Artificial Intelligence: State Space Search & Heuristics
Master algorithmic problem solving: state-space graphs, A* search, minimax decision trees, alpha-beta pruning, and automated deduction.
Course Overview
Artificial Intelligence at its core is the study of computational agents that act rationally. This comprehensive masterclass covers problem spaces, state-space search representations, uninformed search (BFS/DFS), optimal heuristic search (A* and Hill Climbing), adversarial game-playing algorithms (Minimax and Alpha-Beta Cutoffs), and automated theorem proving via First-Order Predicate Logic.
What You'll Learn & Build
Prerequisites
- Basic Python programming skills (loops, functions, and dictionaries)
- Familiarity with discrete math concepts (sets and graphs) is helpful
Who This Is For
- Computer science students, AI engineers, and software architects building search & planning systems
Course Curriculum & Lessons
4 Modules • 12 Total Lessons • Lessons 1-4 are Free to Solve-
1. State Space Search & Production Systems
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2. Breadth-First Search (BFS) for Shortest Path
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3. Depth-First Search (DFS) & Recursion Stacks
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4. Heuristic Evaluation: Manhattan & Euclidean Metrics
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5. Hill Climbing Search & Local Maxima
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6. The A* Search Algorithm: f(n) = g(n) + h(n)
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7. Minimax Decision Trees for Two-Player Games
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8. Alpha-Beta Pruning: Branch Cutoffs in Game Trees
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9. Iterative Deepening Search (IDDFS)
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10. Propositional Logic & Truth Table Entailment
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11. First-Order Logic & Resolution Proof
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12. Rule-Based Expert Systems: Forward & Backward Chaining
Your Instructor
Stro Hafta AI Faculty Guild
Senior software architect with extensive production engineering experience. Builds clean, modern curriculum and interactive web playgrounds for developers.
