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CS 4700: Foundations of Artificial Intelligence. Carla P. Gomes gomes@cs.cornell.edu http://www.cs.cornell.edu/Courses/cs4700/2008fa/ Module: Introduction (Reading R&N: Chapter 1). Overview of this Lecture. Course Administration What is Artificial Intelligence?

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CS 4700:Foundations of Artificial Intelligence

Carla P. Gomes

gomes@cs.cornell.edu

http://www.cs.cornell.edu/Courses/cs4700/2008fa/Module:

Introduction

(Reading R&N: Chapter 1)


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Overview of this Lecture

  • Course Administration

  • What is Artificial Intelligence?

  • Course Themes, Goals, and Syllabus



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CS 4700:Foundations of Artificial Intelligence

Lectures: Monday, Wednesday, Friday 1:115 – 12:05

Location: Phillips Hall, room 101

Lecturer: Prof. Gomes

Office: 5133 Upson Hall

Phone: 255 9189

Email: gomes@cs.cornell.edu

Administrative Assistant: Kelly Duby

Kelly Duby  <kduby@cs.cornell.edu>

     4105 Upson Hall, 255-0980

Web Site:http://www.cs.cornell.edu/Courses/cs4700/2008fa/


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CS 4700:Foundations of Artificial Intelligence

Head Teaching Assistants

Yunsong Guo guoys @cs.cornell.edu

Anton Morozov  amoroz @cs.cornell.edu

Teaching Assistants

Clayton Chang cc843 @cornell.edu

Sean Sullivan sps27 @cornell.edu

Web Site:http://www.cs.cornell.edu/Courses/cs4700/2008fa/


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Office Hours

  • Prof. Gomes:

  • Office: 5133 Upson Hall

  • I prefer to meet during my scheduled office hours, however,

  • if you need to meet with me at a different time please

  • schedule an appointment by email.

  • TAs - TBA

Fridays: 1:15p.m – 2:15 p.m. (starting next week)


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Grades

Midterm (15%)

Homework                     (45%)

Participation                   (5%)

Final                               (35%)


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Homework

  • Homework is very important. It is the best way for you to learn the

  • material. You are encouraged to discuss the problems with your

  • classmates, but all work handed in should be original, written by you in

  • your own words.

  • Assignments turned in late will drop 5 points for each period of 24

  • hours for which the assignment is late. In addition, no assignments

  • will be accepted after the solutions have been made available.No late

  • homework will be accepted


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Mailing List

  • cs4700ta-l@lists.cs.cornell.edu.

  • Contact us by using this mailing list.  The list is set to mail all

  • the TA's and Prof. Gomes -- you will get the best response

  • time by using this facility, and all the TA's will know the

  • question you asked and the answers you receive.


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CS 4701:Practicum in Artificial Intelligence (Optional)

  • CS4701 Project  (Optional)

  • CS4700 is a co-requisite for CS473. 

  • There will be an organizational meeting in Hollister Hall room 110 on Tuesday, September 2nd at  3:35pm.

  • The main assignment for CS4701 is a course project. Students will work in groups (probably pairs).  A project proposal is required.  A separate project handout with project suggestions, details, and due dates regarding the project proposal,  and final project write-up will be made available from the course home page.

  • Grading CS4701

  • 20%: Project proposal

  • 80%: Final code, write-up, and presentation


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Required

Textbook

Artificial Intelligence: A Modern Approach (AIMA)

(Second Edition) by Stuart Russell and Peter Norvig

Artificial Intelligence : A New Synthesis

By Nils Nilsson


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Lecture notes and reading material

http://www.cs.cornell.edu/Courses/cs4700/2008fa/

Optional

reading material


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Welcome to this class!

  • We will work together throughout this semester.

  • Questions and suggestions are welcome anytime.

    • E.g., if you find anything incorrect or unclear, send an email or talk to me.

  • Any questions?


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Overview of this Lecture

  • Course Administration

  • What is Artificial Intelligence?

  • Course Themes, Goals, and Syllabus


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AI: Goals

  • Ambitious goals:

    • understand “intelligent” behavior

    • build “intelligent” agents


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What is Intelligence?

