10. AAAI 1992:
San Jose, California
William R. Swartout (Ed.):
Proceedings of the 10th National Conference on Artificial Intelligence. San Jose, CA, July 12-16, 1992.
AAAI Press / The MIT Press 1992, ISBN 0-262-51063-4 
Explanation & Tutoring
Learning
Learning:
Constructive and Linguistic
Learning:
Discovery
Learning:
Inductive
- Gilles Bisson:
Learning in FOL with a Similarity Measure.
82-87
- Vlad G. Dabija, Katsuhiko Tsujino, Shogo Nishida:
Learning to Learn Decision Trees.
88-95
- C. Lisa Dent, Jesus Boticario, John P. McDermott, Tom M. Mitchell, David Zabowski:
A Personal Learning Apprentice.
96-103
- Usama M. Fayyad, Keki B. Irani:
The Attribute Selection Problem in Decision Tree Generation.
104-110
- David Perry Greene, Stephen F. Smith:
COGIN: Symbolic Induction with Genetic Algorithms.
111-116
- Haym Hirsh:
Polynomial-Time Learning with Version Spaces.
117-122
- Randy Kerber:
ChiMerge: Discretization of Numeric Attributes.
123-128
- Kenji Kira, Larry A. Rendell:
The Feature Selection Problem: Traditional Methods and a New Algorithm.
129-134
- Philip D. Laird:
Discrete Sequence Prediction and its Applications.
135-140
- Steven W. Norton, Haym Hirsh:
Classifier Learning from Noisy Data as Probabilistic Evidence Combination.
141-146
- Cullen Schaffer:
Sparse Data and the Effect of Overfitting Avoidance in Decision Tree Induction.
147-152
- Wei-Min Shen:
Complementary Discrimination Learning with Decision Lists.
153-158
Learning:
Neural Network and Hybrid
Learning:
Robotic
Learning:
Theory
Utility and Bias
Multi-Agent Coordination
Natural Language
Natural Language:
Interpretation
Natural Language:
Parsing
- Paul S. Jacobs:
Parsing Run Amok: Relation-Driven Control for Text Analysis.
315-321
- Mark A. Jones, Jason Eisner:
A Probabilistic Parser Applied to Software Testing Documents.
322-328
- Ellen Riloff, Wendy G. Lehnert:
Classifying Texts Using Relevancy Signatures.
329-334
- Uri Zernik:
Shipping Departments vs. Shipping Pacemakers: Using Thematic Analysis to Improve Tagging Accuracy.
335-342
Perception
Planning
Problem Solving
Problem Solving:
Constraint Satisfaction
Problem Solving:
Hardness and Easiness
Problem Solving:
Real-Time
Problem Solving:
Search and Expert Systems
Representation and Reasoning
Representation and Reasoning:
Abduction and Diagnosis
Representation and Reasoning:
Action and Change
Representation and Reasoning:
Belief
Representation and Reasoning:
Case-Based
Representation and Reasoning:
Qualitative
Representation and Reasoning:
Qualitative Model Construction
Representation and Reasoning:
Temporal
Representation and Reasoning:
Terminological
Representation and Reasoning:
Tractability
Robot Navigation
Scaling Up
- Robert B. Doorenbos, Milind Tambe, Allen Newell:
Learning 10, 000 Chunks: What's It Like Out There?
830-836
- Nomi L. Harris, Lawrence Hunter, David J. States:
Mega-Classification: Discovering Motifs in Massive Datastreams.
837-842
- Hiroaki Kitano, Akihiro Shibata, Hideo Shimazu, Juichirou Kajihara, Atsumi Sato:
Building Large-Scale and Corporate-Wide Case-Based Systems: Integration of the Organizational and Machine Executable Algorithms.
843-849
- Hiroaki Kitano, Moritoshi Yasunaga:
Wafer Scale Integration for Massively Parallel Memory-Based Reasoning.
850-856
Invited Talks
- Edmund H. Durfee:
What Your Computer Really Needs to Know, You Learned in Kindergarten.
858-864
- Kristian J. Hammond:
Reasoning as Remembering: The Theory and Practice of CBR.
865
- Lawrence Hunter:
Artificial Intelligence and Molecular Biology.
866-868
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