Issues in Research Methodology

Throughout my career, I have been concerned with the methodology of machine learning, artificial intelligence, and cognitive science. My commentaries on this topic - which have addressed the experimental evaluation of AI systems, formal analyses of AI methods, computational models of human behavior, and applications of machine learning - have made their way into various papers and editorials. Here I give links to essays on more general topics, including the need for constructing complete intelligent agents and the advantages of unified rather than divisive science.

Related Publications

Langley, P., Meadows, B., Sridharan, M., & Choi, D. (in press). Explainable agency for intelligent autonomous systems. Proceedings of the Twenty-Ninth Annual Conference on Innovative Applications of Artificial Intelligence. San Francisco: AAAI Press.

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.

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

Cassimatis, N. L., Bello, P., & Langley, P. (2008). Ability, breadth and parsimony in computational models of higher-order cognition. Cognitive Science, 32, 1304-1322.

Lavrac, N., Motoda, H., Fawcett, T., Holte, R., Langley, P., & Adriaans, P. (in press). Lessons learned from data mining applications and collaborative problem solving. Machine Learning, 57, 13-34.

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. (2000). The computational support of scientific discovery. International Journal of Human-Computer Studies, 53, 393-410. 393-410.

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. (1999). The computer-aided discovery of scientific knowledge . Proceedings of the First International Conference on Discovery Science. Fukuoka, Japan: Springer.

Langley, P., & Fehling, M. (1998). The experimental study of adaptive user interfaces (Technical Report 98-3). Institute for the Study of Learning and Expertise, Palo Alto, CA.

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. (1997). Machine learning for adaptive user interfaces. Proceedings of the 21st German Annual Conference on Artificial Intelligence (pp. 53-62). Freiburg, Germany: Springer.

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., & Simon, H. A. (1995). Applications of machine learning and rule induction. Communications of the ACM, 38, November, 55-64.

Langley, P., & Kibler, D. (1991). The experimental study of machine learning. Unpublished manuscript, AI Research Branch, NASA Ames Research Center, Moffett Field, CA.

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.

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