11.1 Key Concepts Summary
| Chapter | Core Concepts |
|---|---|
| 1: Introduction | AI definitions, types, applications |
| 2: Agents | Agent types, PEAS, rationality |
| 3: Environments | Environment properties, classifications |
| 4: Search | Search algorithms, heuristics |
| 5: CSPs | Constraint satisfaction, backtracking |
| 6: KR&R | Logic, semantic nets, reasoning |
| 7: Games | Minimax, alpha-beta pruning |
| 8: Planning | PDDL, STRIPS, HTN |
| 9: Learning | Supervised/unsupervised/RL |
| 10: NLP | Expert systems, NLP pipeline |
11.2 Mnemonics & Memory Aids
- PEAS: Performance, Environment, Actuators, Sensors
- CSP: Variables, Domains, Constraints
- FOL: ∀ (all), ∃ (exists), predicates, functions
- Minimax: MAX wants high, MIN wants low
- NLP Steps: Tokenize → Tag → Parse → Understand
11.3 Common Mistakes to Avoid
- Confusing BFS (complete) with DFS (not complete)
- Mixing up forward vs backward chaining
- Forgetting to check preconditions in planning
- Assuming all games are perfect information
- Overlooking bias-variance tradeoff in ML
11.4 Practice Questions
- Compare BFS, DFS, and UCS with examples.
- Convert English statements to FOL and vice versa.
- Solve a CSP using backtracking with forward checking.
- Apply minimax to a simple game tree.
- Design a small expert system rule base.
11.5 Exam Preparation Tips
- Focus on understanding concepts rather than memorization
- Practice drawing diagrams (game trees, semantic nets)
- Review algorithm pseudocode and complexity
- Prepare concise definitions for key terms
- Time yourself solving problems