Siksha Sarovar

Siksha Sarovar (sikshasarovar.com) is a free educational web application that helps students in India learn programming and prepare for academic and competitive exams. The platform offers structured coding courses (C, C++, Python, Java, HTML, CSS, PHP, Power BI, AI, Machine Learning, Data Science), complete university curriculum notes for BCA/MCA students with previous year question papers, Class 10 and Class 12 CBSE/HBSE school notes, and dedicated preparation material for SSC, UPSC, Banking, Railway and other government exams. Browsing the site is completely free and requires no account. Users may optionally sign in with Google solely to save their learning progress, quiz scores and personal preferences across devices.

Privacy Policy | Terms of Service | Contact Siksha Sarovar | About Siksha Sarovar

v4.0.9 · PWA
Siksha Sarovar logo
Siksha Sarovar
Your Learning Universe

Siksha Sarovar is a free e-learning platform for coding courses, BCA university notes and competitive exam preparation. Optional Google sign-in saves your learning progress across devices.

Initializing knowledge base…
Compiling modules 0%

Unit 1 — AI Techniques

Lesson 5 of 34 in the free Artificial Intelligence notes on Siksha Sarovar, written by Rohit Jangra.

AI Techniques

An AI technique is a method of organising and using knowledge efficiently so that a computer program can behave intelligently, without simply enumerating every possibility. Raw search over "all possible states" is almost always too large to be practical — AI techniques exist to tame that scale.

Why Techniques Are Needed

Consider chess: the game tree has roughly 10^120 possible games — far beyond brute-force search on any computer that will ever exist. AI techniques let a program search intelligently, using knowledge to prune what to explore.

Characteristics of a Good AI Technique

CharacteristicMeaning
GeneralityShould apply across a range of problems, not just one instance
ExtensibilityNew facts and knowledge can be added without redesigning the whole system
Rich enough representationKnowledge should capture generalisations, be understandable, and reflect real-world structure
ModifiabilityCan be updated to correct errors as understanding improves
UsabilityKnowledge can be retrieved efficiently despite imperfect/incomplete match

Broad Categories of AI Techniques

CategoryWhat it doesExample technique
SearchExplores a state space to find a solution pathBFS, DFS, hill climbing, A*
Knowledge RepresentationStructures facts so they can be reasoned overPredicate logic, semantic networks, frames
LearningImproves performance from experience/examplesRote learning, induction, EBL
PlanningBreaks a goal into ordered sub-goals/actionsMeans-ends analysis, STRIPS
Reasoning under uncertaintyHandles incomplete/noisy knowledgeProbability, Bayesian networks, fuzzy logic

The remainder of this course develops these categories one at a time — search and heuristics in the rest of Unit 1, representation in Unit 2, language and learning in Unit 3, and applied systems in Unit 4.