Abstract is missing.
- Unifying Themes in Empirical and Explanation-Based LearningPat Langley. 2-4
- Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional AspectsRaymond J. Mooney, Dirk Ourston. 5-7
- Conceptual Clustering of ExplanationsJungsoon P. Yoo, Douglas H. Fisher. 8-10
- A Tight Integration of Deductive LearningGerhard Widmer. 11-13
- Multi-Strategy Learning in Nonhomongeneous Domain TheoriesGheorghe Tecuci, Yves Kodratoff. 14-16
- A Description of Preference Criterion in Constructive Learning: A Discussion of Basis IssuesJianping Zhang, Ryszard S. Michalski. 17-19
- Combining Case-Based Reasoning, Explanation-Based Learning, and Learning form InstructionMichael Redmond. 20-22
- Deduction in Top-Down Inductive LearningFrancesco Bergadano, Attilio Giordana, S. Ponsero. 23-25
- One-Sided Algorithms for Integrating Empirical and Explanation-Based LearningWendy Sarrett, Michael J. Pazzani. 26-28
- Combining Empirical and Analytical Learning with Version SpacesHaym Hirsh. 29-33
- Finding New Rules for Incomplete Theories: Explicit Biases for Induction with Contextual InformationAndrea Pohoreckyj Danyluk. 34-36
- Learning from Plausible ExplanationsTom Fawcett. 37-39
- Augmenting Domain Theory for Explanation-Based GeneralizationKamal M. Ali. 40-42
- Explanation Based Learning as Constrained SearchDavid Haines. 43-45
- Reducing Search and Learning Goal PreferencesSteven Morris. 46-48
- Adaptation-Based Explanation: Explanations as CasesAlex Kass. 49-51
- A Retrieval Model Using Feature SelectionColleen M. Seifert. 52-54
- Improving Decision-Making on the Basis of ExperienceBruce Krulwich, Gregg Collins, Lawrence Birnbaum. 55-57
- Explanation-Based Acceleration of Similarity-Based LearningMasayuki Numao, Masamichi Shimura. 58-60
- Knowledge Acquisition Planning: Results and ProspectsLawrence Hunter. 61-65
- Learning by Instruction in connectionist SystemsJoachim Diederich. 66-68
- Integrating Learning in a Neural NetworkBruce F. Katz. 69-71
- Explanation-Based Learning with Week Domain TheoriesMichael J. Pazzani. 72-74
- Using Domain Knowledge to Improve Inductive Learning Algorithms for DiagnosisGerhard Friedrich, Wolfgang Nejdl. 75-77
- A Framework for Improving Efficiency and AccuracyJames Wogulis. 78-80
- Error Correction in Constructive InductionGeorge Drastal, Regine Meunier, Stan Raatz. 81-83
- Improving Explanation-Based Indexing with Empirical LearningRalph Barletta, Randy Kerber. 84-86
- A Schema for an Integrated Learning SystemMichael Wollowski. 87-89
- Combining Explanation-Based Learning and Artificial Neural NetworksJude W. Shavlik, Geoffrey G. Towell. 90-93
- Learning Classification Rules Using BayesWray L. Buntine. 94-98
- New Empirical Learning Mechanisms Perform Significantly Better in Real Life DomainsMatjaz Gams, Aram Karalic. 99-103
- Inductive Learning with BCTPhilip K. Chan. 104-108
- What Good Are Experiments?Ritchey A. Ruff, Thomas G. Dietterich. 109-112
- An Experimental Comparison of Human and Machine Learning FormalismsStephen Muggleton, Michael Bain, Jean Hayes Michie, Donald Michie. 113-118
- Two Algorithms That Learn DNF by Discovering Relevant FeaturesGiulia Pagallo, David Haussler. 119-123
- Limitations on Inductive LearningThomas G. Dietterich. 124-128
- The Induction of Probabilistic Rule Sets - The Itrule AlgorithmRodney M. Goodman, Padhraic Smyth. 129-132
- Empirical Substructure DiscoveryLawrence B. Holder. 133-136
- Learning the Behavior of Dynamical Systems form ExamplesJan Paredis. 137-140
- Experiments in Robot LearningMatthew T. Mason, Alan D. Christiansen, Tom M. Mitchell. 141-145
- Induction of Decision Trees from Inconclusive DataW. Scott Spangler, Usama M. Fayyad, Ramasamy Uthurusamy. 146-150
- Knowledge Intensive InductionMichel Manago. 151-155
- An Ounce of Knowledge is Worth a Ton of Data: Quantitative studies of the Trade-Off between Expertise and Data Based On Statistically Well-Founded Empirical InductionBrian R. Gaines. 156-159