  • Intelligence:

    • “the capacity to learn and solve problems”

      (Webster dictionary)

    • the ability to act rationally


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What is AI?

  • Views of AI fall into four different perspectives:

    • Thinking versus Acting

    • Human versus Rational

Human-like

Intelligence

“Ideal” Intelligent/

Rationally

Thought/

Reasoning

Behavior/

Actions


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Human Thinking

Human Acting

Rational Thinking

Rational Acting

Different AI Perspectives

2. Systems that think like humans

3. Systems that think rationally

1. Systems that act like humans

4. Systems that act rationally


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1. Acting Humanly

Human-like

Intelligence

“Ideal” Intelligent/

Rationally

Thought/

Reasoning

Behavior/

Actions


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Alan Turing

In 1936, Alan Turing, a British mathematician, showed that there exists a relatively simple universal computing device that can perform any computational process.

Computers use such a universal model.

Turing Machine

(abstraction)

Turing also showed the limits of computation – some problems cannot be computed even with the most powerful computer and even with unlimited amount of time – e.g., Halting problem.


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Acting humanly: Turing Test

Alan Turing

  • Turing (1950) "Computing machinery and intelligence":

  • "Can machines think?“ Instead, "Can machines behave intelligently?"

    • Operational test for intelligent behavior: the Imitation Game

  • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes

  • Anticipated all major arguments against AI in following 50 years

  • Suggested major components of AI

AI system passes

if interrogator

cannot tell which one

is the machine

(interaction via written questions)


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Acting humanly: Turing Test

Turing test  identified key research areas in AI:

  • Natural Language Processing – to communicate with the machine;

  • Knowledge Representation – to store and manipulate information;

  • Automated reasoning – to use the stored information to answer questions and draw new conclusions;

  • Machine Learning – to adapt to new circumstances and to detect and extrapolate patterns.

but does a machine need to act humanly

to be considered intelligent?


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Some Famous Imitation Games

  • 1960s ELIZA Joseph Weizenbaum

    • Rogerian psychotherapist

  • 1990s ALICE

  • Loebner prize

    • win $100,000 if you pass the test


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ELIZA: impersonating a Rogerian psychotherapist

1960s ELIZA Joseph Weizenbaum

http://www-ai.ijs.si/eliza/eliza.html

Eliza:

a friend you could never have before

Eliza: Hello. I am ELIZA. How can I help you?

You: Well I feel sad

Eliza: Do you often feel sad?

You: not very often, but it's becoming more common

Eliza: Please go on.


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Chat Bot – ALICE(AI Foundation)

  • Human: hi my name is CarlaALICE: How are you doing, Carla.Human: what is your name?ALICE: My name is ALICE.Human: what is your full name?ALICE: ALICE = Artificial Linguistic Internet Computer Entity.Human: when were you born?ALICE: I was connected to the net on November 23, 1995.

  • Human: Have you passed the Loebner Prize?ALICE: The Loebner Prize is an annual Turing Test sponsored by New York philanthropist Hugh Loebner. Dr. Wallace and ALICE won it in 2000. The ALICE program was ranked "most human computer" by the panel of judges.

  • Human: who is Dr. Wallace?ALICE: He is a famous computer scientist, the author of ALICE, and a graduate of Carnegie Mellon.

  • Human: Where does Dr. Wallace work?ALICE: he works from his home in San Francisco.


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The Loebner contest

  • A modern version of the Turing Test, held annually, with a $100,000 cash prize.

  • Hugh Loebner was once director of UMBC’s Academic Computing Services (née UCS)

  • http://www.loebner.net/Prizef/loebner-prize.html

  • Restricted topic (removed in 1995) and limited time.

  • Participants include a set of humans and a set of computers and a set of judges.

  • Scoring

    • Rank from least human to most human.

    • Highest median rank wins $2000.

