Course Objectives

This course aims to:

• Introduce the fundamental concepts and techniques of Artificial Intelligence (AI).
• Provide an understanding of various AI problem-solving approaches, including search algorithms, knowledge representation, and reasoning.
• Explore applications of AI in different domains.
• Discuss the ethical and societal implications of AI.

Course Outcomes

Upon completion of this course, students will be able to:

1. Define AI and understand its different approaches.
2. Apply search algorithms to solve problems in AI.
3. Represent knowledge using various techniques.
4. Implement reasoning algorithms to draw inferences from knowledge.
5. Understand and apply AI techniques in various domains.
6. Analyze the ethical and societal implications of AI.

Detailed Syllabus

Module 1: Introduction

• Introduction to Artificial Intelligence
• History and Applications
• Intelligent Agents

Module 2: Problem Solving

• Problem-Solving Agents
• Uninformed Search Strategies: Breadth-first search, Depth-first search, Iterative deepening search, etc.
• Informed (Heuristic) Search Strategies: Best-first search, A* search, Heuristic functions
• Local Search Algorithms and Optimization Problems: Hill-climbing search, Simulated annealing, Genetic algorithms
• Adversarial Search: Minimax algorithm, Alpha-beta pruning
• Constraint Satisfaction Problems

Module 3: Knowledge Representation and Reasoning

• Propositional Logic: Syntax, Semantics, Inference
• First-Order Logic: Syntax, Semantics, Inference
• Inference in First-Order Logic: Unification, Resolution
• Knowledge Representation using Rules, Frames, and Semantic Nets
• Forward Chaining and Backward Chaining

Module 4: Planning

• Classical Planning: State-space representation, Partial-order planning, Planning graphs
• Planning and Acting in the Real World

Module 5: Uncertain Knowledge and Reasoning

• Uncertainty, Probability, and Bayes' Rule
• Bayesian Networks
• Hidden Markov Models
• Decision Making under Uncertainty

Module 6: Learning

• Learning from Examples: Decision Trees, Neural Networks
• Reinforcement Learning

Textbooks

• Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig

Reference Books

• Artificial Intelligence: Structures and Strategies for Complex Problem Solving by George F. Luger
• Artificial Intelligence: A New Synthesis by Nils J. Nilsson

List of Experiments

1. Implement and analyze uninformed search algorithms (BFS, DFS, etc.) for problems like 8-puzzle, water jug, etc.
2. Implement and analyze informed search algorithms (A* search) for pathfinding or similar problems.
3. Implement a game playing agent using minimax and alpha-beta pruning.
4. Solve constraint satisfaction problems like Sudoku or map coloring.
5. Implement a simple rule-based system or expert system.
6. Implement a decision tree learning algorithm.
7. Implement a simple neural network for classification or regression.

Course Outcomes

Upon completion of this lab, students will be able to:

1. Implement and experiment with various AI algorithms.
2. Analyze the performance of different AI techniques.
3. Apply AI techniques to solve real-world problems.
4. Gain hands-on experience with AI tools and libraries.