An analytical inductive functional programming system that avoids unintended programs

  • Authors:

  • Susumu Katayama

  • Affiliations:

  • University of Miyazaki, Miyazaki, Japan

  • Venue:
  • PEPM '12 Proceedings of the ACM SIGPLAN 2012 workshop on Partial evaluation and program manipulation
  • Year:
  • 2012

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Abstract

Inductive functional programming (IFP) is a research field extending from software science to artificial intelligence that deals with functional program synthesis based on generalization from ambiguous specifications, usually given as input-output example pairs. Currently, the approaches to IFP can be categorized into two general groups: the analytical approach that is based on analysis of the input-output example pairs, and the generate-and-test approach that is based on generation and testing of many candidate programs. The analytical approach shows greater promise for application to greater problems because the search space is restricted by the given example set, but it requires much more examples written in orderto yield results that reflect the user's intention, which is bothersome and causes the algorithm to slow down. On the other hand, the generate-and-test approach does not require long description of input-output examples, but does not restrict the search space using the example set. This paper proposes a new approach taking the best of the two, called "analytically-generate-and-test approach", which is based on analytical generation and testing of many program candidates. For generating many candidate programs, the proposed system uses a new variant of I GOR II, the exemplary analytical inductive functional programming algorithm. This new system preserves the efficiency features of analytical approaches, while minimizing the possibility of generating unintended programs even when using fewer input-output examples.