Collaborating Authors


AI-Lab - Automatic Programming

AITopics Original Links

Our goal is automatic generation of computer programs from specifications that are much smaller and easier to write than ordinary programs. Generation of geometric programs specified by diagrams 2011 Yulin Li and Gordon S. Novak, Jr., In Proceedings of the 10th ACM international conference on Generative programming and component engineering, pp.

A Perspective on Automatic Programming

AI Magazine

Most work in automatic programming has focused primarily on the roles of deduction and programming knowledge. However, the role played by knowledge of the task domain seems to be at least as important, both for the usability of an automatic programming system and for the feasibility of building one which works on non-trivial problems. This perspective has evolved during the course of a variety of studies over the last several years, including detailed examination of existing software for a particular domain (quantitative interpretation of oil well logs) and the implementation of an experimental automatic programming system for that domain. The importance of domain knowledge has two important implications: a primary goal of automatic programming research should be to characterize the programming process for specific domains; and a crucial issue to be addressed in these characterizations is the interaction of domain and programming knowledge during program synthesis.

Principles of artificial intelligence


A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. Palo Alto, California: Tioga.

An experiment in knowledge-based automatic programming


Summary of Stanford Ph.D. dissertation, Computer Science Dept. Stanford University (1977).Artificial Intelligence 12(2): 73-119.