Download What is Artificial Intelligence? • Meet ELIZA • Written between 1964-1966

What is Artificial Intelligence?
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• Meet ELIZA
• Written between 1964-1966
• At MIT by Joseph Weizenbaum
Artificial Intelligence
– What Is Artificial Intelligence?
– Why Study AI?
– Fields in AI
– Expert Systems
– Machine Learning
– Natural Language Processing
– Vision & Perception
– Robotics
– Decision Trees
– Summary
!
A * on slide title means it contains
something examinable
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Is ELIZA Intelligent?
* What is Artificial Intelligence?
• No?
• Asks silly questions
• Yes?
• Processes Human Text
• Does what a physiologist does
• What is Artificial Intelligence?
• Human-like Intelligence?
!“What is intelligence, anyway? It is only a word that people use to name
those unknown processes with which our brains solve problems we call
hard. But whenever you learn a skill yourself, you are less impressed or
mystified when other people do the same.
!
This is why the meaning of “intelligence” seems so elusive: It describes not
some definite thing but only the momentary horizon of our ignorance about
how minds might work.”
- Marvin Minsky
The exciting new effort to make
computers think … Machines with Minds
- Haugeland 1985
The study of how to make computers do
things at which, at the moment, people
are better
- Rich and Knight, 1991
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The art of creating machines
that perform functions that
require intelligence when
performed by people
- Kurzweil, 1990
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* Turing Test
Alan Turing (1912−1954)
In 1950, Turing published a paper defining intelligence by imagining
a test, called the Turing test
Turing, considered by many to be the father of
computer science, was a brilliant
cryptographer and provided much of the
mathematical basis for cracking the German
Enigma codes during WWII.
In this test, an interrogator sits alone in room and types questions
into a computer terminal. The questions can be about anythingmaths, science, politics, sports, art, entertainment, emotions anything. The interrogator tries to
guess whether those answers came
from a person or a computer on the
other end of the wire
Despite being largely responsible for saving
thousands of Allied lives he was later
persecuted by the British authorities for his
homosexuality and committed suicide in
1954, aged just 41.
He chose to enact a Snow White death,
eating an apple laced with cyanide (Steve
Jobs denied that the Apple logo is in homage
to Turing, but it makes a great story).
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Source of
images:
Wikipedia
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If a computer could answer well
enough that the interrogator cannot
tell whether there is a person or a
machine answering, the machine is
said to be intelligent
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Is Human-like Intelligence Enough?
* What is Artificial Intelligence?
• Deep Blue
• There are many definitions
• The debate still rages
• Systems that Act Rationally
• A system that usually makes a sensible action
• Could be the action a human makes
• But humans sometimes make mistakes
• Waston
• Self-Driving Cars
• Robotic Soccer
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AI … is concerned with intelligent behaviour in artefacts
- Nilsson, 1998
The study of computations that make it possible to perceive, reason and act
- Winston, 1992
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* Why Study AI?
* Major Areas of AI
Why would we want artificial intelligence?
• Perform computationally difficult tasks
• Reduce Human Errors
• Make Jobs safer
• Make machines easier to use
• Study the human mind or intelligence
• Amplify our own intelligence
There are many areas of study that come under the
general heading of AI:
• Knowledge Representation and Reasoning
• Search and Planning
• Machine Learning
• Natural Language Processing
• Vision and Perception
• Robotics
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* Knowledge Representation and Reasoning
* Knowledge Representation and Reasoning
Information needs to be represented in a formation
the computer can understand so that an AI system
can reason (or make decisions) about it.
The is an important and challenging part of AI.
How would you represent?
A Spreadsheet?
X
O
O
An Image?
An Object?
X
The Rules of a Game?
(like Tic-Tac-Toe)
First Order Logic can be processed quickly by
computers
• Facts are expressed by True/False atoms
occupied(x, 1, 1)
• Rules state what can and cannot be true
occupied(x,1,1) and … => 3_in_row
3_in_row => win
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!
More complex logic languages can give even more
powerful expressions.
