R23
IT · 3-1
✓ VERIFIED VS. PUBLISHED SYLLABUS 2026-07-11
Artificial Intelligence
JNTUK R23 · IT · semester 3-1 · syllabus
This JNTUK R23 professional elective is listed for Information Technology (IT). It is listed in semester 3-1. It carries 3 credits. The published coverage runs across 5 units, from Introduction through Expert Systems.
Verified vs. published syllabus
Checked 11 Jul 2026
SOURCE DOCKET
Published evidence
✓ Verified
Checked 11 Jul 2026
Record 5 published units
Verification scope Subject name, credits, and unit-wise syllabus are sourced directly from JNTUK's official R23 course structure and syllabus document.
5 units, from Introduction to Expert Systems. Tick off units as you cover them — your progress stays on this device.
Unit-wise syllabus
0 / 5 COVERED
UNIT 01 — Introduction
AI problems, foundation of AI and history of AI · Intelligent agents: agents and environments, the concept of rationality · The nature of environments, structure of agents · Problem solving agents, problem formulation
UNIT 02 — Searching
Searching for solutions, uninformed search strategies: breadth-first search, depth-first search · Search with partial information (heuristic search): hill climbing, A*, AO* algorithms, problem reduction · Game Playing: adversarial search, games, mini-max algorithm, optimal decisions in multiplayer games · Problems in game playing, alpha-beta pruning, evaluation functions
UNIT 03 — Representation of Knowledge
Knowledge representation issues, predicate logic, logic programming · Semantic nets, frames and inheritance, constraint propagation · Representing knowledge using rules, rule-based deduction systems · Reasoning under uncertainty, review of probability, Bayes' probabilistic inferences and Dempster-Shafer theory
UNIT 04 — Logic Concepts
First order logic, inference in first order logic, propositional vs. first order inference · Unification and lifts, forward chaining, backward chaining, resolution · Learning from observation: inductive learning, decision trees, explanation-based learning · Statistical learning methods, reinforcement learning
UNIT 05 — Expert Systems
Architecture of expert systems · Roles of expert systems: knowledge acquisition, meta-knowledge, heuristics · Typical expert systems: MYCIN, DART, XCON · Expert system shells