    • If better than a human, win $100,000. (Nobody yet…)


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2. Thinking Humanly

Human-like

Intelligence

“Ideal” Intelligent/

Rationally

Thought/

Reasoning

Behavior/

Actions


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Thinking humanly: modeling cognitive processes

  • Requires scientific theories of internal activities of the brain;

  • 1) Cognitive Science (top-down) : computer models + experimental techniques from psychology

  •  Predicting and testing behavior of human subjects

  • 2) Cognitive Neuroscience (bottom-up)

    •  Direct identification from neurological data

Both approaches are now distinct from AI

1960s "cognitive revolution": information-processing psychology


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3. Thinking Rationally

Human-like

Intelligence

“Ideal” Intelligent/

Rationally

Thought/

Reasoning

Behavior/

Actions


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Thinking rationally: formalizing the "laws of thought“

  • Logic  Making the right inferences! Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts;

  • Aristotle: what are correct arguments/thought processes? (characterization of “right thinking”);

  • Socrates is a man

    All men are mortal

    --------------------------

    Therefore Socrates is mortal

  • More contemporary logicians (e.g. Boole, Frege, Tarski) 

  • Direct line through mathematics and philosophy to modern AI

  • Limitations::

    • Not all intelligent behavior is mediated by logical deliberation

    • What is the purpose of thinking? What thoughts should I have?


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Course

Perspective

4. Acting Rationally

Human-like

Intelligence

“Ideal” Intelligent/

Rationally

Thought/

Reasoning

Behavior/

Actions


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Acting rationally: rational agent

  • Rational behavior: doing the right thing

  • The right thing: that which is expected to maximize goal achievement, given the available information

  • Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action


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Rational agents

  • An agent is an entity that perceives and acts

  • This course is about designing rational agents

  • Abstractly, an agent is a function from percept histories to actions:

  • [f: P*A]

  • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance

  • Caveat: computational limitations make perfect rationality unachievable

     design best program for given machine resources


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Focus of CS4700 (most recent progress).

Building Intelligent Machines

  • I Building exact models of human cognition

    view from psychology and cognitive science

  • II Developing methods to match or exceed human

  • performance in certain domains, possibly by

  • very different means  e.g., Deep Blue;


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Methodology of AI

  • Theoretical aspects

    • Mathematical formalizations, properties, algorithms

  • Engineering aspects

    • The act of building (useful) machines

  • Empirical science

    • Experiments


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CS4700

What's involved in Intelligence?

  • A) Ability to interact with the real world

    to perceive, understand, and act

    speech recognition and understanding

    image understanding (computer vision)

  • B) Reasoning and Planning

    modelling the external world

    problem solving, planning, and decision making

    ability to deal with unexpected problems, uncertainties

  • C) Learning and Adaptation

  • We are continuously learning and adapting.

    We want systems that adapt to us!



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AI Leverages from different disciplines

  • Philosophy

  • e.g., foundational issues in logic, methods of reasoning,

  • mind as physical system, foundations of learning,

  • language, rationality

  • Computer science and engineering

  • e.g., complexity theory, algorithms, logic and inference,

  • programming languages, and system building (hardware

  • and software).

  • Mathematics and physics

  • e.g., probability theory, statistical modeling, continuous mathematics,

  • Markov models, statistical physics, and complex systems.

  • and others, e.g., cognitive science, neuroscience, economics, psychology, linguistics,


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AI More direct Influence

  • Obtaining an understanding of the human mind is one of the

  • final frontiers of modern science.

  • George Boole, Gottlob Frege, and Alfred Tarski

    formalizing the laws of human thought

  • Alan Turing, John von Neumann, and Claude Shannon

  • thinking as computation

  • Direct Founders:

  • John McCarthy, Marvin Minsky, Herbert Simon, and Allen Newell

    the start of the field of AI (1959)


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History of AI:MilestonesThe gestation of AI 1943-1956

  • 1943 : McCulloch and Pitts

    • McCulloch and Pitts’s model of artificial neurons

    • Minsky’s 40-neuron network

  • 1950 : Turing’s “Computing machinery and intelligence”

  • 1950s Early AI programs, including Samuel’s checkers program, Newell and Simon’s Logic theorist

  • 1956 Dartmouth meeting : Birth of “Artificial Intelligence”

    • A 2-month Dartmouth workshop of 10 attendees – the name of AI

    • Newell and Simon’s Logic Theorist

    • Do you think AI is a god name?


