Artificial Intelligence Notes
Subject Overview
Artificial Intelligence introduces intelligent agents and problem-solving, uninformed and informed search (BFS, DFS, A*, Minimax, Alpha-Beta pruning), knowledge representation via propositional and predicate logic with forward/
Subject at a glance
4
Units
27
Topics
3
Credits
4
Infographics
Unit-wise Syllabus
4 units — open any unit for detailed topic-wise notes, diagrams and important exam questions
- What AI is
- intelligent agents and environments
- the concept of rationality
- agent structure
- knowledge-based agents
- the Wumpus World example
- problem-solving agents
- 1Perceive
Sensors read the environment
- 2Think
Use knowledge to choose an action
- 3Act
Actuators change the environment
- 4Learn
Improve from the result
Study guide
This unit introduces what AI is and how intelligent systems are structured. You study intelligent agents and environments, the concept of rationality, agent structure, knowledge-based agents, the Wumpus World example and problem-solving agents. Be able to describe an agent in terms of its percepts, actions, performance measure and environment.
- Uninformed search (DFS, BFS, iterative deepening)
- informed search (Best First Search, A*, AO*)
- adversarial search (Minimax, Alpha-Beta pruning)
- constraint satisfaction problems and backtracking search
- evolutionary search techniques and genetic algorithms
Uninformed
BFS, DFS, iterative deepening
Informed
Best-first, A*, AO*
Adversarial
Minimax, alpha-beta pruning
Constraint satisfaction
Backtracking
Evolutionary
Genetic algorithms
Study guide
Many AI problems are solved by searching. You study uninformed search (DFS, BFS, iterative deepening), informed search (Best First, A*, AO*), adversarial search (Minimax and Alpha-Beta pruning), constraint satisfaction problems with backtracking, and evolutionary techniques such as genetic algorithms. Trace A* on a small grid and Minimax with Alpha-Beta on a small game tree.
- Propositional and first-order predicate logic
- unification and lifting
- forward/backward chaining
- resolution
- truth maintenance systems
- introduction to planning (Blocks World, STRIPS)
- non-monotonic reasoning
- probabilistic reasoning
- introduction to fuzzy set theory
Starts from
Known facts
The goal
Direction
Data-driven
Goal-driven
Good for
Monitoring, planning
Diagnosis, answering a query
Example
Expert system generating conclusions
Proving a specific hypothesis
Study guide
AI systems must reason and handle uncertainty. You study propositional and first-order logic, unification and lifting, forward and backward chaining, resolution, truth maintenance systems, an introduction to planning (Blocks World, STRIPS), non-monotonic reasoning, probabilistic reasoning and fuzzy set theory. Practise converting English sentences into first-order logic.
- Introduction to machine learning
- computer vision
- robotics
- natural language processing and deep neural networks
- expert system architecture with case studies
- legal and ethical issues in AI
Machine learning
Learning patterns from data
Computer vision
Understanding images and video
NLP
Understanding human language
Robotics
Acting in the physical world
Expert systems
Rule-based reasoning
Study guide
The last unit surveys where AI is applied: machine learning, computer vision, robotics, natural language processing and deep neural networks, the architecture of expert systems with case studies, and the legal and ethical issues of AI. Prepare a short note on bias, privacy and accountability in AI; ethics questions are now common.
Why Artificial Intelligence Matters
AI is reshaping every industry and is the most sought-after skill area in technology. A clear understanding of search, reasoning and agents explains how AI systems actually work and prepares you for machine learning, data science and AI careers.
How to Prepare for Artificial Intelligence
- Learn the difference between uninformed and informed search with an example of each.
- Practise A* and Minimax with Alpha-Beta by hand.
- Prepare a short, balanced note on the ethical issues of AI.
- Follow current AI developments to give relevant, modern examples in your answers.
Artificial Intelligence: Frequently Asked Questions
What is a rational agent?
A rational agent chooses actions that are expected to maximise its performance measure given what it has perceived and what it knows.
What is the difference between BFS and A* search?
BFS explores nodes level by level without using any knowledge of the goal, while A* uses a heuristic estimate of cost to the goal to explore the most promising paths first.