13897_1.AI Introduction(Lecture 1-5)

CHANDRA PRAKASH

ASSISTANT PROFESSOR

LPU

Artificial Intelligence

References

Artificial Intelligence by Elaine Rich & Kevin

Knight, Third Ed, Tata McGraw Hill

P. H.Winston, “Artificial Intelligence”

D.W.Patterson, Introduction to AI & Expert Systems, Prentice Hall.

Chandra Prakash , LPU

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If human beings can think then why not machines?

If machines If machines

can think, How? can not think, Why?

Can they surpass human And what does this performance? say about the mind?

Chandra Prakash , LPU 3

Artificial + Intelligent


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What is artificial intelligence?

Intelligence (Oxford dictionary ):

Ability to • Learn• Understand and• Think.


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Artificial :o fake, not real , man made

Intelligence: “the capacity to learn and solve problems” in particular,

the ability to solve novel problems the ability to act rationally the ability to act like humans

Artificial + Intelligence

INTELLIGENCE

Intelligence is the computational part of the ability to achieve goals in the world.

Varying kinds and degrees of intelligence occur in people, many animals and some machines.

AI is the study of how to make computers make things which at the moment people do better.

Examples: Speech recognition, Smell, Face, Object, Intuition, Inferencing, Learning new skills, Decision making, Abstract thinking


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Ability to interact with the real world to perceive, understand, and act e.g., speech recognition and understanding and synthesis e.g., image understanding e.g., ability to take actions, have an effect

Reasoning and Planning modeling the external world, given input solving new problems, planning, and making decisions ability to deal with unexpected problems, uncertainties

Learning and Adaptation we are continuously learning and adapting our internal models are always being “updated”

e.g., a baby learning to categorize and recognize animals

What is involved in INTELLIGENCE

ARTIFICIAL INTELLIGENCE

There are no clear agreement on the definition of AI

It is the science and engineering of making intelligent machines, especially intelligent computer programs.

It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

AI is the study of how to make computers just like humans. That means how to make computers to do things that people do better.


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Other possible AI definitions

• AI is a collection of hard problems which can be solved by humans and other living things, but for which we don’t have good algorithms for solving.

– e. g., understanding spoken natural language, medical diagnosis, circuit design, learning, self-adaptation, reasoning, chess playing, proving math theories, etc.

AI is a process of making a machine or a program that– Learn and understand like human – Acts like human (Turing test)– Thinks like human (human-like patterns of thinking steps)– Acts or thinks rationally (logically, correctly)


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Cont…

Some problems used to be thought of as AI but are now considered not

– e. g., compiling Fortran(suited to numeric computation and scientific computing) in 1955,

– symbolic mathematics (manipulate mathematical equations ) in 1965

– proving math theories


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Cont…

AI is the study and design of intelligent agents

where, an intelligent agent is a system that interact

with its environment and takes actions that maximize its chances of success.


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Problems In AI

Easy Problems in AI• It’s been easier to mechanize many of the high level cognitive tasks

we usually associate with “intelligence” in people– e. g., symbolic integration, proving theorems, playing chess,

some aspect of medical diagnosis, etc.Hard Problems in AI

• It’s been very hard to mechanize tasks that animals can do easily– walking around without running into things (ASIMO)– interpreting complex sensory information (visual, aural, …)– working as a team (ants, bees)


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ASIMO (2000) at the Expo 2005


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Humanoid robot created by Honda. Standing at 130 centi-meters (4 feet 3 inches) and weighing 54 kilograms

Kismet now resides at the MIT Museum inCambridge, Massachusetts

Kismet is a robot made in the late 1990s at Massachusetts Institute of Technology with auditory, visual and expressive systems intended to participate in human social interaction and to demonstrate simulated human emotion and appearance.


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TOPIO, a human robot played table tennis at Tokyo International Robot Exhibition (IREX) 2009

Stanley Robot in Stanford Racing Team

Robot holding the Bulb

Factory Automation with industrial robots

Post Office automatic address recognition and sorting of mail

Banks automatic check readers, signature verification systems automated loan application classification

Customer Service automatic voice recognition

The Web Identifying your age, gender, location, from your Web surfing Automated fraud detection

Digital Cameras Automated face detection and focusing

Computer Games Intelligent characters/agents

Intelligent Systems in Your Everyday Life

Foundations of AI


21ComputerScience &

Engineering

AI

Mathematics

CognitiveScience

Philosophy

Psychology Linguistics

BiologyEconomics

Philosophy :Logic, methods of reasoning, mind as physical system, foundations of learning, language,rationality.

