Artificial Intelligence and Soft Computing
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A01=Amit Konar
Antecedent Clauses
Arc Function
artificial intelligence machines
Artificial Neural Nets
Author_Amit Konar
Backward Reasoning
biological brain
Category=UYQ
cognitive memory model
computational neuroscience
constraint satisfaction problems
Constraint Satisfaction Techniques
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eq_computing
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eq_isMigrated=2
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Feed Forward Neural Net
Flowshop Scheduling Problem
Forward Reasoning
FPN
fuzzy reasoning methods
genetic algorithm techniques
Inductive Logic Programming
Intelligent Search Algorithms
Knowledge Acquisition
Konar Amit
Logic Program
Membership Functions
Mobile Robots
Neural Net
neural network modeling
Non-monotonic Reasoning
parallel distributed intelligent systems
Petri Net
Semantic Net
Soft Computing
soft computing tools
symbolic logic systems
Timed Petri Net
Timed Petri Net Model
Transition Tr
Vice Versa
Water Jug Problem
Product details
- ISBN 9780849313851
- Weight: 1670g
- Dimensions: 152 x 229mm
- Publication Date: 08 Dec 1999
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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With all the material available in the field of artificial intelligence (AI) and soft computing-texts, monographs, and journal articles-there remains a serious gap in the literature. Until now, there has been no comprehensive resource accessible to a broad audience yet containing a depth and breadth of information that enables the reader to fully understand and readily apply AI and soft computing concepts.
Artificial Intelligence and Soft Computing fills this gap. It presents both the traditional and the modern aspects of AI and soft computing in a clear, insightful, and highly comprehensive style. It provides an in-depth analysis of mathematical models and algorithms and demonstrates their applications in real world problems.
Beginning with the behavioral perspective of "human cognition," the text covers the tools and techniques required for its intelligent realization on machines. The author addresses the classical aspects-search, symbolic logic, planning, and machine learning-in detail and includes the latest research in these areas. He introduces the modern aspects of soft computing from first principles and discusses them in a manner that enables a beginner to grasp the subject. He also covers a number of other leading aspects of AI research, including nonmonotonic and spatio-temporal reasoning, knowledge acquisition, and much more.
Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain is unique for its diverse content, clear presentation, and overall completeness. It provides a practical, detailed introduction that will prove valuable to computer science practitioners and students as well as to researchers migrating to the subject from other disciplines.
Amit Konar (Jadavpur University, Calcutta, India)
