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Books in Artificial intelligence

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521-523 of 523 results in All results

Representation and Understanding

  • 1st Edition
  • September 28, 1975
  • Jerry Bobrow
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 9 1 5 - 0
Language, Thought, and Culture: Advances in the Study of Cognition: Representation and Understanding: Studies in Cognitive Science focuses on the principles, processes, and methodologies involved in artificial intelligence. The selection first offers information on the dimensions of representation, foundations for semantic networks, and reflections on the formal description of behavior. Discussions focus on relativity of behavioral description, hierarchical organization of processes, problems in knowledge representation, and inference, access, and self-awareness. The text then takes a look at the synthesis, analysis, and contingent knowledge in specialized understanding systems, some principles of memory schemata, and representing knowledge for recognition. The book examines frame representations and declarative/procedural controversy, schema for stories, and structure of episodes in memory. Topics include long-term memory, conceptual dependency, understanding paragraphs, simple story grammar, and first attempt at synthesis. The publication then ponders on concepts for representing mundane reality in plans and multiple representations of knowledge for tutorial reasoning. The selection is highly recommended for researchers interested in exploring artificial intelligence.

Symbolic Logic and Mechanical Theorem Proving

  • 1st Edition
  • May 28, 1973
  • Chin-Liang Chang + 1 more
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 1 7 0 3 5 0 - 9
  • eBook
    9 7 8 - 0 - 0 8 - 0 9 1 7 2 8 - 3
This book contains an introduction to symbolic logic and a thorough discussion of mechanical theorem proving and its applications. The book consists of three major parts. Chapters 2 and 3 constitute an introduction to symbolic logic. Chapters 4-9 introduce several techniques in mechanical theorem proving, and Chapters 10 an 11 show how theorem proving can be applied to various areas such as question answering, problem solving, program analysis, and program synthesis.

Machine Learning

  • 1st Edition
  • January 1, 1955
  • Ryszard S. Michalski + 2 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 1 0 5 4 - 5
Machine 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.

Related subjects

Artificial intelligence expert systems and knowledge based systems

Artificial intelligence general

Cognitive science

Computer human interaction

Evolutionary computation genetic algorithms programming

Fuzzy logic

Machine learning

Natural language and computational linguistics

Neural networks

Planning and scheduling

Reasoning

Robotics and autonomous systems