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Characteristics of Artificial Intelligence: Artificial Intelligence (AI) is a branch of Science which deals with helping machines find solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence and applying them as algorithms in a computer-friendly way. A more or less flexible or efficient approach can be taken depending on the requirements established, which influences how artificial intelligent behavior appears, Artificial intelligence can be viewed from a variety of perspectives.

Introduction To Artificial Intelligence Pdf

Artificial Intelligence Notes For CSE 6th sem

  1. PDF Artificial intelligence (AI), deep learning, machine learning and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world.
  2. Essentially, the goal of artificial intelligence is to optimize: make optimal decisions in complex environments and multi-body interactions. Almost all artificial intelligence problems will ultimately come down to the solution of an optimization problem, so the optimization theory is also the basic knowledge necessary for artificial intelligence.
  3. Artificial Intelligence – What it’s all about. Artificial intelligence (AI) traditionally refers to an artificial creation of human-like intelligence that can learn, reason, plan, perceive, or process natural language. Artificial intelligence is further defined as “narrow AI” or “general AI”.
  4. Artificial Intelligence: A Modern Approach. Artificial Intelligence: A Modern Approach (AIMA) is a university textbook on artificial intelligence, written by Stuart J. Russell and Peter Norvig. It was first published in 1995 and the third edition of the book was released 11 December 2009.
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Artificial intelligence paired with facial recognition systems may be used for mass surveillance. This is already the case in some parts of China. An artificial intelligence has also competed in the Tama City mayoral elections in 2018. Artificial intelligence (AI) and machine learning are being rapidly adopted for a range of applications in the financial services industry. As such, it is important to begin considering the financial stability implications of such uses. Because uses of this technology in finance are in a. Artificial Intelligence Can Create Immersive Experiences, Not Lessons. Machine learning in artificial intelligence should be geared toward meaningful lessons, not simply passing a quiz. Artificial Intelligence systems are able to identify each student's needs and come up with models which focus on method and reason rather than bald facts.

To have basic proficiency in a traditional AI language including an ability to write simply 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. Basic understanding of some of the more advanced topics of AI such as learning, natural language processing, agents and robotics, expert systems, and planning

Reference Books List for Artificial Intelligence

  • Artificial Intelligence- Saroj Kaushik, CENGAGE Learning,
  • The Artificial intelligence, A Modern Approach, 2nd ed, Stuart Russel, Peter Norvig, PEA
  • Artificial Intelligence- Rich, Kevin Knight, Shiv Shankar B Nair, 3rd ed, TMH
  • Introduction to Artificial Intelligence, Patterson, PHI
  • Artificial intelligence, structures, and Strategies for Complex problem solving, -George F Lugar, 5th ed, PEA
  • Introduction to Artificial Intelligence, Ertel, Wolf Gang, Springer
  • Artificial Intelligence, A new Synthesis, Nils J Nilsson, Elsevier

Artificial Intelligence Syllabus – B.Tech 3rd Year


Introduction to artificial intelligence: Introduction , history, intelligent systems, foundations of AI, applications, tic-tac-tie game playing, development of ai languages, current trends in AI


Problem-solving: state-space search and control strategies: Introduction, general problem solving, characteristics of the 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


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


Knowledge representation: Introduction, approaches to knowledge representation, knowledge representation using the 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


Expert system and applications: Introduction phases in building expert systems, expert system versus traditional systems, rule-based expert systems blackboard systems truth maintenance
systems, application of expert systems, list of shells and tools

Artificial Intelligence


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.

• Identify problems that are amenable to solution by AI methods, and which AI methods may be suited to solving a given problem.
• Formalize a given problem in the language/framework of different AI methods (e.g., as a search problem, as a constraint satisfaction problem, as a planning problem, as a Markov
decision process, etc).
• Implement basic AI algorithms (e.g., standard search algorithms or dynamic programming).
• Design and carry out an empirical evaluation of different algorithms on a problem formalization, and state the conclusions that the evaluation supports.

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Artificial Intelligence Important Questions List- 3rd Year

  • Discuss the areas of application of Artificial Intelligence.?
  • Discuss the tic-tac-toe problem in detail and explain how it can be solved using AI techniques?
  • Explain the A* algorithm with an example. What are the limitations of the A* algorithm?
  • Describe the different control strategies used in problem-solving?
  • Consider the following sentences: i. John likes all kinds of food. ii. Apples are food iii. Chicken is food iv. Anything anyone eats and isn’t killed by is food. v. Bill eats peanuts and is still alive. vi. Sue eats everything Bill eats.

Inductive Learning In Artificial Intelligence Pdf

a) Translate these sentences into formulas in predicate logic.

b) Convert the formulas into clause form.

c) Prove that John likes peanuts using Resolution.

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  • What are the frames? Give a sample frame of the Computer department of a college?
  • Explain reasoning using Semantic networks?
  • With a neat diagram, explain the justification based truth maintenance system?
  • Explain the logic-based truth maintenance system with an example?
  • Define the certainty factor. What are the components of the certainty factor?
  • Explain the Bayesian method of reasoning?

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