sc JNTUH B.TECH R18 4-1 Syllabus For Artificial intelligence PDF 2022 – Cynohub

# JNTUH B.TECH R18 4-1 Syllabus For Artificial intelligence PDF 2022

### Get Complete Lecture Notes for Artificial intelligence on Cynohub APP

You will be able to find information about Artificial intelligence along with its Course Objectives and Course outcomes and also a list of textbook and reference books in this blog.You will get to learn a lot of new stuff and resolve a lot of questions you may have regarding Artificial intelligence after reading this blog. Artificial intelligence has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Artificial intelligence can be learnt easily as long as you have a well planned study schedule and practice all the previous question papers, which are also available on the CynoHub app.

All of the Topic and subtopics related to Artificial intelligence are mentioned below in detail. If you are having a hard time understanding Artificial intelligence or any other Engineering Subject of any semester or year then please watch the video lectures on the official CynoHub app as it has detailed explanations of each and every topic making your engineering experience easy and fun.

### Artificial intelligence Unit One

#### UNIT – I

Problem Solving by Search-I: Introduction to AI, Intelligent Agents
Problem Solving by Search –II: Problem-Solving Agents, Searching for Solutions, Uninformed Search Strategies: Breadth-first search, Uniform cost search, Depth-first search, Iterative deepening Depth-first search, Bidirectional search, Informed (Heuristic) Search Strategies: Greedy best-first search, A* search, Heuristic Functions, Beyond Classical Search: Hill-climbing search, Simulated annealing search, Local Search in Continuous Spaces, Searching with Non-Deterministic Actions, Searching wih Partial Observations, Online Search Agents and Unknown Environment .

### Artificial intelligence Unit Two

#### UNIT – II

Problem Solving by Search-II and Propositional Logic
Adversarial Search: Games, Optimal Decisions in Games, Alpha–Beta Pruning, Imperfect Real-Time Decisions.
Constraint Satisfaction Problems: Defining Constraint Satisfaction Problems, Constraint Propagation, Backtracking Search for CSPs, Local Search for CSPs, The Structure of Problems.
Propositional Logic: Knowledge-Based Agents, The Wumpus World, Logic, Propositional Logic, Propositional Theorem Proving: Inference and proofs, Proof by resolution, Horn clauses and definite clauses, Forward and backward chaining, Effective Propositional Model Checking, Agents Based on Propositional Logic.

### Artificial intelligence Unit Three

#### UNIT – III

Logic and Knowledge Representation
First-Order Logic: Representation, Syntax and Semantics of First-Order Logic, Using First-Order Logic, Knowledge Engineering in First-Order Logic.
Inference in First-Order Logic: Propositional vs. First-Order Inference, Unification and Lifting, Forward Chaining, Backward Chaining, Resolution.
Knowledge Representation: Ontological Engineering, Categories and Objects, Events. Mental Events and Mental Objects, Reasoning Systems for Categories, Reasoning with Default Information.

### Artificial intelligence Unit Four

#### UNIT – IV

Planning
Classical Planning: Definition of Classical Planning, Algorithms for Planning with State-Space Search, Planning Graphs, other Classical Planning Approaches, Analysis of Planning approaches.
Planning and Acting in the Real World: Time, Schedules, and Resources, Hierarchical Planning, Planning and Acting in Nondeterministic Domains, Multi agent Planning.

### Artificial intelligence Unit Five

#### UNIT – V

Uncertain knowledge and Learning
Uncertainty: Acting under Uncertainty, Basic Probability Notation, Inference Using Full Joint Distributions, Independence, Bayes’ Rule and Its Use,
Probabilistic Reasoning: Representing Knowledge in an Uncertain Domain, The Semantics of Bayesian Networks, Efficient Representation of Conditional Distributions, Approximate Inference in Bayesian Networks, Relational and First-Order Probability, Other Approaches to Uncertain Reasoning; Dempster-Shafer theory.
Learning: Forms of Learning, Supervised Learning, Learning Decision Trees. Knowledge in Learning: Logical Formulation of Learning, Knowledge in Learning, Explanation-Based Learning, Learning Using Relevance Information, Inductive Logic Programming.

### Artificial intelligence Course Objectives

To learn the distinction between optimal reasoning Vs. human like reasoning
To understand the concepts of state space representation, exhaustive search, heuristic search together with the time and space complexities.
To learn different knowledge representation techniques.
To understand the applications of AI, namely game playing, theorem proving, and machine learning.

### Artificial intelligence Course Outcomes

Ability to formulate an efficient problem space for a problem expressed in natural language.
Select a search algorithm for a problem and estimate its time and space complexities.
Possess the skill for representing knowledge using the appropriate technique for a given problem.
Possess the ability to apply AI techniques to solve problems of game playing, and machine learning.

### Artificial intelligence Text Books

Artificial Intelligence A Modern Approach, Third Edition, Stuart Russell and Peter Norvig, Pearson Education.

### Artificial intelligence Reference Books

Artificial Intelligence, 3rd Edn, E. Rich and K.Knight (TMH)
Artificial Intelligence, 3rd Edn., Patrick Henny Winston, Pearson Education.
Artificial Intelligence, Shivani Goel, Pearson Education.
Artificial Intelligence and Expert systems – Patterson, Pearson Education.