Artificial Intelligence Syllabus - BIM (TU)

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Course Description

Course Objectives

This module aims to provide the students with the basic foundation on concepts of searching and knowledge representation in AI systems. The key objective is to make students more pragmatic in knowledge of AI by giving its applications like designing and training Artificial Neural Networks along with additional laboratory works.

Course Description

Introduction, Agents and Environments, Informed and Uninformed Search, Knowledge Representation, Learning, Applications of AI, Production Systems, Uncertainty in AI.

Unit Contents

Course Details

Unit 1: Introduction : 4 hrs

  • What is AI?
    • Turing test approach: Chinese room argument
    • Cognitive approach
    • Laws of thought approach
    • Rational agent approach
  • Difference between AI and Omniscience

Unit 2: Agents and Environments : 7 hrs

  • Agent, Rational agent, and Intelligent Agent
  • Relationship between agents and environments
  • Environments and its properties
  • Agent structures
    • Simple reflex agents
    • Model-based reflex agents
    • Goal-based agents
    • Utility-based agents
    • Learning agents
  • Performance evaluation of agents: PEAS description

Unit 3: Informed and Uninformed Search : 8 hrs

  • Why search in AI?
  • Blind search (Un-informed search)
    • Breadth first search (BFS)

Variations: Uniform cost search

  • Depth first search (DFS)

Variations: Depth limited search, Iterative deepening DFS

  • Heuristic search (Informed search)
    • Hill climbing
    • The Foothills Problem
    • The Plateau Problem
    • The Ridge Problem
    • Greedy (Best-first) search
    • A* algorithm (search)
    • Means-Ends Analysis: Household ROBOT, Monkey Banana Problem
  • General Problem Solving (GPS): Problem solving agents
    • Constraint satisfaction problem
    • Constraint Satisfaction Search
    • AND/OR trees
    • The bidirectional search
    • Cryptoarithmatic
  • Game playing and AI
    • Game Trees and Minimax Evaluation
    • Heuristic Evaluation
    • Min-max algorithm (search)
    • Min-max with alpha-beta
    • Games of chance
    • Game theory

Unit 4: Knowledge Representation : 8 hrs

  • Logic
    • Propositional Logic
      • Syntax, semantics, and properties
      • Conjunctive Normal Form (CNF)
      • Disjunctive Normal Form (DNF)
      • Inference Rules
      • Resolution
    • Prehdicate Logic
      • First-Order Predicate Logic (FOPL)
      • Syntax and semantics in FOPL
      • Quantifiers
      • Clausal Normal Form
      • Resolution
    • Fuzzy Logics
  • Semantic networks (nets): Introduction, and examples

Unit 5: Learning : 6 hrs

  • Why learning?
  • Supervised (Error based) learning
  • Gradient descent learning: Least Mean Square, Back Propagation algorithm
  • Stochastic learning
  • Unsupervised learning
    • Hebbian learning algorithm
    • Competitive learning
  • Reinforced learning (output based)
  • Genetic algorithms: operators

Unit 6: Applications of AI : 5 hrs

  • Artificial Neural Networks (ANN)
    • Neural Networks (NN) and ANN
    • Activation functions: unit (unary and binary), ramp, piecewise linear, & sigmoid
    • Training and testing: Basic concept
    • Mc-Colloch-Pits neuron model
  • Realization of AND, OR, NOT, and XOR gates
  • Neural network architectures
  • Single layer feed-forward architecture: ADALINE, Perceptron NN
  • Applications of ANN
  • Natural Language Processing (NLP)
    • Fundamentals of language processing

Unit 7: Production systems : 4 hrs

  • Strong Methods vs Weak Methods
  • Advantages of Production Systems
  • Production Systems and inference methods
    • Conflict resolution strategies
    • Forward chaining
    • Backward chaining

Unit 8: Uncertainty in AI : 3 hrs

  • Fuzzy sets
  • Fuzzy logic
  • Fuzzy inferences
  • Probability theory and uncertainty

Unit 9: Expert Systems Human and Machine experts : 3 hrs

  • Characteristics of expert systems
  • Knowledge engineering
  • Knowledge acquisition
  • Classic expert system
    • MYCIN
    • EMYCIN
  • Case based reasoning

Lab Task:

Students are required to carry out at least 6 lab tasks on predicate calculus, searching and neural networks using ProLog and C/C++/Java. Some of the lab tasks may be on:

  • Relationship programs (e.g. mother, father, brother etc)
  • Recursive programs: Factorial, Fibonacci series etc
  • Ancestor programs
  • Tower of Hanoi (TOH) program
  • Monkey banana problem
  • Realization of logic gates (using C/C++/Java)

Text and Reference Books


Russel S. and Norvig P., Artificial Intelligence: A modern Approach, Prentice hall Ritch and Knight, Artificial Intelligence, Prentice hall

Dan W. Patterson, Artificial Intelligence

Artificial Intelligence in the 21st century, Stephen Lucci, Danny Kopec, Mercury Learning and Information

Artificial Intelligence: Foundations of computational Agents, David L. Poole, Alan K. Mackworth, first edtion, Cambridge University Press

Download Syllabus
  • Short Name N/A
  • Course code IT 228
  • Semester Seventh Semester
  • Full Marks 100
  • Pass Marks 45
  • Credit 3 hrs
  • Elective/Compulsary Compulsary