ARTIFICIAL INTELLIGENCE AI is a subfield of computer science that studies intelligent systems Subfields/topics in AI studied in CS (adapted from IJCAI). Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Sep 11, 2019 NIST participates in interagency efforts to further innovation in AI. NIST Director and Undersecretary of Commerce for Standards and Technology Walter Copan serves on the White House Select Committee on Artificial Intelligence. Charles Romine, Director of NIST’s Information Technology Laboratory, serves on the Machine Learning and AI.
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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.
Artificial Intelligence Notes For CSE 6th sem
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 Syllabus – B.Tech 3rd YearUNIT-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 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
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:
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
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
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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