Case-Based Reasoning System for Bearing Design A thesis Submitted to the Faculty of Drexel University by Xiaoli Qin in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Xiaoli Qin Email: xq22@drexel.edu Webpage: http://httpsrv.ocs.drexel.edu/grad/xq22/student.html Thesis Advisor/Committee Chair: Dr. Regli, MCS Committee Members: Dr. Herrmann, MCS; Dr. Aktan, DIII Dr. Greenwald, MCS; Dr. Tsikos, DIII

Case-Based Reasoning System for Bearing Design Objective of Research Overview Case-Based Reasoning Theory Case-Based Reasoning System for Bearing Design Problem Statements Knowledge Representation Issue Case-Based Reasoning Engine (Intelligent Reasoner) Demo Conclusions and Future Works

Objective of Researh Develop a System which use Case-Based Reasoning techniques to solve design problems. Develop Knowledge Representations and Case Memory Organizations for this system Develop a Reasoning Sub-System to adapt retrieved cases automatically. Develop a mechanism to achieve system Learning.

Case-Based Reasoning Background What is Case-Based Reasoning(CBR) ? Case-Based Reasoning is reasoning by remembering. (Cognitive Model, Psychology, Simulate human reasoning methods) A Case-Based Reasoner solves new problems by adapting solutions that were used to solve old problems. (System Implementation, Computational/Process Model) Case-Based Reasoning is a recent approach to problem solving and learning. (Computational /Process Model)

Case-Based Reasoning (Background) A methodology to model human reasoning and thinking. A methodology for building intelligent computer system. “I have but one lamp by which my feet are guided, and that is the lamp of experience. I know no way of judging the future but by past.” Patrick Henry (Speech in Virginia Convention, Richmond. March 23, 1775)

CBR versus Rule-Based Systems Knowledge space for engineering design problem is incomplete and dynamic ! Rule-Based System is suitable for situations like: Knowledge is incomplete, uncertain (Difficult to abstract rules, Knowledge Acquisition Bottleneck) Rules space is large (infinite) (rule spaghetti, hard to maintain) Case-Based System is good at above situations.

History of Case-Based Reasoning (Background) 1977-1988, Yale University, Roger Schank Focus: Cognitive Science Theory: Memory Organization Packet (MOP) Product: JUDGE, SWALE, CHEF 1990-1992, U. of Mass., Edwina Rissland Focus: CBR in Law Product: HYPO and CABARET 1990-now, CMU, Jaime Carbonell Focus: Analogy/CBR Case-Based planing using analogy

History of Case-Based Reasoning (Background) 1988-1991, U. of Kaiserslautern, Germany, Michael M. Richter Focus: CBR for Expert system, Technical Diagnosis (Mechanical, Building, Bridge) 1990-now, Enric Plaza, Spain Focus: Medical Diagnosis 1991-now, U. of Trondheim, Norway Focus: Integration of Cases and general knowledge