AI-CBR
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The Laboratory for Knowledge Discovery in Databases (KDD) is a research group in the Computing and Information Sciences (CIS) Department at Kansas State University. Its research emphasis is in the areas of applied artificial intelligence (AI) and knowledge-based software engineering (KBSE) for decision support systems.

More specifically, we are interested in machine learning, data mining and knowledge discovery from large spatial and temporal databases, human-computer intelligent interaction (HCII), and high-performance computation in learning and optimization. In our research, we look for ways to systematically decompose analytical learning problems based upon information theoretic and probabilistic criteria, so that the most appropriate machine learning methods may be applied to the resulting transformed problems.

One of the major challenges in this area is the design of unsupervised learning and bias (or hyperparameter) optimization methods to produce an effective decomposition of learning tasks. An interesting opportunity presented by this problem is that, by addressing the high-level control of inductive learning in a statistically sound fashion, we can improve our techniques for both model selection and model integration (as practiced in multimodal sensor fusion). We have developed and applied such approaches to multistrategy learning, which are potentially computation-intensive, to interesting analytical problems in the areas of decision support (uncertain reasoning) and control automation.

The goal of our work is to gain insight into the interaction between artifacts that adapt or learn - whether by Bayesian, neural, or genetic computation - and their users. Important examples of this interaction include data visualization in intelligent displays, software agents for distributed high-performance computation and information retrieval, and virtual environments for simulation and computer-assisted instruction.

• CBR Publications
• ai-cbr at the University of Auckland
• Case-Based Reasoning on the Web at the University of Kaiserslautern

Projects

(Last updated 14 Jul 2001)

Presentations

(Last updated 28 Jan 2002)

• Schmidt, C. Foundations and Aproaches to Case-Based Reasoning technical presentation to Soft Computing Laboratory (SCL) group, Kansas State University February 28, 2002
• Plummer, J. Incremental Learning technical presentation to Laboratory for Knowledge Discovery in Databases (KDD) group, Kansas State University November 16, 2001
• Schmidt, C. Constructive Induction for Knowledge Discovery: Clustering Techniques for KDD technical presentation to Laboratory for Knowledge Discovery in Databases (KDD) group, Kansas State University November 7, 2001
• Raju, M. A Parallel Algorithm for Delaunay Triangulation technical presentation to Laboratory for Knowledge Discovery in Databases (KDD) group, Kansas State University July 13, 2001
• Schmidt, C. Case-Based Reasoning and Real Time Scheduling technical presentation to Laboratory for Knowledge Discovery in Databases (KDD) group, Kansas State University March, 2001
• Schmidt, C. Instance Based Learning technical presentation to Laboratory for Knowledge Discovery in Databases (KDD) group, Kansas State University Novemember, 2000

• Journal Papers
• Conference Papers
• Technical Reports
• White Papers

Work in Progress

(Last updated 14 Jul 2001)

• Timetables / Milestones
• To-Do Lists
• Data Sets
• Software Releases
• Documentation

• [AP94] A. Aamodt, E. Plaza (1994) Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. In AICom - Artificial Intelligence Communications, IOS Press, Vol. 7: 1, pp. 39-59. pdf
• [CS99] Coello J., dos Santos R. (1999), Integrating CBR and Heuristic Search for Learning and Reusing Solutions in Real-Time Task Scheduling, Lecture Notes in AI 1650, p 89 ff. Springer-Verlag
• [LL00] Laird J., van Lent M. (2000) Human-level AI’s Killer Application: Interactive Computer Games, Proc. AAAI 2000, pp. 1171-1178. AAAI Press / The MIT Press pdf
• [Mi97] Mitchell, T. (1997) Machine Learning, MIT Press and The McGraw-Hill Companies, Inc.Boston, Mass.

Group Members and Affiliates

(Last updated 14 Jul 2001)

Faculty and Affiliates

• David Ben-Arieh
  Industrial and Manufacturing Systems Engineering
• Shing I Chang
  Industrial and Manufacturing Systems Engineering
• William H. Hsu
  Computing and Information Sciences
• Surya Ramanachandran
  AIdentity Matrix

• Haipeng Guo
• Cecil P. Schmidt