Abstract
This position paper sketches an analysis of the essential features that logic-based programming languages will need to embrace to compete in a quickly evolving field where learnability and expressiveness of language constructs, seen as aspects of a learner’s user experience, have become dominant decision factors for choosing a programming language or paradigm.
Our analysis centers on the main driving force in the evolution of programming languages: automation of coding tasks, a recurring promise of declarative languages, instrumental for developing software artifacts competitively.
In this context we will focus on taking advantage of the close correspondence between logic-based language constructs and their natural language equivalents, the adoption of language constructs enhancing the expressiveness and learnability of logic-based programming languages and their synergistic uses in interacting declaratively with deep learning frameworks.
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Notes
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at https://github.com/ptarau/natlog, ready to install with “pip3 install natlog”.
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but not closer, as unnecessary verbosity can hinder expressiveness.
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Acknowledgments
These reflections have been inspired by the live and deep-probing Prolog’50 discussions lead by Bob Kowalski and Veronica Dahl with focus on logical thinking and logic-based programming as well as on approaches to make logic-based programming accessible to newcomers, including use cases for a first-contact introduction to computing. I am thankful to the participants of these meetings for sharing their thoughts on both the last 50 years and the next 50 years of logic programming. Finally, many thanks go to the reviewers of the paper for their careful reading and constructive suggestions that helped clarify and substantiate key concepts covered in the paper.
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Tarau, P. (2023). Reflections on Automation, Learnability and Expressiveness in Logic-Based Programming Languages. In: Warren, D.S., Dahl, V., Eiter, T., Hermenegildo, M.V., Kowalski, R., Rossi, F. (eds) Prolog: The Next 50 Years. Lecture Notes in Computer Science(), vol 13900. Springer, Cham. https://doi.org/10.1007/978-3-031-35254-6_29
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