CSC361 Course Syllabus:
· What is meant by AI.
· Discussion of different definitions
· Discussion of Different Views
· The History of Artificial Intelligence
· The State of the Art
2. Concept of Intelligent Agents
· Agents and Environments
· Performance measures
· Task environment
· The Structure of Agents
3. Solving Problems by Searching
· Problem-Solving Agents
· Well-defined problems and solutions
· Formulating problems
· Searching for Solutions
· Uninformed Search Strategies
i. Breadth-first search
ii. Uniform-cost search
iii. Depth-first search
iv. Depth-limited search
v. Iterative deepening depth-first search
vi.
Informed Search and Exploration
i. Informed (Heuristic) Search Strategies
ii. Greedy best-first search
iii. A* search: Minimizing the total estimated solution cost
iv. Heuristic Functions
v. Local Search Algorithms and Optimization Problems
1. Hill-climbing search
2. Simulated annealing search
· Constraint Satisfaction Problems {CSPs)
i. Backtracking Search for CSPs
ii. Variable and value ordering
iii. Propagating information through constraints
1. Forward checking
2. Constraint propagation
3. Handling special constraints
iv. Local Search for Constraint Satisfaction Problems
v. The Structure of Problems
· Games
i. Optimal Decisions in Games
ii. Optimal strategies
iii. The minimax algorithm
iv. Alpha-Beta Pruning
4. Knowledge Representation and reasoning
· Basics of Knowledge Representation
· Knowledge representation Paradigms
· Knowledge bases
· Propositional Logic
· First-Order Logic
Semantic nets: Concepts, Uses, and Examples.
Frames and Scripts: Concepts, Uses, and Examples.
5. Planning
· Introduction and basic concepts
· STRIPS
· Planning with State-Space Search
· Partial-Order Planning
6. Natural Language Processing
· Introduction and basic concepts
· Language Structure
· Processing Techniques
· Tools