Download Introduction to AI

What is AI? ì Possible Answers? AI: How did it all begin? ì AI prehistory ì 
Philosophy
Logic, methods of reasoning, mind as physical
system foundations of learning, language,
rationality
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Mathematics
Formal representation and proof algorithms,
computation, (un)decidability, (in)tractability,
probability
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Economics
utility, decision theory
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Neuroscience
physical substrate for mental activity
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Psychology
cognitive science, affective science
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Computer
engineering
efficient algorithms
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Control theory
design systems that maximize an objective
function over time
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Linguistics
knowledge representation, grammar
1956: The Birth of AI …solve kinds of problems now reserved for humans… …significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer… Abridged history of AI ì 
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1943
1950
1956
1952—69
1950s
McCulloch & Pitts: Boolean circuit model of brain
Turing's "Computing Machinery and Intelligence"
Dartmouth meeting: "Artificial Intelligence" adopted
Look, Ma, no hands!
Early AI programs, including Samuel's checkers
program, Newell & Simon's Logic Theorist,
Gelernter's Geometry Engine
1960s: Initial Optimism ì  Playing checkers (Arthur Samuel) ì  General Problem Solver (Allen Newell & Herbert Simon) Abridged history of AI ì  1965
ì  1966—73
ì  1969—79
Robinson's complete algorithm for logical reasoning
AI discovers computational complexity
Neural network research almost disappears
Early development of knowledge-based systems
Blocks world SHRDLU (Winograd 1972) Abridged history of AI Abridged history of AI Road block? ì  What do you think the road block to these types of systems were? Abridged history of AI ì 
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1980-1986-1987-1995--
AI becomes an industry
Neural networks return to popularity
AI becomes a science
The emergence of intelligent agents
1990s: Resurgence ì  ProbabilisLc approaches ì  AdopLon of scienLfic rigor ì  Return to dreams of human-­‐level AI 2000s: Big Data ì  Focus on data ì  SoluLon to KA boPleneck? What is AI? ì What is AI? Views of AI fall into four categories: Thinking humanly Thinking raLonally AcLng humanly AcLng raLonally The textbook advocates "acLng raLonally" Thinking humanly: cognitive modeling ì  CogniLve Modeling Approach ì  1960s "cogniLve revoluLon": informaLon-­‐processing psychology ì  Requires scienLfic theories of internal acLviLes of the brain ì  ValidaLon Requires 1) PredicLng and tesLng behavior of human subjects (top-­‐down) or 2) Direct idenLficaLon from neurological data (boPom-­‐up) Systems modeled based on the way humans think. Systems are based on: ì  ProducLons ì  Rule-­‐based ì  Experimental results of human behavior Thinking humanly: examples ì  CogniLve Modeling Approach -­‐ SOAR Credit for images from Laird’s arLcle on IntroducLon to Soar Thinking humanly: cognitive modeling ì  CogniLve Modeling Approach -­‐ SOAR Credit for images from Laird’s arLcle on IntroducLon to Soar Thinking humanly: examples ì  CogniLve Modeling Approach – ACT-­‐R Image from: hPp://act-­‐r.psy.cmu.edu/about/ Acting humanly: Turing Test ì  Turing (1950) "Computing machinery and intelligence":
ì  "Can machines think?" à "Can machines behave
intelligently?”
Image from: Hwee Tou Ng’s AI lectures Acting humanly: Turing Test ì  Stimulated the development of:
ì  Natural Language Processing ì  Knowledge RepresentaLon ì  Automated Reasoning ì  Machine Learning ì  Computer Vision ì  RoboLcs Eliza ELIZA Person : Men are all alike. ELIZA : In what way? Person : They're always bugging us about something or other. ELIZA : Can you think of a specific example? Person : Well, my boyfriend made me come here. ELIZA : Your boyfriend made you come here? Person : He says I'm depressed much of the Lme. ELIZA : I am sorry to hear that you are depressed. Person : It's true. I am unhappy. ELIZA : Do you think coming here will help you not to be unhappy? Person : I need some help, that much seems certain. ELIZA : What would it mean to you if you got some help? Person : Perhaps I could learn to get along with my mother. ELIZA : Tell me more about your family Examples Views of AI fall into four categories: Thinking humanly Thinking raLonally AcLng humanly AcLng raLonally Example Applications hPp://www.youtube.com/watch?v=iragGqvoHLQ Example Applications hPp://www.youtube.com/watch?v=oG-­‐2qr78GbE&playnext=1&list=PLSVPS6u0z0xfa7 Xml9TKLv6IWcHF5cNBc&feature=results_main Example Applications hPp://www.youtube.com/watch?v=nZKxsG0AVw Thinking rationally: "laws of thought" ì 
Aristotle: what are correct arguments/
thought processes? ì 
Purely logical thought and reasoning ì 
MathemaLcally well-­‐defined ì 
Problems: 1. 
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Not all intelligent behavior is mediated by logical deliberaLon What is the purpose of thinking? What thoughts should I have? Acting rationally: rational agent Agent: entity that perceives and acts Rational behavior:
choose behavior that
maximize goal achievement, given the available
information
Agent-­‐Based AI Agent Design ì 
What can the agent do? ì 
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What is the environment? (Input: percepts) ì 
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How is it interpreted? What does the agent know? ì 
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Range of ac5ons History of previous inputs and acLons (how far back?) ProperLes of environment: world knowledge Knowledge of its own goals and preferences Strategies for behavior How does the agent choose to act? ì 
Mapping from percept sequence -­‐> acLon called an agent func5on Example: Vacuum Cleaner World What are the ac5ons? What are the percepts? Kinds of Agents: Simple Reflex Agent Kinds of Agents: Model-­‐Based Agent Kinds of Agents: Goal-­‐Based Agent Kinds of Agents: Utility-­‐Based Agent Kinds of Agents: Learning Agent More Applications ì Robotics hPp://www.cs.northwestern.edu/~ian/videos/pit.mov Computer Vision hPps://www.youtube.com/watch?v=LdQw8PSV2P8 Machine Translation One final example hPps://www.youtube.com/watch?v=lI-­‐M7O_bRNg