Advanced Artificial IntelligenceArtificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavior.Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel, reflects the research updates in this field, and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel. |
Contents
| 1 | |
| 30 | |
| 107 | |
Chapter 4 Qualitative Reasoning | 147 |
Chapter 5 CaseBased Reasoning | 171 |
Chapter 6 Probabilistic Reasoning | 214 |
Chapter 7 Inductive Learning | 260 |
Chapter 8 Support Vector Machine | 309 |
Chapter 10 Reinforcement Learning | 362 |
Chapter 11 Rough Set | 387 |
Chapter 12 Association Rules | 430 |
Chapter 13 Evolutionary Computation | 467 |
Chapter 14 Distributed Intelligence | 499 |
Chapter 15 Artificial Life | 553 |
References | 585 |
Chapter 9 ExplanationBased Learning | 328 |
Other editions - View all
Common terms and phrases
according action application approach artificial intelligence association rules backtracking basic Bayesian network behavior called cellular automata classifier complex concept description consistent constraint propagation construct data mining database decision rule decision table decision tree default theory defined Definition delete denoted describe description logic descriptor distribution domain theory dynamic environment equation explanation structure expression formula function genetic algorithm global includes inconsistent inductive inference input itemsets knowledge base knowledge representation language learning algorithm logic machine learning method mobile agent multi-agent multi-agent system neural network node nonmonotonic object operation operationality criterion parameters partition predicate prior probability problem solving Prolog proposed qualitative reasoning reduce reinforcement learning relation represent result retrieval rough set theory sample satisfy semantic similar simulation SLD resolution solution space strategy subset support count symbol theorem training examples variable VC dimension vector