Artificial Intelligence : AI 14.0

This comprehensive resource on Artificial Intelligence offers a detailed overview suitable for both students and professionals. Designed as a portable engineering reference, it covers a wide spectrum of topics with illustrative figures and tables to enhance understanding.

The book delves into over 600 subjects across Artificial Intelligence, Automata, Real-time Systems, and Neuro-Fuzzy Systems, organized meticulously into five units to facilitate structured learning. It provides an in-depth exploration of the field, which focuses on developing computer and software systems capable of exhibiting intelligent behavior.

This app serves as a practical handbook, presenting approximately 142 key topics within AI in a clear and accessible manner. The interdisciplinary nature of AI is highlighted, encompassing fields such as computer science, mathematics, psychology, linguistics, philosophy, neuroscience, and specialized areas like artificial psychology.

Among the topics addressed are:

• Turing Test
• Introduction to Artificial Intelligence
• Historical Development of AI
• The AI Cycle
• Knowledge Representation
• Typical AI Problems and Limitations
• Introduction to Intelligent Agents and their Performance
• Agent Architectures and Types
• Goal-Based and Utility-Based Agents
• Environment-Agent Interactions
• Search Algorithms and Problem-Solving Techniques
• State Spaces, Graph Search, and Generic Searching Algorithms
• Informed and Uninformed Search Strategies including Breadth-First, Depth-First, and Heuristic Search
• Mathematical Foundations of Inductive Learning and Search Trees
• Advanced Search Methods such as Iterative Deepening, Greedy Search, and A* Algorithm
• Game Playing and Adversarial Search including Minimax and Alpha-Beta Pruning
• Logical Reasoning: Propositional and First-Order Logic, Resolution, and Knowledge Representation Formalisms such as Semantic Networks and Frames
• Reasoning Techniques including Inference Rules, Deductive Systems, and Non-Monotonic Reasoning
• Knowledge-Based Systems: Rule-Based Inference, Prolog Programming with Forward and Backward Chaining
• Probabilistic Models: Hidden Markov Models and Bayesian Networks
• Machine Learning Fundamentals: Supervised Learning, Decision Trees, and Learning Formalisms
• Semantic Web Concepts: Formalisms for Knowledge Representation and Inference in Semantic Networks and Frames
• Planning Methods: Logic-Based Planning, Search-Based Planning, Partial-Order Planning, and Plan-Space Algorithms
• Probabilistic Reasoning: Bayesian Networks and Uncertainty Handling
• An Introduction to Learning Systems: Taxonomies and Key Concepts

If you find this material helpful but wish to provide feedback or have queries, please email us rather than leaving a low rating. Your feedback on ratings and suggestions is highly valued and will guide future updates. We are committed to addressing your concerns promptly.