Download Introduction to Artificial Intelligence LECTURE 1: Introduction

Introduction to Artificial Intelligence
Fall 98
Definitions of AI
Introduction to Artificial Intelligence
• Develop programs/systems that perform/act
like humans
• Develop programs/systems that perform/act
rationally
• Understand human intelligence
• Formalize the laws of thought and action
LECTURE 1: Introduction
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What is AI?
Foundations of AI
The History of AI
State of the Art
INTELLIGENT AGENTS
Intro to AI, Fall 2002.
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Intro to AI, Fall 2002.
What is AI?
• Play games
– tic-tac-toe, chess, backgammon, poker
COMPUTER/
• Process natural language
– control tower conversation, stock market briefs
HUMAN
- types in questions
- receives answers on screen
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Examples of task for AI
The Turing Test
HUMAN
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• Industrial applications
- processes questions
- returns answers
– plant diagnostics, plan for manufacturing
• Expert-level performance
If the human cannot tell if it is a computer or a
human, the program exhibits intelligence
Intro to AI, Fall 2002.
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– molecular biology, computer configuration
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Intro to AI, Fall 2002.
Why is AI different than
conventional programming?
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Example: TIC-TAC-TOE
• 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
Intro to AI, Fall 2002.
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Intro to AI, Fall 2002.
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Introduction to Artificial Intelligence
Fall 98
Program 1: hard wired
Program 2: less hard wired
• Use procedures designed for the game:
• 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:
– try to place two marks in a row
– if opponent has two marks in a row, place mark in
third space
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– time efficient, requires lots of storage
– not extensible: requires a table for other games
Intro to AI, Fall 2002.
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Pattern matching to recognize board positions
Can encode different playing strategies
Better space efficiency, less time efficiency
Still game-dependent
Intro to AI, Fall 2002.
Program 3: AI-like
• Philosophy: mechanistic views of the mind, Spinoza and
his theory of emotions, philosophy of language, logical
positivism, rationality.
– current board position
– next legal positions
• Mathematics: algorithms, logic, formalization of
• Use an evaluation function:
mathematics, incompleteness, decision theory.
• Psychology: learning, behaviorism, cognitive science.
• Linguistics: grammars, syntax and semantics.
• Computer Science: computers, software, hardware,
– Rate the next move according to how likely it will
lead to a win
– look-ahead of possible opponent moves
programming languages, data bases, complexity
• Others: neuroscience, economics, game theory.
• More general because it embodies a general
strategy.
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AI and other domains
• Represent the state of the game:
Intro to AI, Fall 2002.
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Intro to AI, Fall 2002.
A brief history of AI (1)
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A brief history of AI (2)
• Gestation (43-56):
– Microworlds: ANALOGY, blocks world
– automata theory, neural networks, checkers,
theorem proving.
– Shannon, Turing, Von Neumann, Newell and
Simon, Minsky, McCarthy, Dartmouth Workshop.
• Great expectations (52-69):
– computers can do more than just arithmetic!
– General Problem Solver (GPS), better checkers
– LISP (LISt Processing language)
Intro to AI, Fall 2002.
© L. Joskowicz
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Intro to AI, Fall 2002.
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Introduction to Artificial Intelligence
Fall 98
A brief history of AI (3)
A brief history of AI (4)
• A dose of reality (66-74):
• Commercial AI: the ‘80s boom (80-90)
– ELIZA: human-like conversation.
– limitations of neural networks, genetic
algorithms, machine evolution.
– acting in the real world: robotics.
– DEC’s R1 computer configuration program
– many expert systems tools companies (mostly
defunct): Symbolics, Teknolwedge, etc.
– Japan’s 5th generation project: PROLOG.
– limited success in autonomous robotics and
vision systems.
• Knowledge-based systems (69-79):
– domain focus: experts systems vs. General
Problem Solvers.
– DENDRAL, MYCIN, XCON, etc.
Intro to AI, Fall 2002.
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Intro to AI, Fall 2002.
A brief history of AI (5)
• Embedded AI: many use AI techniques
without saying it is AI!
– 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!
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– Credit card approval (American Express)
– Consumer electronics (fuzzy logic)
– Systems that learn the user’s preferences
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• 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)
Intro to AI, Fall 2002.
Sociological view of AI
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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
• A well-defined community: interests,
meetings and a strong sense of belonging
• A variety of techniques: heuristic search,
knowledge representation, logics,
uncertainty, statistical learning, agent
programming
Intro to AI, Fall 2002.
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State of the Art
• The 90’s and now: specialization, quiet
progress
Intro to AI, Fall 2002.
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Intro to AI, Fall 2002.
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