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* Knowledge Representation and Reasoning
* Search and Planning
Logic is hard for humans to
read - but easy for computers.
Given a set of facts and rules, how can you find or
know the what to do?
An AI system needs to search for the right answer,
or plan a sequence of actions (or moves) to solve
the problem (or win a game).
X
!
O
O
X
“Blind” Tic-tac-Toe in GDL-II
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2
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role(xplayer).
role(oplayer).
init(control(xplayer)).
init(cell(1,1,b)).
...
init(cell(3,3,b)).
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legal(R,mark(M,N))
:- true(control(R)),
true(cell(M,N,Z)),
not true(tried(M,N)).
legal(xplayer,noop) :- true(control(oplayer)).
legal(oplayer,noop) :- true(control(xplayer)).
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validmove :- does(R,mark(M,N)), true(cell(M,N,b)).
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next(F)
next(tried(M,N))
next(cell(M,N,x)):- validmove, does(xplayer,mark(M,N)).
next(cell(M,N,o)):- validmove, does(oplayer,mark(M,N)).
next(cell(M,N,Z)):- validmove, true(cell(M,N,Z)),
does(P,mark(I,J)), distinct(M,I).
next(cell(M,N,Z)):- validmove, true(cell(M,N,Z)),
does(P,mark(I,J)), distinct(N,J).
next(control(oplayer)):- validmove,
true(control(xplayer)).
next(control(xplayer)):- validmove,
true(control(oplayer)).
:- not validmove, true(F).
:- not validmove, does(P,mark(M,N)).
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sees(R,yourmove)
:- not validmove,
true(control(R)).
sees(xplayer,yourmove) :- validmove,
true(control(oplayer)).
sees(oplayer,yourmove) :- validmove,
true(control(xplayer)).
Figure 1: A GDL-II description of “Krieg-Tictactoe.” The game positions are encoded using the three features control(R),
where R ∈ {xplayer, oplayer}; cell(M,
N, Z), where M, N ∈ {1, 2, 3} and Z ∈ {x, o, b}, with b meaning “blank;” and
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tried(M, N ). For the sake of simplicity, we have omitted the (straightforward) specification of termination and goal values.
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13s1 of this sort is the domain of acDrawing conclusions
tion theories, and the Situation Calculus is the oldest technique for formalising and automating reasoning about actions (McCarthy 1963). To be able to exploit this line of
research in the context of General Game Playing requires
to fully embed the game description language GDL-II into
the Situation Calculus. The mapping that we will develop in
this section is based on an extended Situation Calculus with
a special fluent to represent the knowledge of agents (Scherl
and Levesque 2003). We will have to slightly modify and
further extend this approach for our purposes. In general,
the Situation Calculus is a predicate logic with a few predefined language elements:
• constant s0 , denoting the initial situation, and construcMAX (X)
tor D O(α, σ), denoting the situation resulting from doing
action α in situation σ;
• predicate H OLDS(ϕ, σ), denoting that fluent ϕ (i.e., an
atomic state feature) is true in situation σ;
• predicate P OSS(α, σ), denoting that action α is possible
X
X
X
in situation
σ.
MIN (O)
X
Games
Derived action predicates
Compound actions
In games with two or more players, a position update results
from all players moving simultaneously. An adequate formalisation in the Situation Calculus requires to identify the
X α with
X
X O
O a vector
. .k.⟩ conconcept
of an action
⟨m1 , . . . , m
MAX (X) taining one move for each player.OIn a given GDL-II description, the domain of moves is implicitly determined by the
arguments of keyword legal and the second arguments
of does; e.g. mark(M, N ) and noop in Krieg-Tictactoe.
Assuming an arbitrary but fixed order of the players, as in
X Ooplayer),
X
XweOdefine a simple
X O axiom that. identifies
(xplayer,
..
MIN (O) the individual moveXof a player r inXan action vector:
We now show how any GDL-II description G can be
mapped in a modular way into a Situation Calculus theory.
First, some atoms that occur in G are rewritten as follows.