Mathematics :Formal representation and proof, algorithms, computation, (un)decidability, (in)tractability

Probability/Statistics : modeling uncertainty, learning from data Economics : utility, decision theory, rational economic agents Neuroscience : neurons as information processing units. Psychology / Cognitive Science : how do people behave, perceive, process

cognitive information, represent knowledge. Computer : building fast computers engineering Control theory: design systems that maximize an objective function over

time Linguistics : knowledge representation, grammars

Disciplines related to AI

History of AI• AI has roots in a number of scientific disciplines

– computer science and engineering (hardware and software)– philosophy (rules of reasoning)– mathematics (logic, algorithms, optimization)– cognitive science and psychology (modeling high level

human/animal thinking)– neural science (model low level human/animal brain activity)– linguistics

• The birth of AI (1943 – 1956)– Pitts and McCulloch (1943): simplified mathematical model of

neurons (resting/firing states) can realize all propositional logic primitives (can compute all Turing computable functions)

– Allen Turing: Turing machine and Turing test (1950)– Claude Shannon: information theory; possibility of chess playing

computers


• Early enthusiasm (1952 – 1969)– 1956 Dartmouth conference

John McCarthy (Lisp);Marvin Minsky (first neural network machine);Alan Newell and Herbert Simon (GPS);

– Emphasize on intelligent general problem solvingGSP (means-ends analysis);Lisp (AI programming language);Resolution by John Robinson (basis for automatic theorem proving);heuristic search (A*, AO*, game tree search)

• Emphasis on knowledge (1966 – 1974)– domain specific knowledge is the key to overcome existing

difficulties– knowledge representation (KR) paradigms– declarative vs. procedural representation


• Knowledge-based systems (1969 – 1999)– DENDRAL: the first knowledge intensive system (determining

3D structures of complex chemical compounds)– MYCIN: first rule-based expert system (containing 450 rules for

diagnosing blood infectious diseases)EMYCIN: an ES shell

– PROSPECTOR: first knowledge-based system that made significant profit (geological ES for mineral deposits)

• AI became an industry (1980 – 1989)– wide applications in various domains– commercially available tools

• Current trends (1990 – present)– more realistic goals – more practical (application oriented)– distributed AI and intelligent software agents– resurgence of neural networks and emergence of genetic

algorithmsChandra Prakash , LPU 25

Programming languages for AI

The programs for AI problems can be written with on procedural languages like PASCAL or declaration languages like PROLOG.

Generally relational languages like PROLOG or LISP are preferred for symbolic reasoning in AI.

However, if the program requires much arithmetic computation (say for the purpose of uncertainty management), then procedural languages would be preferred.

Recently a number of shell are available, where the user needs to submit knowledge only and the shall offers the implementation of both symbolic

processing simultaneously.


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Speech recognition : converts spoken words to textFace Recognition : a computer application for

automatically identifying or verifying a person from a digital image or a video

Finger print scanningOptical character recognition : electronic translation of

printed text into machine-encoded textHandwriting recognitionMany more……

Applications of AI

Task Domains of AI

Mundane Tasks: Perception

Vision Speech

Natural Languages Understanding Generation Translation

Common sense reasoning Robot Control

Formal Tasks Games : chess, checkers etc Mathematics: Geometry, logic,Proving properties of programs

Expert Tasks: Engineering ( Design, Fault finding, Manufacturing planning) Scientific Analysis Medical Diagnosis Financial Analysis


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Possible Approaches in AI


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Think

Act

Like humans Well

GPS

Eliza

Rationalagents

Heuristicsystems

AI tends to work mostly in this area

Think like humans

Cognitive science approach Focus not just on behavior and I/O

but also look at reasoning process. Computational model should reflect “how” results

were obtained.

GPS (General Problem Solver): Goal not just to produce humanlike behavior (like ELIZA), but to produce a sequence of steps of the reasoning process that was similar to the steps followed by a person in solving the same task.


30 Think

Act

Like humans Well

GPS

Eliza

Rationalagents

Heuristicsystems

Think well

Law of thoughts Develop formal models of knowledge

representation, reasoning, learning, memory, problem solving, that can be result in algorithms.

There is often an emphasis on a systems that are provably correct, and guarantee finding an optimal solution.


