Introduction to Artificial Intelligence (CS-487)

Fall 23-24, 24-25 (Final Year Undergraduate Course)

Textbook

  • Russell, S. J., & Norvig, P. (2022). Artificial Intelligence: A Modern Approach 4th Edition. Pearson Education.

Syllabus

  1. Introduction to AI
    • Foundations of AI, History of AI, State of the Art, Risks and Benefits
  2. Intelligent Agents
    • Agents and Environments, Rationality, Agent Structures
  3. Solving Problems by Searching
    • Problem-Solving Agents, State-space search, problem formulation, Search Algorithms: Uninformed (DFS, BFS, uniform-cost) and informed (\(A^*\), greedy search) search strategies, Heuristic Functions
  4. Local Search and Optimization
    • Local Search Algorithms (hill-climbing, simulated annealing, genetic algorithms), Continuous Space Optimization, Partially Observable Environments, Online Search Agents
  5. Constraint Satisfaction Problems (CSPs)
    • Constraint Propagation: Inference techniques, Backtracking and Local Search
  6. Adversarial Search
    • Minimax and alpha-beta pruning, Monte Carlo Tree Search, Stochastic and Partially Observable Games
  7. Logical Agents
    • Knowledge-Based Agents, Propositional Logic for reasoning, Theorem Proving, Model Checking
  8. First-Order Logic (FOL)
    • Syntax and Semantics of FOL, Knowledge Engineering, Inference in FOL (unification, forward/backward chaining, resolution rule)
  9. Knowledge Representation
    • Ontological Engineering, Categories and Objects, Reasoning Systems
  10. AI Planning
    • Classical Planning, Planning Heuristics, Hierarchical Planning, Nondeterministic Planning, Resource Planning

Prerequisites

  • CS-240 (Data Structures)
  • CS-180 (Logic)

Course material is available on Moodle (e-learn platform).