Abstract
Zoea is a knowledge-based inductive programming system that generates code directly from a set of test cases. It also allows developers to combine generated software in a variety of ways to form programs of arbitrary size. The Zoea compiler is built using a modern variant of the blackboard architecture. Zoea integrates a large number of knowledge sources that encode different elements of programming language and software development expertise, using test cases as a ubiquitous basis for knowledge representation. Hypotheses are managed through the creation of synthetic test cases and blackboard recursion. We briefly outline the text-based and visual specification languages, and the associated composable inductive programming development paradigm. The benefits of the approach and some plans for future development are also identified.
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References
Flener, P., Schmid, U.: An introduction to inductive programming. Artif. Intell. Rev. 29(1), 45–62 (2008)
Kitzelmann, E.: Analytical inductive functional programming. In: Hanus, Michael (ed.) LOPSTR 2008. LNCS, vol. 5438, pp. 87–102. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00515-2_7
Galwani, S., Hernandez-Orallo, J., Kitzelmann, E., Muggleton, S.H., Schmid, U., Zorn, B.: Inductive programming meets the real world. Commun. ACM 58(11), 90–99 (2015)
Zoea Code Examples. http://rosettacode.org/wiki/Category:Zoea. Accessed 2 Sept 2021
McDaid, E., McDaid, S.: Zoea – Composable Inductive Programming Without Limits (2019). arXiv:1911.08286
Acknowledgements
This work was funded and carried out by Zoea Ltd (https://zoea.co.uk). Zoea is a trademark of Zoea Ltd. Other trademarks are the property of their respective owners.
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McDaid, E., McDaid, S. (2021). Knowledge-Based Composable Inductive Programming. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVIII. SGAI-AI 2021. Lecture Notes in Computer Science(), vol 13101. Springer, Cham. https://doi.org/10.1007/978-3-030-91100-3_13
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DOI: https://doi.org/10.1007/978-3-030-91100-3_13
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