31Think

Act

Like humans Well

GPS

Eliza

Rationalagents

Heuristicsystems

Act well

For a given set of inputs, generate an appropriate output that is not necessarily correct but

gets the job done.

A heuristic (heuristic rule, heuristic method) is a rule of thumb, strategy, trick, simplification, or any other kind of device which drastically limits search for solutions in large problem spaces.

Heuristics do not guarantee optimal solutions; in fact, they do not guarantee any solution at all:

All that can be said for a useful heuristic is that it offers solutions which are good enough most of the time.


32Think

Act

Like humans Well

GPS

Eliza

Rationalagents

Heuristicsystems

Act like humans

Behaviorist approach.Not interested in how you get results,

just the similarity to what human results are.

Exemplified by the Turing Test (Alan Turing, 1950).


33Think

Act

Like humans Well

GPS

Eliza

Rationalagents

Heuristicsystems

Acting Humanly: The Turing Test

Alan Turing (1912-1954)

“Computing Machinery and Intelligence”

(1950)


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Human Interrogator

Human

AI System

Imitation Game

Turing Test(cont..)

Three rooms contain a person, a computer, and an interrogator.

The interrogator can communicate with the other two by teleprinter.

The interrogator tries to determine which is the person and which is the machine.

The machine tries to fool the interrogator into believing that it is the person.

If the machine succeeds, then we conclude that the machine can think.


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Turing Test

The "standard interpretation" of the Turing Test, in which player C, the interrogator, is tasked with trying to determine which player - A or B - is a computer and which is a human. The interrogator is limited to using the responses to written questions in order to make the determination.


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Strengths of the test

Tractability

Turing Test provides something that can actually be measured

philosophy of mind, psychology, and modern neuroscience unable to be precise – intelligence & thinking

SimplicityBreadth of subject matter

the format of the test allows the interrogator to give the machine a wide variety of intellectual tasks.

Can include any field during test

Weaknesses of the test

Human intelligence vs intelligence in generalThe Turing test does not directly test whether the

computer behaves intelligently - it tests only whether the computer behaves like a human being.

human behaviour and intelligent behaviour are not same

Some human behaviour is unintelligent

Turing Test even tests for behaviours that we may not consider intelligent at all, such as the susceptibility to insults, the temptation to lie or, simply, a high frequency of typing mistakes.

If a machine cannot imitate human behaviour in detail it fails the test.

Some intelligent behaviour is inhuman

The Turing test does not test for highly intelligent behaviours, such as the ability to solve difficult problems or come up with original insights

if the machine is more intelligent than a human being it must deliberately avoid appearing too intelligent.

If it were to solve a computational problem that is impossible for any human to solve, then the interrogator would know the program is not human, and the machine would fail the test.

Naivete of interrogators

the test's results can easily be dominated not only by the computer's intelligence

Skill of the questioneraverage interrogator would not have more

than 70 per cent chance of making the right identification after five minutes of questioning".

"average interrogator"

Chinese Room argument

Devised by John Searle, It is an argument against the

possibility of true artificial intelligence.

The argument centers on a thought experiment in which someone who knows only English sits alone in a room following English instructions for manipulating strings of Chinese characters, such that to those outside the room it appears as if someone in the room understands Chinese.


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Chinese Room argument

If you can carry on an intelligent conversation using pieces of paper slid under a door, does this imply that someone or something on the other side understands what you are saying?


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Real intelligence vs simulated intelligence

the Turing test is a good operational definition of intelligence, it may not indicate that the machine has a mind, consciousness

behaviour cannot be used to determine if a machine is "actually" thinking or merely "simulating thinking.

Eliza

ELIZA: A program that simulated a psychotherapist interacting with a patient and successfully passed the Turing Test.

Coded at MIT during 1964-1966 by Joel Weizenbaum. First script was DOCTOR.

The script was a simple collection of syntactic patterns not unlike regular expressions

Each pattern had an associated reply which might include bits of the input (after simple transformations (my your)


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The Loebner Contest

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

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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What can AI systems do

Here are some example applications Computer vision: face recognition from a large set Robotics: autonomous (mostly) automobile Natural language processing: simple machine

translation Expert systems: medical diagnosis in a narrow domain Spoken language systems: ~1000 word continuous

speech Planning and scheduling: Hubble Telescope

experiments Learning: text categorization into ~1000 topics Games: Grand Master level in chess (world champion),

checkers, etc.


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What can’t AI systems do yet?

