Artificial Intelligence MCQ
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1. Introduction to Artificial Intelligence |
1. Introduction to Artificial Intelligence
1. What is AI?
2. Early work in AI
3. AI and related fields
4. AI problems and Techniques
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2. Problems, Problem Spaces and Search |
2. Problems, Problem Spaces and Search 1. Defining AI problems as a State Space Search: example
2. Production Systems
3. Search and Control Strategies
4. Problem Characteristics
5. Issues in Design of Search Programs
6. Additional Problems
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3. Heuristic Search Techniques |
3. Heuristic Search Techniques
1. Generate-and-test
2. Hill Climbing
3. Best First Search
4. Problem Reduction
5. Constraint Satisfaction
6. Mean-Ends Analysis
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4. Knowledge Representation |
4. Knowledge Representation
1. Representations and Mappings
2. Approaches to Knowledge Representation
3. Knowledge representation method
4. Propositional Logic
5. Predicate logic
6. Representing Simple facts in Logic
7. Representing Instances and Isa relationships
8. Computable Functions and Predicates
9. Resolution
10. Forward and backward chaining
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5. Slot – and – Filler Structures |
5. Slot – and – Filler Structures
1.Weak Structures
2. Semantic Networks
3. Frames
4. Strong Structures
5. Conceptual Dependencies
6. Scripts
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6. Game Playing |
6. Game Playing
1. Minimax Search Procedures
2. Adding alpha-beta cutoffs
3. Uncertianty Reasoning: Basic Probabilty Axioms, Baye's Rule, Baysian Classification, Certainty Factor Theory, Dempster Shafar
Theory.
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7. Learning |
7. Learning
1. What is learning?
2. Rote Learning
3. Learning by taking advice
4. Learning in problem solving
5. Learning from examples
6. Explanation based learning
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