Skip to main content

23AD02 - ARTIFICIAL INTELLIGENCE

  Artificial Intelligence (AI) is a branch of computer science that enables machines to perform tasks that normally require human intelligence.

It involves learning, reasoning, problem-solving, and decision-making using algorithms and data. AI is widely used in applications such as virtual assistants, healthcare, robotics, and autonomous systems.

PREREQUISITE: Computer Programming, Mathematical Foundations of Computer Science, linear algebra, data structures and algorithms 

COURSE OUTCOMES (COs): At the end of the course, students will be able to

  • CO1: Enumerate the history & foundation of AI. (Understand- L2)
  • CO2: Applythe searching algorithms for AI in problem solving. (Apply- L3)
  • CO3: Choose the appropriate representation of knowledge. (Apply- L3)
  • CO4: Choose the appropriate logic concepts. (Apply- L3)
  • CO5: Understand Expert systems techniques in AI (Understand-L2) 
UNIT-1:  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 II: Searching: Searching for solutions, uniformed search strategies – Breadth first search, depth first Search. Search with partial information (Heuristic search) Hill climbing, A*, AO* Algorithms, Problem reduction, Game Playing-Adversial search, Games, mini-max algorithm, optimal decisions in multiplayer games, Problem in Game playing, Alpha-Beta pruning, Evaluation functions. 
UNIT III: Representation of Knowledge: Knowledge representation issues, predicate logic- logic programming, semantic nets- frames and inheritance, constraint propagation, representing knowledge using rules, rules based deduction systems. Reasoning under uncertainty, review of probability, Bayes’ probabilistic interferences and dempstershafer theory.
UNIT IV: Logic concepts: First order logic. Inference in first order logic, propositional vs. first order inference, unification & lifts forward chaining, Backward chaining, Resolution, Learning from observation Inductive learning, Decision trees, Explanation based learning, Statistical Learning methods, Reinforcement Learning.
UNIT V: Expert Systems: Architecture of expert systems, Roles of expert systems – Knowledge Acquisition Meta knowledge Heuristics. Typical expert systems – MYCIN, DART, XCON: Expert systems shells. 

Comments

  1. DpbossJune 28, 2026 at 5:07 PM

    I found this article very useful for understanding current AI trends. Madhur Night Jodi Chart

    ReplyDelete

Post a Comment

Popular posts from this blog

23CS52 - DATA STRUCTURES (Lab)

Data Structures Lab is designed to provide hands-on experience in implementing and analyzing various data structures using the C programming language. Through a series of practical exercises, students learn to implement both linear and non-linear data structures such as arrays, linked lists, stacks, queues, trees, and hashing techniques.
1 comment
Read more

23CS02 - DATA STRUCTURES (Theory)

Data Structures is a fundamental subject in computer science that deals with organizing, storing, and managing data efficiently. It helps in performing operations such as insertion, deletion, searching, and sorting in an optimized way.
1 comment
Read more

Operating Systems - 20CS11 Lecture Notes

  COURSE OUTCOMES (COs): At the end of the course, students will be able to CO1 Demonstrate the underlying principles and techniques of the operating system     (Understand-l2) CO2 Interpret scheduling and communication methods of processes handled by operating systems (Understand-L2) CO3 Distinguish the process synchronization methods and deadlock handling approaches employed in operating systems (Understand-L2) CO4 Classify memory management techniques and virtual memory mechanisms     (Understand-L2) CO5 Interpret the strategies of disk scheduling algorithms and file system architecture(Understand-L2) Unit-Wise Lecture Notes:
Post a Comment
Read more