Machine Learning: An Artificial Intelligence Approach (Volume I), Volume 1Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs—particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems—one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS). This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers. |
Contents
| 39 | |
LEARNING IN PROBLEMSOLVING AND PLANNING | 135 |
LEARNING FROM OBSERVATION AND DISCOVERY | 241 |
LEARNING FROM INSTRUCTION | 365 |
APPLIED LEARNING SYSTEMS | 461 |
Comprehensive Bibliography of Machine Learning | 511 |
Glossary of Selected Terms In Machine Learning | 551 |
About the Authors | 557 |
| 563 | |
| 567 | |
Common terms and phrases
ADDSUB algorithm analogical analysis applied Artificial Intelligence attributes BACON4 behavior Carnegie-Mellon University Chapter clustering complex components Computer Science concept description conceptual clustering condition conjunctive constraints constructive induction criterion decision tree defined described descriptors discovery disjunction divisors domain EURISKO evaluation example experience expression Figure function given goal Hayes-Roth heuristic search heuristics hypotheses inductive assertions inductive inference inductive learning initial input interact knowledge acquisition LAMBDA language learning systems Lenat logic machine learning mal-rules match means-ends analysis methods Michalski models MULT NANOKLAUS nodes objects observed operators path pattern player predicate predicate calculus Problem Solver problem-solving procedure production properties queen of spades representation rules satisfied schema selected selectors sequence situations slot solution SOLVE FIN2 specific star statement structure taking points task theory theory’s tion training instance transformation trick values variables version space