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Characteristic 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 the intelligent behavior appears, Artificial intelligence can be viewed from a variety of perspectives.
Artificial Intelligence Notes For CSE 6th sem
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To have a 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
UNIT-I:
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
UNIT-II:
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
UNIT-III:
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
UNIT-IV:
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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
UNIT-V:
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
UNIT-VI:
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.
OUTCOMES:
• 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.
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 A* algorithm with an example. What are the limitations of A* algorithm?
- Describe 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.
a) Translate these sentences into formulas in predicate logic.
b) Convert the formulas into clause form.
c) Prove that John likes peanuts using Resolution.
- What are frames? Give a sample frame of a Computer department of a college?
- Explain reasoning using Semantic networks?
- With neat diagram, explain the justification based truth maintenance system?
- Explain the logic based truth maintenance system with an example?
- Define certainty factor. What are the components of certainty factor?
- Explain the Bayesian method of reasoning?
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Sale Artificial Intelligence: What Everyone Needs to Know®- Oxford University Press, USA
- Jerry Kaplan
- OUP USA
- Paperback: 192 pages
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- Edition no. 3 (01/01/1970)
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