The omnipresence of case-based reasoning in science and application

A surprisingly large number of research disciplines have contributed towards the development of knowledge on lazy problem solving, which is characterized by its storage of ground cases and its demand-driven response to queries. Case-based reasoning (CBR) is an alternative, increasingly popular approach for designing expert systems that implements this approach. This paper lists pointers to some contributions in some related disciplines that offer insights for CBR research. We then outline a small number of Navy applications based on this approach that demonstrate its breadth of applicability. Finally, we list a few successful and failed attempts to apply CBR, and list some predictions on the future roles of CBR in applications.

[1]  Ming Tan,et al.  Two Case Studies in Cost-Sensitive Concept Acquisition , 1990, AAAI.

[2]  Christopher K. Riesbeck,et al.  Inside Case-Based Reasoning , 1989 .

[3]  Karl Branting,et al.  Automated acquisition of user preferences , 1997, Int. J. Hum. Comput. Stud..

[4]  David W. Aha,et al.  Comparing Instance-Averaging with Instance-Saving Learning Algorithms , 1990 .

[5]  Hiroaki Kitano,et al.  Building Large-Scale and Corporate-Wide Case-Based Systems: Integration of the Organizational and Machine Executable Algorithms , 1992, AAAI.

[6]  Peter E. Hart,et al.  The condensed nearest neighbor rule (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[7]  Andrés Gómez de Silva Garza,et al.  Case-Based Reasoning in Design , 1995, IEEE Expert.

[8]  Lawrence Davis,et al.  A Hybrid Genetic Algorithm for Classification , 1991, IJCAI.

[9]  Jerome H. Friedman,et al.  Flexible Metric Nearest Neighbor Classification , 1994 .

[10]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[11]  David W. Aha,et al.  Learning Representative Exemplars of Concepts: An Initial Case Study , 1987 .

[12]  Frederick W. Jansen Issues and applications of case-based reasoning in design, edited by Mary Lou Maher and Pearl Pu : Lawrence Erlbaum Associates, Inc., New Jersey, 1997, 342 pp, ISBN 0-8058-2313-1, $39.95 , 1998, Comput. Aided Des..

[13]  Gerhard Widmer Combining Knowledge-Based and Instance-Based Learning to Exploit Qualitative Knowledge , 1993, Informatica.

[14]  Douglas L. Medin,et al.  Context theory of classification learning. , 1978 .

[15]  Pat Langley,et al.  Average-Case Analysis of a Nearest Neighbor Algorithm , 1993, IJCAI.

[16]  Craig Stanfill Memory-based Reasoning Applied to English Pronunciation , 1987, AAAI.

[17]  Terry Elliott,et al.  Instance-Based and Generalization-Based Learning Procedures Applied To Solving Integration Problems. , 1991 .

[18]  Sholom M. Weiss,et al.  Using Case Data to Improve on Rule-based Function Approximation , 1995, ICCBR.

[19]  Henry Tirri,et al.  A Bayesian Framework for Case-Based Reasoning , 1996, EWCBR.

[20]  Andrew McCallum,et al.  Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State , 1995, ICML.

[21]  David Leake,et al.  Case-Based Reasoning: Experiences, Lessons and Future Directions , 1996 .

[22]  Marc Goodman,et al.  Results on Controlling Action with Projective Visualization , 1994, AAAI.

[23]  David W. Aha,et al.  Generalizing from Case studies: A Case Study , 1992, ML.

[24]  R. Nosofsky Attention, similarity, and the identification-categorization relationship. , 1986 .

[25]  G. Gates,et al.  The reduced nearest neighbor rule (Corresp.) , 1972, IEEE Trans. Inf. Theory.

[26]  Tony R. Martinez,et al.  Instance Pruning Techniques , 1997, ICML.

[27]  R. Nosofsky Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.

[28]  George S. Sebestyen,et al.  Decision-making processes in pattern recognition , 1962 .

[29]  David B. Skalak,et al.  Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.

[30]  Peter D. Turney Theoretical analyses of cross-validation error and voting in instance-based learning , 1994, J. Exp. Theor. Artif. Intell..

[31]  Gary L. Bradshaw Learning by disjunctive spanning , 1986 .