⃗ are replaced by f (X,
⃗ S) and
1. All derived fluents f (X)
⃗ by p(X,
⃗ A, S), indiall derived action predicates p(X)
cating the dependence on a situation S and an action A,
respectively.
...
...
TERMINAL
X O X
O X
O
...
Search tree becomes very large, very quickly
• Tic-Tac-Toe ≈ 10^5
• Chess ≈ 10^123
• Backgammon ≈ 10^144
• Go (19x19) ≈ 10^360
• (There are 10^80 atoms in the universe)
!
Searching the tree efficiently is a major AI problem
4. Each next(ϕ) is replaced by H OLDS(ϕ, Do(A, S)).
5. Each does(ϱ, µ) is replaced by ACT(ϱ, A) = µ.
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X O X
O O X
X X O
Search and Planning
3. Each true(ϕ) is replaced by H OLDS(ϕ, S).
E.g., ACT(xplayer, ⟨mark(1, 3), noop⟩) = mark(1, 3).
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2. Each init(ϕ) is replaced by H OLDS(ϕ, s0 ).
(1)
ACT(r, ⟨m1 , . . . , mr , . . . , mk ⟩) = mr
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Given a GDL-II game description, we identify as primitive
fluents those terms that occur in the scope of either of the
keywords init(F), true(F), or next(F); in Fig. 1
these are control(R), cell(M, N, Z), and tried(M, N ). As
derived fluents we take all domain-specific predicates that
depend on true but not on does. Following (Davis
1990), derived fluents do not require their own successor
state axioms because their truth-values are fully determined
by the game rules once the values of all primitive fluents are
fixed in a (successor) situation. The keywords terminal
and goal(R,N) are treated as derived fluents, too.
In addition to derived fluents, a mapping of GDL-II into
the Situation Calculus requires the introduction of the new
concept of a derived action predicate. These are the domainspecific predicates that depend on both true and does.
Similar to derived fluents, the truth-value of a derived action
predicate is fully determined by the game rules once we have
fixed both the values of all primitive fluents in a situation and
the action that is being taken in that situation. An example of
X a derived action
X predicate in Krieg-Tictactoe is validmove.
X
X
The mapping X
* Search
Planning deterministic)
Game
Treeand
(2-player,
Let’s play tic-tac-toe
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X O X
X
X O O
...
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* Machine Learning
* Machine Learning - Reinforcement Learning
Rather than searching for a solution, just “know” the
answer.
Machine Learning teaches or trains a computer so
that it can solve a task (or know what move to make)
instantly.
Types of Machine Learning include:
• Reinforcement Learning
• Neural Networks
• Classifiers
• … and more
Like teaching tricks to your pet.
Classic problem of Pole-and-Cart
• Try to balance the pole
• Reward or successful behaviour
• Punish if the pole falls down
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* Natural Language Processing
* Vision and Perception
Allowing computers to understand and produce
meaning from naturally spoken or written language.
“Natural” means as you would talk to another person
who is fluent in the same language. Talking without
using special commands or “dumbed-down”
language.
Interpret and find objects within an Image.
A computer sees an image as an array of numbers,
and computer vision finds “meaning” within those
numbers.
Challenges with vision
• Lighting
• Inconsistent colour
• Occlusion
• Distortion
• Blur
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* Robotics
Robotics - Decision Trees
Incorporates many aspects of other fields of AI.
In AI, we aim to get robots to solve tasks:
• Independently - without human interference
• Intelligently - react to changes in the world
• In Real Time - the robot can’t sit and think
Simple DT in soccer to kick a ball. We have three
variables:
• can_see (true or false) - can the robot see the
ball?
• ball_dist (double) : distance ball is from the
robot
• ball_x (double) : x-position of the ball relative
to the robot
!
One of the biggest challenges however, is devising
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Kick Left Foot
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AI studies how to make computers act rationally. As
technology progress, AI becomes more prominent
!
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Look for ball
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AI systems are still limited by constraints.
Understanding how AI systems are built lets humans
work effectively with them.
!
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Summary
We have barely scratched the surface of how AI
systems are built and what they are capable of.
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