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Kind inference for datatypes

POPL 4(POPL)2019
Ningning Xie, Richard A. Eisenberg, Bruno C. d. S. Oliveira

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原文摘要(Abstract)

In recent years, languages like Haskell have seen a dramatic surge of new features that significantly extends the expressive power of their type systems. With these features, the challenge of kind inference for datatype declarations has presented itself and become a worthy research problem on its own. This paper studies kind inference for datatypes. Inspired by previous research on type-inference, we offer declarative specifications for what datatype declarations should be accepted, both for Haskell98 and for a more advanced system we call PolyKinds, based on the extensions in modern Haskell, including a limited form of dependent types. We believe these formulations to be novel and without precedent, even for Haskell98. These specifications are complemented with implementable algorithmic versions. We study soundness, completeness and the existence of principal kinds in these systems, proving the properties where they hold. This work can serve as a guide both to language designers who wish to formalize their datatype declarations and also to implementors keen to have principled inference of principal types.

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DOI 原文 ·

BibTeX
@article{paperbot571,
  title = {Kind inference for datatypes},
  author = {Ningning Xie and Richard A. Eisenberg and Bruno C. d. S. Oliveira},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {4},
  number = {POPL},
  year = {2019},
  doi = {10.1145/3371121}
}