Artificial Intelligence (2 Units C: LH 15; PH 45)
Course Contents
Overview of Artificial Intelligence. History of AI. Goals of AI. AI Technique. Types of AI.
Branches and applications of AI. Advantages and Disadvantages. Introduction to Intelligent
Agents. Agent Performance, Examples of Agents, Agent Faculties, Rationality, Agent
Environment. Agent Architectures. Search. General Classes of AI Search Algorithm
Problems. Problem Solving by Search. Types of AI Search Techniques and Strategies.
Introduction to the types of problems and techniques in AI. Problem-Solving methods. Major
structures used in AI programmes. Knowledge Representation. KR and Reasoning
Challenges. KR Languages. Knowledge representation techniques such as predicate logic,
non-monotonic logic, and probabilistic reasoning. Semantic Network - types of relationships,
semantic network inheritance, types and components. Introduction to Frames. Natural
Language Processing (NLP). Introduction to natural language understanding and various
syntactic and semantic structures. Introduction to Expert Systems - characteristics,
components, types, requirements, technology, development. Programming Languages for AI.
Introduction to computer image recognition.
Lab work: Group practical in (i) Turing test practical - Students can act out their own version
of the Turing test (ii) Facial recognition practical to aid in teaching students how machine
learning works with students simulating a facial recognition algorithm. Practical applications
of NLP in groups – (i) Question Answering focuses on building systems that automatically
answer the questions asked by humans in a natural language (ii) Spam detection application
for detecting unwanted e-mails getting to a user's inbox (iii) Sentiment analysis/opinion mining should be used on the web to analyse the attitude, behaviour, and emotional state of the sender, implemented through a combination of NLP and statistics (iv) Practical exercise of machine translation used to translate text or speech from one natural language to another
natural language such as the Google Translator (v) Developing a model to provide word
processor software for the spelling correction (vi) Developing a model for speech recognition
for converting spoken words into text (vii) Implementing a Chatbot to provide the
staff/student's chat services. OR
Group Practical exercise on agents and its environment using simulation of a colony of ants
foraging for food; model simulating a message between agents; model simulating the flocking behaviour of birds; model to apply standard search algorithm to the classic search problem of missionaries and cannibals, and how to use communicating agents for searching networks.
Some computer AI animation exercises for any branch of AI. Practical exercise on simple
robots coupling and programming. Group project of building a lawn robot for trimming
grasses, or any simple design and implementation of robotics.
- Teacher: LMS Admin