Understand natural language robustly (e.g., read and understand articles in a newspaper)

Surf the webInterpret an arbitrary visual sceneLearn a natural languageConstruct plans in dynamic real-time domainsRefocus attention in complex environmentsPerform life-long learning


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AI Technique

Intelligence requires Knowledge Knowledge posesses less desirable properties such as:

Voluminous Hard to characterize accurately Constantly changing Differs from data that can be used

AI technique is a method that exploits knowledge that should be represented in such a way that: Knowledge captures generalization It can be understood by people who must provide it It can be easily modified to correct errors. It can be used in variety of situations


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Tic Tac Toe

Three programs are presented :Series increase Their complexity Use of generalization Clarity of their knowledge Extensability of their approach


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Introductory Problem: Tic-Tac-Toe


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X X o


Program 1:Data Structures: Board: 9 element vector representing the board, with 1-9 for each

square. An element contains the value 0 if it is blank, 1 if it is filled by X, or 2 if it is filled with a O

Move-Table: A large vector of 19,683 elements ( 3^9), each element is 9-element vector.

Algorithm:

1. View the vector as a ternary number. Convert it to a decimal number.

2. Use the computed number as an index into Move-Table and access the vector stored there.

3. Set the new board to that vector.


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Comments:This program is very efficient in time.

1. A lot of space to store the Move-Table.

2. A lot of work to specify all the entries in the Move-Table.

3. Difficult to extend.

Thus not a good AI Technique


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1 2 3 4 5 6 7 8 9

Introductory Problem: Tic-Tac-ToeProgram 2:Data Structure: A nine element vector representing the board. But

instead of using 0,1 and 2 in each element, we store 2 for blank, 3 for X and 5 for O

Functions:

Make2: returns 5 if the centre square is blank. Else any other blank square

Posswin(p): Returns 0 if the player p cannot win on his next move; otherwise it returns the number of the square that constitutes a winning move. If the product is 18 (3x3x2), then X can win. If the product is 50 ( 5x5x2) then O can win.

Go(n): Makes a move in the square n

Strategy:

Turn = 1 Go(1)Turn = 2 If Board[5] is blank, Go(5), else Go(1)Turn = 3 If Board[9] is blank, Go(9), else Go(3)Turn = 4 If Posswin(X) 0, then Go(Posswin(X)).......


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Comments:

1. Not efficient in time, as it has to check several conditions before making each move.

2. Easier to understand the program’s strategy.

3. Any bug in programmer tic-tac-toe playing skill will show up in program’s play.

3. Hard to generalize.


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8 3 4 1 5 9 6 7 215 (8 + 5)

New appraoch

All row, column and diagonal sum is 15 Make a list, for each player , of the squares in which he/she

has played. Consider each pair of square owned by that player Computer difference between 15 and sum of two square. If

difference is -ve or if greater then 9, then the original two square were not collinear and thus can be ignored.

If square representing the difference is blank, a move there will produce a win.

No player cant have more then 4 square at a time, so fewer

square to compared to.


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Comments:

1. Checking for a possible win is quicker.

2. Human finds the row-scan approach easier, whilecomputer finds the number-counting approach more efficient.


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Introductory Problem: Question Answering

“Mary went shopping for a new coat. She found a red

one she really liked. When she got it home, she discovered that it went perfectly with her favourite dress”.

Q1: What did Mary go shopping for?

Q2: What did Mary find that she liked?

Q3: Did Mary buy anything?


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Program 1:

1. Match predefined templates to questions to generate text patterns.

2. Match text patterns to input texts to get answers.

“What did X Y” “What did Mary go shoppin for?”

“Mary go shopping for Z”

Z = a new coat


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Program 2:Convert the input text into a structured internal form that

attempts to capture the meaning of the sentences.

Structured representation of sentences:

Event2: Thing1:instance: Finding instance: Coattense: Past colour:Redagent: Maryobject: Thing 1


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Program 3:

Background world knowledge:

C finds M

C leaves L C buys M

C leaves L

C takes M


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Conclusion

• The subject of AI deals more with symbolic reasoning that conventional number crusting problems.

• Knowledge representation, learning, speech and uncertainty management of data and knowledge are the common areas covered under AI.

• LISP and PROLOG are the used for programming AI problems.


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13897_1.AI Introduction(Lecture 1-5)

Documents

speech recognition

procedural

artificial

wellrational

intelligent

introductory

alan turing

chandra prakash