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Foundations of Artificial Intelligence: Intelligent Agents, Problem Solving, Search Algorithms, and Constraint Satisfaction
Paperback
Series: Artificial Intelligence, Book 1
Publisher: Independently Published
Published: Jul 19 2026
Pages: 172
Weight: 0.91
Height: 0.37 Width: 8.50 Depth: 11.00
Language: English
The journey to intelligent systems begins with a strong foundation.
Artificial Intelligence (AI) is transforming industries, revolutionizing decision-making, and redefining the future of technology. Whether you're an engineering student, an educator, or an aspiring AI professional, understanding the core principles of AI is essential before exploring advanced topics such as Machine Learning, Deep Learning, and Generative AI.
Foundations of Artificial Intelligence is the first volume in the Artificial Intelligence Series, designed to provide a structured and comprehensive introduction to the fundamental concepts of AI. The book emphasizes conceptual clarity through intuitive explanations, real-world examples, worked illustrations, and step-by-step algorithm analysis.
Rather than presenting algorithms as isolated topics, this book explains why they are needed, how they work, where they are applied, and how to implement them, making it an ideal companion for both classroom learning and self-study.
What You'll LearnIntroduction to Artificial Intelligence
AI Problems and Intelligent Agents
Agents and Environments
Problem Formulation and Problem Spaces
Problem-Solving Agents
Uninformed Search Algorithms
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- Iterative Deepening Search
Heuristic Search Techniques
- Hill Climbing
- Generic Best-First Search
- Greedy Best-First Search
- A* Search Algorithm
Constraint Satisfaction Problems
- Backtracking Search
- Local Search
- Concept-driven learning with intuitive explanations
- Step-by-step algorithm walkthroughs
- Flowcharts and illustrative diagrams
- Worked examples with detailed solutions
- Pseudocode for every major algorithm
- C programming implementations
- Time and space complexity analysis
- Practical applications and real-world examples
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