent paradigm for program synthesis grounded in relational logic and ILP.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Statistical Relational/Logic Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We introduce a relational approach to program synthesis. The key idea is to decompose synthesis tasks into simpler relational synthesis subtasks. Specifically, our representation decomposes a training input-output example into sets of input and output facts respectively. We then learn relations between the input and output facts. We demonstrate our approach using an off-the-shelf inductive logic programming (ILP) system on four challenging synthesis datasets. Our results show that (i) our representation can outperform a standard one, and (ii) an off-the-shelf ILP system with our representation can outperform domain-specific approaches.

Problem

• Introduces relational approach to program synthesis
• Decomposes tasks into simpler relational subtasks
• Improves synthesis using inductive logic programming

Innovation

• Relational decomposition technique
• Inductive logic programming system
• Input-output facts learning