- Signal Detection Theory: Valuable Tools for Evaluating Inductive LearningKent A. Spackman. 160-163
- Unknown Attribute Values in InductionJ. Ross Quinlan. 164-168
- Processing Issues in Comparisons of Symbolic and Connectionist Learning SystemsDouglas H. Fisher, Kathleen B. McKusick, Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell. 169-173
- Bacon, Data Analysis and Artificial IntelligenceCullen Schaffer. 174-179
- Learning to Plan in Complex DomainsDavid Rudy, Dennis F. Kibler. 180-182
- An Empirical Analysis of EBL Approaches for Learning Plan SchemataJude W. Shavlik. 183-187
- Learning Decision Rules for scheduling Problems: A Classifier Hybrid ApproachMike R. Hilliard, Gunar E. Liepins, G. Rangarajan, Mark Palmer. 188-190
- Learning Tactical Plans for Pilot AidingKeith R. Levi, David L. Perschbacher, Valerie L. Shalin. 191-193
- Issues in the Justification-Based Diagnosis of Planning FailuresLawrence Birnbaum, Gregg Collins, Bruce Krulwich. 194-196
- Learning Procedural Knowledge in the EBG ContextStan Matwin, Johanne Morin. 197-199
- Learning Invariants from ExplanationsJean-Francois Puget. 200-204
- Using Learning to Recover Side-Effects of Operators in RoboticsRalph P. Sobek, Jean-Paul Laumond. 205-208
- Learning to Recognize Plans Involving AffectPaul O Rorke, Timothy Cain, Andrew Ortony. 209-211
- Learning to Retrieve Useful Information for Problem SolvingRandolph M. Jones. 212-214
- Discovering Problem Solving Strategies: What Humans Do and Machines Don t (Yet)Kurt VanLehn. 215-217
- Approximating Learned Search Control KnowledgeMelissa P. Chase, Monte Zweben, Richard L. Piazza, John D. Burger, Paul P. Maglio, Haym Hirsh. 218-220
- Planning Approximate Plans for Use in the Real WorldPrasad Tadepalli. 224-228
- Using Concept Hierarchies to Organize Plan KnowledgeJohn A. Allen, Pat Langley. 229-231
- Conceptual Clustering of Mean-Ends PlansHua Yang, Douglas H. Fisher. 232-234
- Learning Appropriate Abstractions for Planning in Formation ProblemsNicholas S. Flann. 235-239
- Discovering Admissible Search Heuristics by Abstracting and OptimizingJack Mostow, Armand Prieditis. 240-240
- Learning Hierarchies of Abstraction SpacesCraig A. Knoblock. 241-245
- Learning from OpportunityTimothy M. Converse, Kristian J. Hammond, Mitchell Marks. 246-248
- Learning by Analyzing Fortuitous OccurrencesSteve A. Chien. 249-251
- Explanation-Based Learning of Reactive OperationsMelinda T. Gervasio, Gerald DeJong. 252-254
- On Becoming ReactiveJim Blythe, Tom M. Mitchell. 255-259
- Knowledge Base Refinement and Theory RevisionAllen Ginsberg. 260-265
- Theory Formation by Abduction: Initial Results of a Case Study Based on the Chemical RevolutionPaul O Rorke, Steven Morris, David Schulenburg. 266-271
- Using Domain Knowledge to Aid Scientific Theory RevisionDonald Rose. 272-277
- The Role of Experimentation in Scientific Theory RevisionDeepak Kulkarni, Herbert A. Simon. 278-283
- Exemplar-Based Theory Rejection: An Approach to the Experience Consistency ProblemShankar A. Rajamoney. 284-289
- Controlling Search for the Consequences of New Information During Knowledge IntegrationKenneth S. Murray, Bruce W. Porter. 290-295
- Identifying Knowledge Base Deficiencies by Observing User BehaviorKeith R. Levi, Valerie L. Shalin, David L. Perschbacher. 296-301
- Toward Automated Rational Reconstruction: A Case StudyChris Tong, Phil Franklin. 302-307
- Discovering Mathematical Operation DefinitionsMichael H. Sims, John L. Bresina. 308-313
- Imprecise Concept Learning within a Growing LanguageZbigniew W. Ras, Maria Zemankova. 314-319
- Using Determinations in EBL: A Solution to the incomplete Theory ProblemSridhar Mahadevan. 320-325
- Some Results on the Complexity of Knowledge-Based RefinementMarco Valtorta. 326-331
- Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain TheoryDavid C. Wilkins, Kok-Wah Tan. 332-339
- Incremental Learning of Control Strategies with Genetic algorithmsJohn J. Grefenstette. 340-344
