Essays, Editorials, and Humor

All scientific disciplines rely centrally on technical papers to communicate novel problems, approaches, and results, but they also benefit from commentaries that step back and offer new perspectives on the field. Such commentaries are more fun to read, and can often be more fun to write, because the author can use polemical devices usually disallowed from technical pieces. Here I include my essays and editorials on machine learning, cognitive systems, and related topics. I also include two humorous papers that have poked fun at the former.


Essays on Cognitive Systems

Langley, P. (2016). The central role of cognition in learning. Advances in Cognitive Systems, 4, 3-12.

Langley, P. (2014). Four research challenges for cognitive systems. Advances in Cognitive Systems, 3, 3-11.

Langley, P. (2012). Intelligent behavior in humans and machines. Advances in Cognitive Systems, 2, 3-12.

Langley, P. (2012). The cognitive systems paradigm. Advances in Cognitive Systems, 1, 3-13.

Essays and Editorials on Machine Learning

Langley, P. (2011). The changing science of machine learning. Machine Learning, 82, 275-279.

Langley, P. (2000). Preface: The maturing science of machine learning. Proceedings of the Seventeenth International Conference on Machine Learning (pp. xi-xii). Stanford, CA: Morgan Kaufmann.

Langley, P. (2000). Crafting papers on machine learning. Proceedings of the Seventeenth International Conference on Machine Learning (pp. 1207-1211). Stanford, CA: Morgan Kaufmann.

Langley, P. (1997). Machine learning for intelligent systems. Proceedings of the Fourteenth National Conference on Artificial Intelligence (pp. 763-769). Providence, RI: AAAI Press.

Langley, P. (October, 1996). Relevance and insight in experimental studies. IEEE Expert, 11-12.

Langley, P. (1990). Advice to authors of machine learning papers. Machine Learning, 5, 233-237.

Langley, P. (1989). Unifying themes in empirical and explanation-based learning. Proceedings of the Sixth International Workshop on Machine Learning. Ithaca, NY: Morgan Kaufmann.

Langley, P. (1989). Toward a unified science of machine learning. Machine Learning, 3, 253-259.

Kibler, D., & Langley, P. (1988). Machine learning as an experimental science. Proceedings of the Third European Working Session on Learning (pp. 81-92). Glasgow: Pittman.

Langley, P. (1988). Induction and explanation: Complementary models of learning. Behavioral and Brain Sciences.

Langley, P. (1988). Machine learning as an experimental science. Machine Learning, 3, 5--8.

Langley, P. (1987). The emerging science of machine learning. Proceedings of the Fourth International Workshop on Machine Learning (pp. 5-6). Irvine, CA: Morgan Kaufmann.

Langley, P. (1987). Research papers in machine learning. Machine Learning, 2, 195-198.

Langley, P. (1987). Machine learning and concept formation. Machine Learning, 2, 99-102.

Langley, P. (1987). Machine learning and grammar induction. Machine Learning, 2, 5-8.

Langley, P. (1986). Machine learning and discovery. Machine Learning, 1, 363-366.

Langley, P. (1986). Human and machine learning. Machine Learning, 1, 243-248.

Langley, P. (1986). The terminology of machine learning. Machine Learning, 1, 14--144.

Langley, P. (1986). On machine learning. Machine Learning, 1, 5-10.

Miscellaneous Essays

Langley, P. (2004). Heuristics for scientific discovery: The legacy of Herbert Simon. In M. E. Augier & J. G. March (Eds.), Models of a man: Essays in memory of Herbert A. Simon. Cambridge, MA: MIT Press.

Langley, P., & Shafto, M. G. (1997). Expanding our mental horizons. Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society (pp. xxi-xxii). Mahwah, NJ: Lawrence Erlbaum.

Langley, P. (1988). Structure and process in schema-based architectures. Behavioral and Brain Sciences.

Academic Humor

Gangly, B. (1987). The devolving science of machine learning. Proceedings of the Fourth International Workshop on Machine Learning (pp. 398-401). Irvine, CA: Morgan Kaufmann.

Gangly, B. (2000). The degenerate science of machine learning. Proceedings of the Seventeenth International Conference on Machine Learning (pp. 1213-1216). Stanford, CA: Morgan Kaufmann.

For more information, send electronic mail to langley@isle.org


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