Abstract is missing.
- Design Rationale Capture as Knowledge AcquisitionThomas R. Gruber, Catherine Baudin, John H. Boose, Jay Webber. 3-12
- A Domain-Independent Framework for Effective Experimentation in PlanningYolanda Gil. 13-17
- Knowledge Refinement Using a High Level, Non-Technical VocabularyEric K. Jones. 18-22
- Improving the Performance of Inconsistent Knowledge Bases via Combined Optimization MethodYong Ma, David C. Wilkins. 23-27
- The Flexibility of Speculative RefinementSusan Craw, Derek H. Sleeman. 28-32
- Generating Error Candidates for Assigning Blame in a Knowledge BaseMichael A. Weintraub, Tom Bylander. 33-37
- A Prototype Based Symbolic Concept Learning SystemMichael de la Maza. 41-45
- Combining Evidence of Deep and Surface SimilarityDouglas H. Fisher, Jungsoon P. Yoo. 46-50
- The Importance of Causal Structure and Facts in Evaluating ExplanationsMary Gick, Stan Matwin. 51-54
- Learning Words From ContextPeter M. Hastings, Steven L. Lytinen, Robert K. Lindsay. 55-59
- Modeling the Acquisition and Improvement of Motor SkkillsWayne Iba. 60-64
- Internal World Models and Supervised LearningMichael I. Jordan, David E. Rumelhart. 70-74
- Babel: A Psychologically Plausible Cross-Linguistic Model of Lexical and Syntactic AcquisitionRick Kazman. 75-79
- The Acquisition of Human Planning ExpertisePat Langley, John A. Allen. 80-84
- Adaptive Pattern-Oriented ChessRobert Levinson, Richard Snyder. 85-89
- Variability Bias and Category LearningJoel D. Martin, Dorrit Billman. 90-94
- A Constraint-Motivated Model of Lexical AcquisitionCraig S. Miller, John E. Laird. 95-99
- Computer Modelling of Acquisition Orders in Child LanguageSheldon Nicholl, David C. Wilkins. 100-104
- Simulating Stages of Human Cognitive Development With Connectionist ModelsThomas R. Shultz. 105-109
- Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search ControlKurt VanLehn, Randolph M. Jones. 110-114
- Incremental Constructive Induction: An Instance-Based ApproachDavid W. Aha. 117-121
- A Transformational Approach to Constructive InductionJames P. Callan, Paul E. Utgoff. 122-126
- Learning Variable Descriptors for Applying Heuristics Across CSP ProblemsDavid S. Day. 127-131
- Informed Pruning in Constructive InductionGeorge Drastal. 132-136
- A Hybrid Method for Feature GenerationTom Fawcett, Paul E. Utgoff. 137-141
- Abstracting Concepts with Inverse ResolutionAttilio Giordana, Lorenza Saitta, Davide Roverso. 142-146
- Opportunistic Constructive InductionGregg H. Gunsch, Larry A. Rendell. 147-152
- Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive LearningCarl Myers Kadie. 153-157
- Discovering Production Rules with Higher Order Neural NetworksAdam Kowalczyk, Herman L. Ferrá, Ken Gardiner. 158-162
- Constructive Induction on Symbolic FeaturesBing Leng, Bruce G. Buchanan. 163-167
- Constructive Induction in Theory RefinementRaymond J. Mooney, Dirk Ourston. 178-182
- Constructive Induction of M-of-N TermsPatrick M. Murphy, Michael J. Pazzani. 183-187
- Learning Concepts by Synthesizing Minimal Threshold Gate NetworksArlindo L. Oliveira, Alberto L. Sangiovanni-Vincentelli. 193-197
- On the Effect of Instance Representation on GeneralizationSharad Saxena. 198-202
- Relational Clichés: Constraining Induction During Relational LearningGlenn Silverstein, Michael J. Pazzani. 203-207
- Learning Polynomial Functions by Feature ConstructionRichard S. Sutton, Christopher J. Matheus. 208-212
- Constructive Induction in Knowledge-Based Neural NetworksGeoffrey G. Towell, Mark Craven, Jude W. Shavlik. 213-217
- Feature Construction in Structural Decision TreesLarry Watanabe, Larry A. Rendell. 218-222