- Tower of Hanoi with Connectionist Networks: Learning New FeaturesCharles W. Anderson. 345-349
- A Formal Framework for Learning in Embedded SystemsLeslie Pack Kaelbling. 350-353
- A Role for Anticipation in Reactive Systems that LearnSteven D. Whitehead, Dana H. Ballard. 354-357
- Uncertainty Based Selection of Learning ExperiencesPaul D. Scott, Shaul Markovitch. 358-361
- Improved Training Via Incremental LearningPaul E. Utgoff. 362-365
- Incremental Batch LearningScott H. Clearwater, Tze-Pin Chen, Haym Hirsh, Bruce G. Buchanan. 366-370
- Incremental Concept Formation with Composite ObjectsKevin Thompson, Pat Langley. 371-374
- Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic AlgorithmsRich Caruana, J. David Schaffer, Larry J. Eshelman. 375-378
- Focused Concept FormationJohn H. Gennari. 379-382
- An Exploration Into Incremental Learning: the INFLUENCE SystemAntoine Cornuéjols. 383-386
- Incremental, Instance-Based Learning of Independent and Graded Concept DescriptionsDavid W. Aha. 387-391
- Cost-Sensitive Concept Learning of Sensor Use in Approach ad RecognitionMing Tan, Jeffrey C. Schlimmer. 392-395
- Reducing Redundant LearningJoel D. Martin. 396-399
- Incremental Clustering by Minimizing Representation LengthJakub Segen. 400-403
- Information Filters and Their Implementation in the SYLLOG SystemShaul Markovitch, Paul D. Scott. 404-407
- Adaptive Learning of Decision-Theoretic Search Control KnowledgeEric Wefald, Stuart J. Russell. 408-411
- Atoms of Learning II: Adaptive Strategies A Study of Two-Person Zero-Sum CompetitionOliver G. Selfridge. 412-415
- An Incremental Genetic Algorithm for Real-Time LearningTerence C. Fogarty. 416-419
- Participatory Learning: A Constructivist ModelRonald R. Yager, Kenneth M. Ford. 420-425
- Representational Issues in Machine LearningDevika Subramanian. 426-429
- Labor Saving New DistinctionsJohn Woodfill. 430-433
- A Theory of Justified ReformulationsDevika Subramanian. 434-438
- Reformation from State Space to Reduction SpacePatricia J. Riddle. 439-440
- Knowledge-Based Feature GenerationJames P. Callan. 441-443
- Enriching Vocabularies by Generalizing Explanation StructuresRichard Maclin, Jude W. Shavlik. 444-446
- Higher-Order and Modal Logic as a Framework for Explanation-Based GeneralizationScott Dietzen, Frank Pfenning. 447-449
- Towards a Formal Analysis of EBLRussell Greiner. 450-453
- A Mathematical Framework for Studying RepresentationRobert C. Holte, Robert M. Zimmer. 454-456
- Refining Representations to Improve Problem Solving QualityJeffrey C. Schlimmer. 457-460
- Comparing Systems and analyzing Functions to Improve Constructive InductionLarry A. Rendell. 461-464
- Evaluating alternative Instance RepresentationsSharad Saxena. 465-468
- Evaluating Bias During Pac-LearningLonnie Chrisman. 469-471
- Constructive Induction FrameworkPankaj Mehra. 474-475
- Constructive Induction by AnalogyLuc De Raedt, Maurice Bruynooghe. 476-477
- Concept Discovery Through Utilization of Invariance Embedded in the Description LanguageMieczyslaw M. Kokar. 478-479
- Declarative Bias for Structural DomainsBenjamin N. Grosof, Stuart J. Russell. 480-482
- Compiling Learning Vocabulary from a Performance System DescriptionRichard M. Keller. 482-495
- Automatic Construction of a Hierarchical Generate-and-Test AlgorithmSunil Mohan, Chris Tong. 483-484
- A Knowledge-Level Analysis of InformingJane Yung-jen Hsu. 485-488
- An Object-Oriented Representation for Search algorithmsJack Mostow. 489-491
- Generalized Recursive Splitting Algorithms for Learning Hybrid ConceptsBruce L. Lambert, David K. Tcheng, Stephen C. Y. Lu. 496-498
- Screening Hypotheses with Explicit BiasDiana F. Gordon. 499-500
- Building A Learning Bias from Perceived DependenciesChristian de Sainte Marie. 501-502
- A Bootstrapping Approach to Concept ClusteringKatharina Morik, Jörg-Uwe Kietz. 503-504
- Overcoming Feature Space Bias in a Reactive EnvironmentHans Tallis. 505-508