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Exploration of adversarial search techniques, including the Minimax algorithm and Alpha-Beta pruning, which form the basis of strategic gaming engines. 2. Knowledge Representation and Logic
The book is designed to bridge the gap between theoretical AI concepts and the practical implementation of intelligent systems. Unlike some texts that focus solely on abstract theory or highly specific, advanced algorithms, Padhy’s work provides a balanced and thorough introduction, making complex topics accessible to learners. Unlike some texts that focus solely on abstract
Moving away from binary (true/false) logic to degrees of truth, exploring fuzzy sets, membership functions, and fuzzification/de-fuzzification processes.
1. Key Topics Covered in N.P. Padhy's "Artificial Intelligence & Intelligent Systems" Key Topics Covered in N
: The text introduces fuzzy membership functions, linguistic variables, and fuzzy relations, contrasting them rigorously against classical crisp sets.
: Implementing conditional "If-Then" workflows to construct early-stage automation patterns. 3. Expert Systems and Soft Computing and fuzzy relations
: Introduces biological brain models, backpropagation algorithms, and the architecture of early deep learning layers.