Artificial Intelligence

Syllabus

1. Module 1

   Intelligent Agents and Problem Formulation

   • Agents, Environments, and Rational Action
   • Performance Measures and Environment Types
   • State-Space Models
   • Problem Formulation and Abstraction
   • Limits of Rational Models

2. Module 2

   Uninformed and Informed Search

   • Breadth-First and Uniform-Cost Search
   • Depth-First and Iterative Deepening
   • Heuristic Functions
   • A* Search and Optimality
   • Memory-Bounded and Bidirectional Search

3. Module 3

   Constraint Satisfaction

   • Variables, Domains, and Constraints
   • Backtracking Search
   • Constraint Propagation and Arc Consistency
   • Variable and Value Ordering
   • Local Search for CSPs

4. Module 4

   Adversarial Search and Games

   • Minimax
   • Alpha–Beta Pruning
   • Evaluation Functions
   • Stochastic and Partially Observable Games
   • Monte Carlo Tree Search

5. Module 5

   Knowledge Representation and Logic

   • Propositional Knowledge Bases
   • First-Order Logic
   • Unification and Inference
   • Rule Systems and Ontologies
   • Knowledge Representation Trade-Offs

6. Module 6

   Planning

   • Classical Planning Models
   • State-Space and Plan-Space Search
   • Planning Graphs
   • Hierarchical Task Networks
   • Planning Under Uncertainty

7. Module 7

   Reasoning Under Uncertainty

   • Probabilistic Reasoning
   • Bayesian Networks
   • Exact Inference
   • Approximate Inference and Sampling
   • Temporal Models and Hidden States

8. Module 8

   Sequential Decision-Making

   • Markov Decision Processes
   • Value Iteration and Policy Iteration
   • Partially Observable MDPs
   • Reinforcement Learning Foundations
   • Exploration and Exploitation

9. Module 9

   Multi-Agent and Hybrid AI

   • Multi-Agent Interaction
   • Cooperation, Competition, and Mechanism Design
   • Combining Search, Logic, and Learning
   • Robotics and Perception Interfaces
   • Human–AI Collaboration

10. Module 10

    Responsible AI and System Evaluation

    • Robustness and Distribution Shift
    • Explainability and Interpretability
    • Bias, Fairness, and Accountability
    • Safety and Misuse
    • Evaluating AI Systems in Context