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Perceptrons Early neural nets

More about

Neural Nets

later in the course…


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History of AILook, Ma, no hands !(1952-1969)Early enthusiasm, great expectations

  • 1957 Herb Simon:

    It is not my aim to surprise or shock you – but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover their ability to do these things is going to increase rapidly until – in the visible future – the range of problems that they can handle will be coextensive with the range to which human mind has been applied.

  • 1958 : John McCarthy’s LISP

  • 1965 : J.A. Robinson invents the resolution principle, basis for automated theorem

  • Intelligent reasoning in Microworlds (such as Block’s world)


The block s world l.jpg

A

D

D

A

B

C

C

T

Initial State

Goal State

The Block’s world


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History of AIA dose of reality (1966-1978)

  • 1965 : Weizenbaum’s ELIZA

  • Difficulties in automated translation (try http://babelfish.yahoo.com/)

  • Syntax is not enough

  • “the spirit is willing but the flesh is weak”

    “the vodka is good but the meat is rotten”

  • Limitations of Perceptrons discovered

  •  can only represent linearly separable functions

    Neural network research almost disappears

  • NP-Completeness (Cook 72)

  • Intractability of the problems attempted by AI,

  • Worst- case result….


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History of AIKnowledge based systems (1969-79)

  • Intelligence requires knowledge - Knowledge based systems as opposed to weak methods (general-purpose search methods)

     Expert Systems,

    E.g.:

    • Mycin : diagnose blood infections

    • R1 : configuring computer systems


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History of AIAI becomes industry (1980-88)

  • Expert systems

  • Lisp-machines

  • Return of Neural Nets

 End of 80’s – limitations of expert systems became clear, even though they have been quite successful in certain domains.


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History of AI:2000-AI is Alive and Kicking

  • Current work on “intelligent agents”:

  • Emphasis on integration of reasoning (search and inference as well as probabilistic reasoning), knowledge representation, and learning techniques

  • AI as a science: Combining theoretical and empirical analysis

  • Mathematical sophistication of AI techniques

AAAI08

Key challenge:

building flexible and scalable AI

systemsin the Open World

.

  • “… A better understanding of the problems and their complexity properties,

  • combined with increased mathematical sophistication,

  • has led to workable research agendas and robust methods” R&N.


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AI Achievements

A few recent examples…


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1996 - EQP:

Robbin’s Algebras are all boolean

A mathematical conjecture (Robbins conjecture) unsolved for 60 years!

The Robbins problem was to determine whether one particular set of rules is powerful enough to capture all of the laws of Boolean algebra. One way to state the Robbins problem in mathematical terms is:

Can the equation not(not(P))=P be derived from the following three equations?

[1] P or Q = Q or P,

[2] (P or Q) or R = P or (Q or R),

[3] not(not(P or Q) or not(P or not(Q))) = P.

First creative mathematical

proof by computer:

unlike brute-force based proofs

such as the 4-color theorem.

[An Argonne lab program] has come up with a major mathematical

proof that would have been called creative if a human had thought of it.

New York Times, December, 1996

http://www-unix.mcs.anl.gov/~mccune/papers/robbins/


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Microsoft Office’97 + Answer Wizard

  • Diagnosis reasoning using Bayesian Models

  • Restricted NLP


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1997:

Deep Blue beats the World Chess Champion

vs.

I could feel human-level intelligence across the room

-Gary Kasparov, World Chess Champion (human…)


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Deep Blue vs. Kasparov

IBM Stock price skyrocketed

on the day Deep Blue beat Kasparov

Game 1: 5/3/97: Kasparov wins

Game 2: 5/4/97:Deep Blue wins

Game 3: 5/6/97:Draw

Game 4: 5/7/97:Draw

Game 5: 5/10/97: Draw

Game 6: 5/11/97:Deep Blue wins

“I felt a new kind of

Intelligence” ( across

the board from him)

Kasparov 1997

The value of IBM’s stock

Increased by $18 Billion!

One of the most famous modern computers,

Deep Blue, which defeated Gary Kasparov at chess.


