Artificial Intelligence and Expert Systems

This course introduces the fundamental concepts, techniques, and applications of Artificial Intelligence (AI) with a strong emphasis on expert systems. Topics include intelligent agents, problem-solving and search, knowledge representation and reasoning, probabilistic reasoning, machine learning techniques (including neural networks), and uncertainty handling. Throughout the course, students will explore how each AI technique contributes to the design, reasoning, and decision-making capabilities of expert systems. Ethical, social, and practical implications of AI-based expert systems are also discussed.

Instructor: Jubair Ahmed Nabin

Term: Spring

Location: Academic Building

Time: Saturdays - Wednesdays, 9:35-10.30 AM, 3:20-4:20 PM

Course Overview

This course provides a comprehensive introduction to Artificial Intelligence (AI) and Expert Systems. Students will:

  • Understand fundamental AI concepts and intelligent agent design
  • Apply search algorithms and problem-solving techniques
  • Learn knowledge representation and logical reasoning methods
  • Analyze uncertainty using probabilistic and fuzzy models
  • Explore machine learning techniques including neural networks
  • Design and evaluate rule-based expert systems
  • Examine ethical and societal implications of AI technologies

Prerequisites

  • No formal prerequisites
  • Basic programming and logical reasoning knowledge is helpful

Textbooks

  • Artificial Intelligence: A Modern Approach — Russell & Norvig
  • Artificial Intelligence Illuminated — Ben Coppin

Grading

  • Mid-Term Exam: 25%
  • Final Exam: 50%
  • Class Test: 10%
  • Assignment/Project/Presentation: 10%
  • Participation/Attendance: 5%

Schedule

Week Date Topic Materials
1 Week 1 Introduction to AI and Expert Systems

Course overview, history, evolution, scope, and applications of AI and expert systems.
2 Week 2 Intelligent Agents and Problem Formulation

Study of intelligent agents, environments, PEAS framework, and problem formulation.
3 Week 3 Uninformed and Informed Search

BFS, DFS, uniform cost search, greedy search, and best-first search techniques.
4 Week 4 Heuristic and Adversarial Search

Heuristic functions, A* algorithm, adversarial search, and game playing.
5 Week 5 Game Playing and Constraint Satisfaction

Minimax algorithm, alpha-beta pruning, constraint satisfaction problems, and decision strategies.
6 Week 6 Knowledge Representation and Logic

Knowledge bases, propositional logic, first-order logic, and rule-based systems.
7 Week 7 Inference and Probabilistic Reasoning

Forward and backward chaining, reasoning trace, probability basics, and Bayes theorem.
8 Week 8 Expert Systems and Planning

Classic expert systems, planning concepts, and block world problems.
9 Week 9 Uncertainty and Fuzzy Logic

Markov decision processes, conflict resolution, and fuzzy logic in expert systems.
10 Week 10 Knowledge Engineering and System Design

Rule representation, knowledge acquisition, and expert system design and testing.
11 Week 11 Machine Learning and Neural Networks

Supervised and unsupervised learning, neural networks, and perceptron models.
12 Week 12 Advanced AI Concepts

Reinforcement learning, generative AI, and hybrid intelligent systems.
13 Week 13 Ethics and Project Presentation

Ethical issues in AI, system limitations, project presentations, and course review.