[32]  Robert Tibshirani,et al.  Discriminant Adaptive Nearest Neighbor Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Anthony D. Griffiths Inductive generalisation in case-based reasoning systems , 1996 .

[34]  Claire Cardie,et al.  Improving Minority Class Prediction Using Case-Specific Feature Weights , 1997, ICML.

[35]  David W. Aha,et al.  Comparing Instance-Averaging with Instance-Filtering Learning Algorithms , 1988, EWSL.

[36]  Terry J. Wagner Convergence of the edited nearest neighbor (Corresp.) , 1973, IEEE Trans. Inf. Theory.

[37]  Ron Kohavi,et al.  The Utility of Feature Weighting in Nearest-Neighbor Algorithms , 1997 .

[38]  Belur V. Dasarathy,et al.  Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Claire Cardie,et al.  Using Decision Trees to Improve Case-Based Learning , 1993, ICML.

[40]  Jaime G. Carbonell,et al.  Machine learning: a guide to current research , 1986 .

[41]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[42]  Roger C. Schank,et al.  Inside case-based explanation , 1994, Artificial intelligence series.

[43]  J. Ross Quinlan,et al.  Combining Instance-Based and Model-Based Learning , 1993, ICML.

[44]  David W. AhaNavy Cloud Classiication Using Error-correcting Output Codes , 1996 .

[45]  David W. Aha,et al.  Improvement to a Neural Network Cloud Classifier , 1996 .

[46]  L. Barsalou On the indistinguishability of exemplar memory and abstraction in category representation , 1990 .

[47]  Paul E. Utgoff,et al.  Learning to control a dynamic physical system , 1987, Comput. Intell..

[48]  Gary L. Bradshaw,et al.  Learning about speech sounds: The NEXUS Project , 1987 .

[49]  David W. Aha,et al.  NaCoDAE: Navy Conversational Decision Aids Environment , 1998 .

[50]  J. L. Hodges,et al.  Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties , 1989 .

[51]  Jack Koplowitz,et al.  On the relation of performance to editing in nearest neighbor rules , 1981, Pattern Recognit..

[52]  Luc Lamontagne,et al.  Case-Based Reasoning Research and Development , 1997, Lecture Notes in Computer Science.

[53]  Ming Tan,et al.  Cost-Sensitive Concept Learning of Sensor Use in Approach ad Recognition , 1989, ML.

[54]  Andrew W. Moore,et al.  Multiresolution Instance-Based Learning , 1995, IJCAI.

[55]  David W. Aha,et al.  Noise-Tolerant Instance-Based Learning Algorithms , 1989, IJCAI.

[56]  Andrew McCallum,et al.  Instance-Based Utile Distinctions for Reinforcement Learning , 1995 .

[57]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[58]  Kenneth D. Forbus,et al.  MAC/FAC: A Model of Similarity-Based Retrieval , 1995, Cogn. Sci..

[59]  David W. Aha,et al.  Error-Correcting Output Codes for Local Learners , 1998, ECML.

[60]  S. Mann Intellectual Capital: The New Wealth of Organizations , 1999 .

[61]  Jerome M. Kurtzberg,et al.  Feature Analysis for Symbol Recognition by Elastic Matching , 1987, IBM J. Res. Dev..

[62]  David B. Skalak,et al.  Prototype Selection for Composite Nearest Neighbor Classifiers , 1995 .

[63]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[64]  C. G. Hilborn,et al.  The Condensed Nearest Neighbor Rule , 1967 .

[65]  Peter Clark,et al.  Lazy Partial Evaluation: An Integration of Explanation-Based Generalization and Partial Evaluation , 1992, ML.

[66]  W. Bruce Croft,et al.  Searching distributed collections with inference networks , 1995, SIGIR '95.

[67]  Angi Voß,et al.  Reasoning with complex cases , 1997 .

[68]  D. L. Hintzman,et al.  Differential forgetting of prototypes and old instances: Simulation by an exemplar-based classification model , 1980, Memory & cognition.

[69]  David W. Aha,et al.  A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner , 1998 .

[70]  Stephan Rudolph,et al.  On The Foundations And Applications Of SimilarityTheory To Case-Based Reasoning , 1997 .

