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Introduction to Artificial Intelligence LECTURE 1: Introduction

Introduction to Artificial Intelligence LECTURE 1: Introduction. What is AI? Foundations of AI The History of AI State of the Art. Definitions of AI. Develop programs/systems that perform/act like humans Develop programs/systems that peform/act rationally Understand human intelligence

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Introduction to Artificial Intelligence LECTURE 1: Introduction

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  1. Introduction to Artificial Intelligence LECTURE 1: Introduction • What is AI? • Foundations of AI • The History of AI • State of the Art

  2. Definitions of AI • Develop programs/systems that perform/act like humans • Develop programs/systems that peform/act rationally • Understand human intelligence • Formalize the laws of thought and action INTELLIGENT AGENTS

  3. What is AI? The Turing Test COMPUTER/ HUMAN HUMAN - types in questions - receives answers on screen - processes questions - returns answers If the human cannot tell if it is a computer or a human, the program exhibits intelligence

  4. Examples of task for AI • Play games • tic-tac-toe, chess, backgammon, poker • Process natural language • control tower conversation, stock market briefs • Industrial applications • plant diagnostics, plan for manufacturing • Expert-level performance • molecular biology, computer configuration

  5. Why is AI different than conventional programming? • Strive for • GENERALITY • EXTENSIBILITY • Capture rational deduction patterns • Tackle problems with no algorithmic solution • Represent and manipulate KNOWLEDGE, rather than DATA • A new set of representation and programming techniques: HEURISTICS

  6. Example: TIC-TAC-TOE

  7. Program 1: hard wired • Code a table of all possible board positions and the transitions between them (state diagram) • Given a position, look in the table for the next move and return • Properties: • time efficient, requires lots of storage • not extensible: requires a table for other games

  8. Program 2: less hard wired • Use procedures designed for the game: • try to place two marks in a row • if opponent has two marks in a row, place mark in third space • Pattern matching to recognize board positions • Can encode different playing strategies • Better space efficiency, less time efficiency • Still game-dependent

  9. Program 3: AI-like • Represent the state of the game: • current board position • next legal positions • Use an evaluation function: • Rate the next move according to how likely it will lead to a win • look-ahead of possible oponent moves • More general because it embodies a general strategy.

  10. Foundations of AI • Philosophy: Aristotle, mechanistic views, materialism, positivism, rationality. • Mathematics: algorithms, logic, formalization of mathematics, incompleteness, decision theory. • Psychology: behaviorism, cognitive science. • Linguistics: grammars, syntax and semantics. • Computer Science: computers, software, theory • Others: neuroscience, economics, game theory.

  11. A brief history of AI (1) • Gestation (43-56): • automata theory, neural networks, checkers, theorem proving. • Shannon, Turing, Von Neumann, Newell and Simon, Minsky, McCarthy, Darmouth Workshop. • Great expectations (52-69): • computers can do more than arithmetics! • General Problem Solver (GPS), better checkers • LISP (LISt Processing language)

  12. A brief history of AI (2) • Microworlds: ANALOGY, blocks world

  13. A brief history of AI (3) • A dose of reality (66-74): • ELIZA: human-like conversation. • limitations of neural networks, genetic algorithms, machine evolution. • acting in the real world: robotics. • Knowledge-based systems (69-79): • domain focus: experts systems vs. General Problem Solvers. • DENDRAL, MYCIN, XCON, etc.

  14. A brief history of AI (4) • Commercial AI: the ‘80s boom (80-90) • DEC’s R1 computer configuration program • many expert systems tools companies (mostly defunct): Symbolics, Teknowledge, etc. • Japan’s 5th generation project: PROLOG. • limited success in autonomous robotics and vision systems.

  15. A brief history of AI (5) • The 90’s: specialization, quiet progress • neural networks, genetic algorithms • probabilistic reasoning and uncertainty • learning • planning and constraint solving • agents • autonomous robotics: NAV autonomous driving van, crater exploration, robot soccer • IBM’s Deep Blue beats Kasparov!

  16. State of the Art • Embedded AI: many use AI techniques without saying it is AI! • Credit card approval (American Express) • Consumer electronics (fuzzy logic) • Healthy research in many areas: intelligent agents, machine learning, man-machine interfaces, etc. • More integrative view: acting in the real world (robots, self diagnosing machines)

  17. To find out more about AI • “Godel, Escher, Bach” D. Hoftstadter • Annual AI conferences : American Assoc of Artificial Intelligence (AAAI) Int. Joint Conf. On Artificial Intelligence (IJCAI) • Specialized conferences: Machine Learning, Knowledge Representation, Vision, Robotics, etc. • A dozen journals: the main one is Artificial Intelligence

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