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Evolutionary program induction directed by logic grammars

Wong1, Leung

  • 1Department of Computing Studies, Hong Kong Baptist University, Hong Kong. mlwong@comp.hkbu.edu.hk

Evolutionary Computation
|July 1, 1997
PubMed
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We introduce LOGENPRO, a novel system unifying inductive logic programming (ILP) and genetic programming (GP) for program induction. LOGENPRO enhances problem-solving power by integrating domain knowledge and supporting diverse programming languages.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Logic

Background:

  • Program induction aims to generate computer programs for specific behaviors.
  • Inductive Logic Programming (ILP) and Genetic Programming (GP) are distinct program induction approaches.
  • Integrating ILP and GP offers enhanced problem-solving capabilities but faces formal challenges.

Purpose of the Study:

  • To present LOGENPRO, a flexible system unifying ILP and GP techniques.
  • To enable program induction across various programming languages using logic grammars.
  • To incorporate context-sensitive and domain-dependent knowledge into program evolution.

Main Methods:

  • LOGENPRO utilizes logic grammars to control program evolution.
  • It integrates techniques from both Genetic Programming (GP) and Inductive Logic Programming (ILP).

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  • The system employs domain-specific knowledge, such as argument types, within a unified framework.

    • Main Results:

    • LOGENPRO successfully emulates GP and GP with Automatically Defined Functions (ADFs).
    • It demonstrates superior performance compared to GP and GP with ADFs when domain knowledge is available.
    • Experiments show the impact of domain knowledge and training data noise on learning speed.

    Conclusions:

    • LOGENPRO provides a unified framework for integrating ILP and GP.
    • The system's flexibility allows for program induction in diverse languages and incorporation of domain knowledge.
    • LOGENPRO offers improved performance and learning efficiency, particularly with available domain-specific information.