Introduction to Artificial Intelligence
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Transcript of Introduction to Artificial Intelligence
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Introduction to Artificial Intelligence
CSE 473
Winter 1999
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Logistics
• Instructor: Alon Levy (alon@cs); Sieg 310.– Office hours: Monday, 3:30-4:30pm.– Email is good, but expect delays.
• TA: Steve Wolfman (wolf@cs); Sieg 428• www.cs.washington.edu/education/courses/cse473/99wi
(not really there yet).
• Mailing list: cse473@cs.– Subscribe by sending mail to majordomo@cs.
(not there yet either).
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Reading
• Required text:
Artificial Intelligence: Theory and Practice
Dean, Allen, Aloimonos
Addison Wesley
• Other good books:– Russell & Norvig: Artificial Intelligence - a Modern
Approach.– Genesereth & Nilsson: Logical Foundations of
Artificial Intelligence.
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Grading
• Problem sets: mostly programming assignments (Lisp: more on this soon).
• Midterm
• Final
• Class participation and discussion.
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What is Artificial Intelligence?
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Some Definitions (I)
The exciting new effort to make computers think …
machines with minds, in the full literal sense.
Haugeland, 1985
(excited but not really useful)
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Some Definitions (II)
The study of mental faculties through the useof computational models.
Charniak and McDermott, 1985
A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes.
Schalkoff, 1990(Applied psychology & philosophy?)
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Some Definitions (III)
The study of how to make computers do things at which, at the moment,
people are better.
Rich & Knight, 1991
(I can almost understand this one).
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Dimensions in AI Definitions
• Build intelligent artifacts vs. understanding human behavior.
• Does it matter how I built it as long as it does the job well?
• Should the system behave like a human or behave intelligently?
The Turing Test
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What Does AI Really Do?• Knowledge Representation (how does a program
represent its domain of discourse?)• Automated reasoning.• Planning (get the robot to find the bananas in the
other room). • Machine Learning (adapt to new circumstances).• Natural language understanding.• Machine vision, speech recognition, finding data on
the web, robotics, and much more.
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A Brief History of AI
• The Dartmouth conference, Summer ‘56.• Early enthusiasm 52-59:
– Puzzle solving with the General Problem Solver, Geometry theorem prover, Checkers player, Lisp.
• Reality strikes: – Programs don’t scale up.– The problem is not as easy as we thought:
• The spirit is willing but the flesh is weak -->
The vodka is good but the meat is rotten.
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More History• Knowledge-based systems (expert systems) 1969-
1979:– Ed Feigenbaum (Stanford): Knowledge is power! (as
opposed to weak methods)• Dendral (inferring molecular structure from a mass
spectrometer). • MYCIN: diagnosis of blood infections
• AI becomes an industry:– R1: configuring computers for DEC.– Robotic vision applications
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Recent Events: 1987-Present
• AI turns more scientific, relies on more mathematically sophisticated tools:– Hidden Markov models (for speech
recognition)– Belief networks (see Office 97).
• Focus turns to building useful artifacts as opposed to solving the grand AI problem.
• The victory of the neats over the scruffies?
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Recent AI Successes• Deep Blue beats Kasparov (AI?)
• Theorem provers proved an unknown theorem.
• Expert systems: medical, diagnosis, design
• Speech recognition applications (in limited domains).
• Robots controlling quality in factories.
• Intelligent agents on board Deep Space 1.
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An Intelligent Agent
Knowledge representation
reasoningplanning
learning
inputNatural lang.vision
effectors
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Outline of the Course • Search: the fundamental tool of AI programs.• Lisp briefing.• Knowledge representation:
– propositional logic– first-order logic– inference (soundness and completeness)– specialized formalisms: Horn rules, description logic.– Non-monotonic reasoning– Reasoning with uncertainty
• Planning• Machine learning• Natural language understanding• More, as time allows.