[71]  Lundy Lewis Managing Computer Networks: A Case-Based Reasoning Approach , 1995 .

[72]  Dietrich Wettschereck,et al.  Relational Instance-Based Learning , 1996, ICML.

[73]  Agnar Aamodt,et al.  Case-Based Reasoning Research and Development , 1995, Lecture Notes in Computer Science.

[74]  Ron Kohavi,et al.  Lazy Decision Trees , 1996, AAAI/IAAI, Vol. 1.

[75]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[76]  Andrew W. Moore,et al.  Acquisition of Dynamic Control Knowledge for a Robotic Manipulator , 1990, ML.

[77]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[78]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[79]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[80]  ItalyDavid W. AhaNavy Extending Local Learners with Error-correcting Output Codes Extending Local Learners with Error-correcting Output Codes , 1997 .

[81]  Yoram Biberman,et al.  A Context Similarity Measure , 1994, ECML.

[82]  Richard L. Bankert,et al.  Cloud Classification of AVHRR Imagery in Maritime Regions Using a Probabilistic Neural Network , 1994 .

[83]  Peter G. Underwood Issues and applications of case‐based reasoning in design , 1998 .

[84]  Pedro M. Domingos Rule Induction and Instance-Based Learning: A Unified Approach , 1995, IJCAI.

[85]  Nobuhiro Yugami,et al.  Theoretical Analysis of Case Retrieval Method Based on Neighborhood of a New Problem , 1997, ICCBR.

[86]  J. Kruschke,et al.  ALCOVE: an exemplar-based connectionist model of category learning. , 1992, Psychological review.

[87]  ci UniversityTR Voting over Multiple Condensed Nearest Neighbors , 1995 .

[88]  G. Gates The Reduced Nearest Neighbor Rule , 1998 .

[89]  Changhwan Lee,et al.  An Instance-Based Learning Method for Database: An Information Theoretic Approach , 1994, ECML.

[90]  David Leake,et al.  Case-based reasoning research and development : Second International Conference on Case-Based Reasoning, ICCBR-97, Providence, RI, USA, July 25-27, 1997 : proceedings , 1997 .

[91]  Claire Cardie,et al.  Examining Locally Varying Weights for Nearest Neighbor Algorithms , 1997, ICCBR.

[92]  David Heckerman,et al.  Decision-theoretic case-based reasoning , 1994, IEEE Trans. Syst. Man Cybern. Part A.

[93]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[94]  Roger C. Schank,et al.  SCRIPTS, PLANS, GOALS, AND UNDERSTANDING , 1988 .

[95]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[96]  Prasad Tadepalli,et al.  Lazy ExplanationBased Learning: A Solution to the Intractable Theory Problem , 1989, IJCAI.

[97]  Jianping Zhang,et al.  Selecting Typical Instances in Instance-Based Learning , 1992, ML.

[98]  Peter D. Turney Exploiting Context When Learning to Classify , 1993, ECML.

[99]  David W. Aha,et al.  Lazy Learning , 1997, Springer Netherlands.

[100]  Tony R. Martinez,et al.  Instance-Based Learning with Genetically Derived Attribute Weights , 1996 .

[101]  Rodney A. Brooks,et al.  Learning to Coordinate Behaviors , 1990, AAAI.

[102]  HastieTrevor,et al.  Discriminant Adaptive Nearest Neighbor Classification , 1996 .

[103]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[104]  Jean-Gabriel Ganascia,et al.  Integrating Case Based Reasoning and Tabu Search for Solving Optimisation Problems , 1995, ICCBR.

[105]  John G. Cleary,et al.  K*: An Instance-based Learner Using and Entropic Distance Measure , 1995, ICML.

[106]  David W. Aha,et al.  Analyses of Instance-Based Learning Algorithms , 1991, AAAI.

[107]  Jean Voisin,et al.  An application of the multiedit-condensing technique to the reference selection problem in a print recognition system , 1987, Pattern Recognit..

[108]  Francesco Ricci,et al.  Learning a Local Similarity Metric for Case-Based Reasoning , 1995, ICCBR.

[109]  David W. Aha,et al.  Refining Conversational Case Libraries , 1997, ICCBR.