Artificial Intelligence (AI) Syllabus

Tribhuvan University

Institute of Science and Technology

Bachelor of Science in Computer Science and Information Technology

Course Title: Artificial Intelligence

Course no: CSC266

Semester: IV

Nature of course: Theory + Lab

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 8 + 8

Credit Hours: 3

Course Description : The course introduces the ideas and techniques underlying the principles and design of artificial intelligent systems. The course covers the basics and applications of AIincluding: design of intelligent agents, problem solving, searching, knowledge representation systems, probabilistic reasoning, neural networks, machine learning and natural language processing.

Course Objective : The main objective of the course is to introduce concepts of Artificial Intelligence. The general objectives are to learn about computer systems that exhibit intelligent behavior, design intelligent agents, identify AI problems and solve the problems, design knowledge representation and expert systems, design neural networks for solving problems, identify different machine learning paradigms and identify their practical applications.

Course Contents:

Unit 1. Introduction 3 Hrs.

1.1.Intelligence, Artificial Intelligence(AI), AI Perspectives: acting and thinking humanly, acting and thinking rationally
1.2.History of AI
1.3.Foundations of AI: Philosophy, Economics, Psychology, Sociology, Linguistics, Neuroscience, Mathematics, Computer Science, Control Theory
1.4.Applications of AI

Unit 2. Intelligent Agents 4 Hrs.

2.1.Introduction of agents, Structure of Intelligent agent, Properties of Intelligent Agents
2.2.Configuration of Agents, PEAS description of Agents, PAGE
2.3.Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based, Learning Agent
2.4.Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, SingleAgent, Multi Agent

Unit 3. Problem Solving by Searching 9 Hrs.

3.1.Definition,State space representation, Problem as a state space search, Problem formulation, Well-defined problems
3.2.Solving Problems by Searching, Search Strategies: Informed, Uninformed, Performance evaluation of search strategies: Time Complexity, Space Complexity, Completeness, Optimality
3.3.Uninformed Search: Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Uniform Cost Search, Bidirectional Search
3.4.Informed Search, Heuristic Function, Admissible Heuristic, Informed Search Techniques: Greedy Best FirstSearch, A* Search, Optimality and Admissibility in A*, Hill Climbing Search, Simulated Annealing Search
3.5.GamePlaying,Adversarial Search Techniques: Mini-max Search, Alpha-Beta Pruning
3.6.Constraint Satisfaction Problems, Examples of Constraint Satisfaction Problems

Unit 4. Knowledge Representation 14 Hrs.

4.1.Definition and importance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems, Properties of Knowledge Representation Systems
4.2.Types of Knowledge Representation Systems: Semantic Nets, Frames, Conceptual Dependencies, Scripts, Rule Based Systems(Production System), Propositional Logic, Predicate Logic
4.3.Propositional Logic(PL): Syntax, Semantics, Formal logic-connectives, truth tables, tautology, validity, well-formed-formula, Inference using Resolution, Backward Chaining and Forward Chaining
4.4.Predicate Logic: FOPL, Syntax, Semantics, Quantification, Inference with FOPL: By converting into PL (existential and universal instantiation), Unification and lifting, Inference using resolution
4.5.Handling Uncertain Knowledge, Radom Variables, Prior and Posterior Probability, Inference using Full Joint Distribution, Bayes' Rule and its use, Bayesian Networks, Reasoning in Belief Networks
4.6.Fuzzy Logic: Fuzzy Sets, Membership in Fuzzy Set, Fuzzy Rulebase Systems

Unit 5. Machine Learning 9 Hrs.

5.1.Introductionto Machine Learning , Concepts of Learning, Supervised, Unsupervised and Reinforcement Learning
5.2.Statistical-based Learning: Naive Bayes Model
5.3.Learning by Genetic Algorithms: Operators in Genetic Algorithm: Selection, Mutation, Crossover, Fitness Function, Genetic Algorithm
5.4.Learning with Neural Networks: Introduction, Biological Neural Networks Vs. Artificial Neural Networks (ANN), Mathematical Model of ANN, Activation Functions: Linear, Step Sigmoid, Types of ANN: Feed-forward, Recurrent, Single Layered, Multi-Layered, Application of Artificial Neural Networks, Learning by Training ANN, Supervised vs. Unsupervised Learning, Hebbian Learning, Perceptron Learning, Back-propagation Learning

Unit 6. Applications of AI 6 Hrs.

6.1.Expert Systems, Components of Expert System: Knowledge base, inference engine, user interface, working memory, Development of Expert Systems
6.2.Natural Language Processing: Natural Language Understanding and Natural Language Generation, Steps of Natural Language Processing: Lexical Analysis(Segmentation, Morphological Analysis), Syntactic Analysis, Semantic Analysis, Pragmatic Analysis, Machine Translation,
6.3.Machine Vision Concepts: Machine vision and its applications, Components of Machine Vision System
6.4.Robotics: Robot Hardware (Sensors and Effectors) , Robotic Perceptions

Laboratory Works:

Student should write programs and prepare lab sheet for mostoftheunits in the syllabus. Majorly, students should practice design and implementation of intelligent agents and expert systems, searching techniques, knowledge representation systems and machine learning techniques. Students are also advised to implement Neural Networks for solving practical problems of AI. Students are advised to use LISP, PROLOG, andany other high level languagelike C, C++, Java, etc. The nature of programmingcan be decided bytheinstructorand student as per their comfort. The instructors have to prepare lab sheets for individual units covering the conceptof the units as per the requirement. The sample lab sessions can be as following descriptions;

Unit II: Intelligent Agents (4 Hrs)-

Write programs for implementing simple intelligent agents.

Unit III: Problem Solving by Searching (12Hrs)-

Write programs for illustrating the concepts of
1. Uninformed Searchlike DFS, BFS, etc.
2. Informed Searchlike Greedy Best First, A*, etc.
3. GameSearchlike MiniMax Search
Write programs for constraint satisfaction problems like water jug, n-queen problem, cryptoarithmatic problem,etc.

Unit IV: Knowledge Representation (12Hrs)-

Write programs for illustrating the concepts knowledge representation systems
1. rule based(program with if then rules)
2. predicate logic(using predicates like in Prolog)
3. frames(using concepts of class)
4. semantic nets (using concepts of graph)

Unit V: Machine Learning(10Hrs)-

Write programfor implementing Naive Bayes.
Write programfor implementing Neural Networks for realization of AND, OR gates.
Write programfor implementing Backpropagation Learning.

Unit VI: Applications of AI(7Hrs)-

Write programfor implementing expert systems like disease prediction, weather forecasting etc.
Use library tools like NLTK to illustrate concepts of Natural Language Processing.

Text Books:

Stuart Russel and Peter Norvig, Artificial Intelligence A Modern Approach, Pearson

Reference Books:

George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Benjamin/Cummings Publication
E. Rich, K. Knight, Shivashankar B. Nair, Artificial Intelligence, Tata McGraw Hill.
D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall.
P. H. Winston, Artificial Intelligence, Addison Wesley.