- Fringe-Like Feature Construction: A Comparative Study and a Unifying SchemeDer-Shung Yang, Larry A. Rendell, Gunnar Blix. 223-227
- A Neural Network Approach to Constructive InductionDit-Yan Yeung. 228-232
- Learning in Intelligent Information RetrievalDavid D. Lewis. 235-239
- A Probabilistic Retrieval Scheme for Cluster-based Adaptive Information RetrievalJay N. Bhuyan, Vijay V. Raghavan. 240-244
- Classification Trees for Information RetrievalStuart L. Crawford, Robert M. Fung, Lee A. Appelbaum, Richard M. Tong. 245-249
- Incremental Learning in a Probalistic Information Retrieval SystemA. Goker, T. L. McCluskey. 255-259
- Query Learning Using an ANN with Adaptive ArchitectureK. L. Kwok. 260-264
- A Goal-Based Approach to Intelligent Information RetrievalAshwin Ram, Lawrence Hunter. 265-269
- Machine Learning in the Combination of Expert Opinion Approach to IRPaul Thompson. 270-274
- Predicting Actions from Induction on Past PerformanceSteven Walczak. 275-279
- Decision-Theoretic Learning in an Action SystemMatthew Brand. 283-287
- On Becoming Decreasingly Reactive: Learning to Deliberate MinimallySteve A. Chien, Melinda T. Gervasio, Gerald DeJong. 288-292
- Learning the Persistence of Actions in Reactive Control RulesHelen G. Cobb, John J. Grefenstette. 292-297
- Learning to Avoid Obstacles Through ReinforcementJosé del R. Millán, Carme Torras. 298-302
- Learning Football Evaluation for a Walking RobotGoang-Tay Hsu, Reid G. Simmons. 303-307
- The Blind Leading the Blind: Mutual Refinement of Approximate TheoriesSmadar Kedar, John L. Bresina, C. Lisa Dent. 308-312
- Learning to Select a Model in a Changing WorldMieczyslaw M. Kokar, Spyros A. Reveliotis. 313-317
- Learning from Deliberated ReactivityBruce Krulwich. 318-322
- Self-improvement Based on Reinforcement Learning, Planning and TeachingLong Ji Lin. 323-327
- Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption ArchitectureSridhar Mahadevan, Jonathan Connell. 328-332
- Variable Resolution Dynamic ProgrammingAndrew W. Moore. 333-337
- Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor ApparatusDavid R. Pierce. 338-342
- Incremental Development of Complex BehaviorsMark B. Ring. 343-347
- Transfer of Learning Across Compositions of Sequentail TasksSatinder P. Singh. 348-352
- Planning by Incremental Dynamic ProgrammingRichard S. Sutton. 353-357
- Learning a Cost-Sensitive Internal Representation for Reinforcement LearningMing Tan. 358-362
- Experiments in Non-Monotonic LearningMichael Bain. 380-384
- Learning Qualitative Models of Dynamic SystemsIvan Bratko, Stephen Muggleton, Alen Varsek. 385-388
- An Investigation of Noise-Tolerant Relational Concept Learning AlgorithmsClifford Brunk, Michael J. Pazzani. 389-393
- Integrity Constraints and Interactive Concept-LearningLuc De Raedt, Maurice Bruynooghe, Bern Martens. 394-398
- Inducing Temporal Fault Diagnostic Rules from a Qualitative ModelC. Feng. 403-406
- Learning Spatial Relations from ImagesKazuo Hiraki, John H. Gennari, Yoshinobu Yamamoto, Yuichiro Anzai. 407-411
- Using Inverse Resolution to Learn Relations from ExperimentsDavid Humme, Claude Sammut. 412-416
- Efficient Learning of Logic Programs with Non-determinant, Non-discriminating LiteralsBoonserm Kijsirikul, Masayuki Numao, Masamichi Shimura. 417-421
- Learning Search Control Rules for Planning: An Inductive ApproachChristopher Leckie, Ingrid Zukerman. 422-426
- Learning Constrained AtomsC. David Page Jr., Alan M. Frisch. 427-431
- A Knowledge-intensive Approach to Learning Relational ConceptsMichael J. Pazzani, Clifford Brunk, Glenn Silverstein. 432-436
- The Consistent Concept AxiomZhaogang Qian, Keki B. Irani. 437-441
