Formation of Probabilistic Concept Hierarchies

Upon moving to UCI in 1984, I began a long-term collaboration with Douglas Fisher, which led initially to analyses of methods for clustering and taxonomy formation. When Fisher developed Cobweb, an incremental unsupervised algorithm that formed hierarchies of probabilistic concept descriptions, I decided it would serve as an excellent base for Icarus, an architecture for physical agents that I was developing with other colleagues.

Over the next few years, our group developed a number of extensions to Cobweb. These included John Gennari's Classit (which handled numeric attributes and supported an attention mechanism), Kevin Thompson's Labyrinth (which dealt with concept formation over composite, structured objects), John Allen's Daedalus (which stored probabilistic summaries of planning decisions), and Wayne Iba's Maeander (which formed probabilistic hierarchies of motor skills).

After moving to NASA Ames, our group continued this work and attempted to integrate the various components in the Icarus architecture. Problems with order effects led to Arachne, a variant of Cobweb developed with Kate McKusick, and a growing interest in the naive Bayesian classifier. Our joint research on Cobweb and its relatives wound down in 1992, when I left NASA for greener pastures.

The list below does not include all publications related to Cobweb. Rather, it focuses on those papers that explore the basic algorithm and its uses in classification and prediction, rather than extensions to more complex problems.

Related Publications

Iba, W. F., & Langley, P. (2011). Cobweb models of categorization and probabilistic concept formation. In E. M. Pothos & A. J. Willis (Eds.), Formal approaches in categorization. Cambridge: Cambridge University Press.

Iba, W., & Langley, P. (2001). Unsupervised learning of probabilistic concept hierarchies. In G. Paliouras, V. Karkaletsis, & C. D. Spyropoulos (Eds)., Machine learning and its applications. Berlin: Springer.

Thompson, K., & Langley, P. (1991). Concept formation in structured domains. In D. H. Fisher, M. J. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann.

McKusick, K. B., & Langley, P. (1991). Constraints on tree structure in concept formation. Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (pp. 810-816). Sydney: Morgan Kaufmann.

Thompson, K., Langley, P., & Iba, W. F. (1991). Using background knowledge in concept formation. Proceedings of the Eighth International Workshop on Machine Learning (pp. 554-558). Evanston, IL: Morgan Kaufmann.

Fisher, D. H., & Langley, P. (1990). The structure and formation of natural categories. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in Research and Theory (Vol. 26). Cambridge, MA: Academic Press.

Billman, D., Fisher, D., Gluck, M., Langley, P., & Pazzani, M. (1990). Computational models of category learning. Proceedings of the Twelfth Conference of the Cognitive Science Society (pp. 989-996). Cambridge, MA: Lawrence Erlbaum.

Gennari, J. H., Langley, P., & Fisher, D. H. (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11-61.

Fisher, D., & Langley, P. (1986). Methods of conceptual clustering and their relation to numerical taxonomy. In W. Gale (Ed.), Artificial intelligence and statistics. Reading, MA: Addison Wesley.

Easterlin, J. D., & Langley, P. (1985). A framework for concept formation. Proceedings of the Seventh Conference of the Cognitive Science Society (pp. 267-271). Irvine, CA.

Fisher, D., & Langley, P. (1985). Approaches to conceptual clustering. Proceedings of the Ninth International Joint Conference on Artificial Intelligence (pp. 691-697). Los Angeles, CA: Morgan Kaufmann.

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