JNTUK R19 3-1 Artificial Intelligence Material/Notes PDF Download

JNTUK R19 3-1 Artificial Intelligence Material/Notes PDF Download

Students those who are studying JNTUK R19 CSE Branch, Can Download Unit wise R19 3-1 Artificial Intelligence Material/Notes PDFs below.

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JNTUK R19 3-1 Artificial Intelligence Material/Notes PDF Download

Course Objectives: The main objectives are

  • To have a basic proficiency in a traditional AI language including an ability to write simple to intermediate programs and an ability to understand code written in that language
  • To have an understanding of the basic issues of knowledge representation and blind and heuristic search, as well as an understanding of other topics such as minimax, resolution, etc. that play an important role in AI programs
  • To have a basic understanding of some of the more advanced topics of AI such as learning, natural language processing, agents and robotics, expert systems, and planning

Course Outcomes: At the end of the course, the students will be able to:

  • Outline problems that are amenable to solution by AI methods, and which AI methods may be suited to solving a given problem
  • Apply the language/framework of different AI methods for a given problem
  • Implement basic AI algorithms- standard search algorithms or dynamic programming
  • Design and carry out an empirical evaluation of different algorithms on problem formalization, and state the conclusions that the evaluation supports

UNIT-1

Introduction, history, intelligent systems, foundations of AI, applications, tic-tac-toe game playing, development of AI languages, current trends.

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UNIT-2

Problem solving: state-space search and control strategies: Introduction, general problem solving, characteristics of problem, exhaustive searches, heuristic search techniques, iterative deepening A*, constraint satisfaction.

Problem reduction and game playing: Introduction, problem reduction, game playing, alpha beta pruning, two-player perfect information games.

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UNIT-3

Logic concepts: Introduction, propositional calculus, proportional logic, natural deduction system, axiomatic system, semantic tableau system in proportional logic, resolution refutation in proportional logic, predicate logic.

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UNIT-4

Knowledge representation: Introduction, approaches to knowledge representation, knowledge representation using semantic network, extended semantic networks for KR, knowledge representation using frames. Advanced knowledge representation techniques: Introduction, conceptual dependency theory, script structure, CYC theory, case grammars, semantic web

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UNIT-5

Expert system and applications: Introduction phases in building expert systems, expert system versus traditional systems

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Uncertainty measure: probability theory: Introduction, probability theory, Bayesian belief networks, certainty factor theory, dempster-shafer theory Fuzzy sets and fuzzy logic: Introduction, fuzzy sets, fuzzy set operations, types of membership functions, multi valued logic, fuzzy logic, linguistic variables and hedges, fuzzy propositions, inference rules for fuzzy propositions, fuzzy systems.

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TEXT BOOKS:

1) Artificial Intelligence- Saroj Kaushik, CENGAGE Learning

2) Artificial intelligence, A modern Approach , 2nded, Stuart Russel, Peter Norvig, PEA

REFERENCE BOOKS:

1) Artificial Intelligence- Deepak Khemani, TMH, 2013

2) Introduction to Artificial Intelligence, Patterson, PHI

3) Atificial intelligence, structures and Strategies for Complex problem solving, -George F Lugar, 5thed, PEA

e-Resources:

1) https://nptel.ac.in

2) http://aima.cs.berkeley.edu/

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