- Determinate Literals in Inductive Logic ProgrammingJ. Ross Quinlan. 442-446
- First-Order Theory RevisionBradley L. Richards, Raymond J. Mooney. 447-451
- Constraints on Predicate InventionRüdiger Wirth, Paul O Rorke. 457-461
- Revising Relational Domain TheoriesJames Wogulis. 462-466
- Learning Stochastic Motifs from Genetic SequencesKenji Yamanishi, Akihiko Konagaya. 467-471
- Refinement of Approximate Reasoning-based Controllers by Reinforcement LearningHamid R. Berenji. 475-479
- The DUCTOR: A Theory Revision System for Propositional DomainsTimothy Cain. 485-489
- The Generality of OvergeneralityWilliam W. Cohen. 490-494
- Probabilistic Evaluating of Bias for Learning SystemsMarie desJardins. 495-499
- Incremental Refinement of Approximate Domain TheoriesRonen Feldman, Alberto Maria Segre, Moshe Koppel. 500-504
- An Enhancer for Reactive PlansDiana F. Gordon. 505-508
- A Hybrid Approach to Guaranteed Effective Control StrategiesJonathan Gratch, Gerald DeJong. 509-513
- Revision Cost for Theory RefinementRei Hamakawa. 514-518
- Revision of Reduced TheoriesXiaofeng Ling, Marco Valtorta. 519-523
- A Smallest Generalization Step StrategyClaire Nedellec. 529-533
- Improving Shared Rules in Multiple Category Domain TheoriesDirk Ourston, Raymond J. Mooney. 534-538
- Learning with Incrutable TheoriesPrasad Tadepalli. 544-548
- A Method for Multistrategy Task-Adaptive Learning Based on Plausible JustificationsGheorghe Tecuci, Ryszard S. Michalski. 549-553
- Using Background Knowledge in Concept FormationKevin Thompson, Pat Langley, Wayne Iba. 554-558
- Is it a Pocket or a Purse? Tighly Coupled Theory and Data Driven LearingEdward J. Wisniewski, Douglas L. Medin. 564-568
- Identifying Cost Effective Boundaries of OperationalityJungsoon P. Yoo, Douglas H. Fisher. 569-573
- Machine Learning in Engineering AutomationSteve A. Chien, Bradley L. Whitehall, Thomas G. Dietterich, Richard J. Doyle, Brian Falkenhainer, James Garrett, Stephen C. Y. Lu. 577-580
- Noise-Resistant ClassificationLeonid V. Belyaev, Loretta P. Falcone. 581-585
- Comparing Stochastic Planning to the Acquisition of Increasingly Permissive PlansScott Bennett, Gerald DeJong. 586-590
- Conceptual Clustering and Exploratory Data AnalysisGautam Biswas, Jerry B. Weinberg, Qian Yang, Glenn R. Koller. 591-595
- Megainduction: A Test FlightJason Catlett. 596-599
- Knowledge Compilation to Speed Up Numerical OptimizationGiuseppe Cerbone, Thomas G. Dietterich. 600-604
- Model Revision: A Theory of Incremental Model LearningAshok K. Goel. 605-609
- Learning Analytical Knowledge About VLSI-Design from ObservationJürgen Herrmann. 610-614
- Continous Conceptual Set Covering: Learning Robot Operators From ExamplesCarl Myers Kadie. 615-619
- Machine Learning for Nondestructive EvaluationPaul O Rorke, Steven Morris, Michael Amirfathi, William Bond, Daniel C. St. Clair. 620-624
- Improving Recognition Effectiveness of Noisy Texture ConceptsPeter Pachowicz, Jerzy W. Bala. 625-629
- Knowledge-Based Equation Discovery in Engineering DomainsR. Bharat Rao, Stephen C. Y. Lu, Robert E. Stepp. 630-634
- Design Integrated Learning Systems for Engineering DesignYoram Reich. 635-639
- AIMS: An Adaptive Interactive Modeling System for Supporting Engineering Decision MakingDavid K. Tcheng, Bruce L. Lambert, Stephen C. Y. Lu, Larry A. Rendell. 645-649
- Decision Tree Induction of 3-D Manufacturing FeaturesLarry Watanabe, Sudhakar Yerramareddy. 650-654
- Knowledge Acquisition Combining Analytical and Empirrcal TechniquesMario Martin, Ramon Sangüesa, Ulises Cortés. 657-661
- Scaling Reinforcement Learning Techniques via ModularityLambert E. Wixson. 3368-372