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1999: Remote Agent takes Deep Space 1 on a galactic ride

For two days in May, 1999, an AI Program called Remote Agent

autonomouslyran Deep Space 1 (some 60,000,000 miles from earth)


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Remote Agent:1999 Winner of NASA's Software of the Year Award

It's one small step in the history of space flight. But it was one giant leap for computer-kind, with a state of the art artificial intelligence system

being given primary command of a spacecraft. Known as Remote Agent,

the software operated NASA's Deep Space 1 spacecraft and its futuristic ion engine during two experiments that started on Monday, May 17, 1999.

For two days Remote Agent ran on the on-board computer of Deep Space 1,

more than 60,000,000 miles (96,500,000 kilometers) from Earth.

The tests were a step toward robotic explorers of the 21st century that are

less costly, more capable and more independent from ground control.

http://ic.arc.nasa.gov/projects/remote-agent/index.html


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Proverb 1999: Solving Crossword Puzzles as Probabilistic Constraint Satisfaction

  • Proverb solves

  • crossword puzzles

  • better than most humans

Michael Littman et a. 99


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2000: SCIFINANCE synthesizes programs for financial modeling

  • Develop pricing models for complex derivative structures

  • Involves the solution of a set of PDEs (partial differential equations)

  • Integration of object-oriented design, symbolic algebra, and plan-based scheduling


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Robocup @ Cornell1999

http://www.mae.cornell.edu/raff/MultiAgentSystems/MultiAgentSystems.htm

Raff D’andrea


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From Robocup to Warehouse Automation

First user of system

Raff D’Andrea


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Machine learning successes

Source: R. Greiner


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Machine learning successes

Source: R. Greiner


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Machine learning successes

Source: R. Greiner


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2005 Autonomous Control:DARPA GRAND CHALLENGE

October 9, 2005

Stanley and the Stanford RacingTeam

were awarded 2 million dollars for being the

first team to complete the 132 mile

DARPA Grand Challenge course (Mojave Desert).

Stanley finished in just under 6 hours 54 minutes

and averaged over 19 miles per hours on the course.



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2007 Darpa Urban ChallengeWinner: CMU Tartan Racing's Boss

  • http://www.tartanracing.org/blog/index.html#26


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The DARPA Urban Challenge is being held at the former George Air Force Base.

The old base buildings are abandoned now and the Marines use the area to train for

urban missions.



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Main annual AI conference: Air Force Base.

AAAI

Association for

Advancement of AI


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Association for Advancement of Artificial Intelligence Air Force Base.(AAAI)AI Topics

http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/HomePage


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Goals for this course Air Force Base.


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Setting expectations for this course Air Force Base.

  • Are we going to build real systems and robots?

NO!!!

Goal:

Introduce the theoretical and computational techniques that serve as a foundation for the study of artificial intelligence (AI).


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Syllabus Air Force Base.

  • Structure of intelligent agents and environments.

  • Problem solving by search: principles of search, uninformed (“blind”) search, informed (“heuristic”) search, and local search.

  • Constraint satisfaction problems: definition, search and inference, and study of structure.

  • Adversarial search: games, optimal strategies, imperfect, real-time decisions.

  • Logical agents: propositional and first order logic, knowledge bases and inference.

  • Uncertainty and probabilistic reasoning: probability concepts, Bayesian networks,  probabilistic reasoning over time, and decision making

  • Learning: inductive learning, concept formation, decision tree learning, statistical approaches, neural networks, reinforcement learning


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Notes Air Force Base.

  • The syllabus is quite ambitious: some of the topics may only be covered

  • briefly, depending on time.

  • Detailed reading information (chapters and sections of R&N) will be

  • provided in the lectures notes and homework assignments.

  • This is not a machine learning course: we will only cover some

  • introductory material learning topics  if you are looking for a machine

  • learning course, here is a specialized machine learning course offered this

  • fall:

  • CS4782 - Probabilistic Graphical Models.


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Summary Air Force Base.

  • Artificial Intelligence and characteristics of intelligent systems.

  • Brief history of AI

  • Examples of AI achievements

  • Computers are getting smarter !!!

Reading: Chapter 1 Russell & Norvig


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