ME 534Intelligent Manufacturing SystemsIntelligent Manufacturing Systems

Chp 2 – Introduction to Artificial Intelligence (AI)Chp 2 Introduction to Artificial Intelligence (AI)

Dr. A. Tolga BozdanaAssistant Professor

Mechanical EngineeringUniversity of Gaziantep

Introduction to AIWhat is Artificial Intelligence?What is Artificial Intelligence?

Science & engineering of making intelligent machines, especially intelligent computer programs.

It is related to using computers for understanding human intelligence.g p g g

Then, what is intelligence?

I t lli i th t ti l t f bilit t hi l i th ld V i ki d d Intelligence is the computational part of ability to achieve goals in the world. Various kinds anddegrees of intelligence occur in people, many animals and some machines.

Do we have single answer to: Is this machine intelligent?

No! Intelligence involves mechanisms, and AI states how to make computers doing some of them.

If doing a task requires only mechanisms well understood computer programs can give impressive If doing a task requires only mechanisms well understood, computer programs can give impressiveperformances on these tasks. Such programs are considered as "somewhat intelligent".

How far is AI from reaching human-level intelligence? When will it happen?

Scientists think that human-level intelligence can be achieved by writing large numbers of programs.People are now writing and assembling vast amount of knowledge-based facts in their programs.

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eop e a e o g a d asse b g as a ou o o edge based ac s e p og a s

However, most AI researchers believe that new fundamental ideas are required, and hence it cannotbe predicted when human-level intelligence will be achieved.

History of AI - Turing Test P d i 1950 b Al T i (B iti h hil h th ti i l i i t l t t

Implementation of Turing Test

Proposed in 1950 by Alan Turing (British philosopher, mathematician, logician, cryptanalyst, computerscientist), Turing Test was the first serious proposal in the philosophy of AI.

Implementation of Turing Test

We have a person, a machine, and an interrogator.

The interrogator is put in a room separated fromthe person and the machine.

Aim of the interrogator is to determine which oneis person or machine.p

The interrogator knows person and machine bylabels A & B, but does not know which one is what.

Alan Turing (1912‐1954)

The interrogator is allowed to ask questions like:“Will A please tell me whether A plays chess?”

Whichever of the machine or the person is A mustanswer questions addressed to A.

Aim of the machine is to cause the interrogator tomistakenly conclude that machine is the personmistakenly conclude that machine is the person.

Aim of the person is to help the interrogator tocorrectly identify the machine. 2

History of AI - Chinese Room Chi R i th ht i t t d b J h S l (A i Chinese Room is a thought experiment presented by John Searle (American

philosopher, and currently the Slusser Professor of Philosophy at the Universityof California, Berkeley) in 1980s.

It is one of the best known and widely credited counters to claims of AI:

“computers do or at least can (someday might) think”

John Searle (1932)

Step 1:A message is sent to a non‐Mandarin 

Step 2:Equipped with an instruction manual of 

Step 3:The Mandarin speaker believes that he has 

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speaker in Chinese Room. possible questions and answers, the non‐Mandarin speaker is able to match the characters and select a response.

been talking to another Mandarin speaker. However, the person in Chinese Room has no idea what he has said.

History of AI – Deep Blue vs Kasparov D Bl G K i f Deep Blue versus Garry Kasparov was a pair of

famous six-game human-computer chess games.

The games were played between IBM supercomputerDeep Blue (with the team of IBM programmers andchess experts who directed and reprogrammed DeepBlue between games) and Garry Kasparov (the Worldg ) y p (Chess Champion).

“I'm not afraid to admit that I'm afraid, andI'm not even afraid to say why I am afraidI m not even afraid to say why I am afraid,because sometimes, you know, it definitelygoes beyond any known program in the world.It makes decisions that still cannot be made byany computer . . .

Kasparov’s Speech5th Game of Rematch

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May 10, 1996

AI Systems

Systems Thinking Humanly “Cognitive modeling” approach (trying to

get inside of our mind)

Systems Acting Humanly Turing Test (to determine whether a

computer has achieved intelligence)g ) Crossover btw computer science &

psychology Areas of interest:

p g ) Computer needs:

– Natural language processing– Knowledge representation

– Cognitive models– Neural networks

Knowledge representation– Automated reasoning– Machine learning

Systems Thinking Rationally Systems Acting Rationally

AI SYSTEMS

“Laws of thought” approach (solving any problem based on logical manipulation) Problems:

Design a rational agent Advantages over “logic approach”:

– Logic is only one tool or many that– Representing certain types of knowledge(e.g. common sense, informal knowledge)– Solving problems in theory & practice

Logic is only one tool or many that can be used to design rational agent– Scientific advances provide more tools for developing better agents

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Solving problems in theory & practice(e.g. computational limits)

tools for developing better agents

Classifications of AI1 S b li AI1. Symbolic AI

Designers explicitly program all of the “knowledge” into AI’s coding.

Uses symbols, variables, etc. to perform it’s work. Uses symbols, variables, etc. to perform it s work.

Strength: Working with logical problems.

Weakness: Working with imperfect data.

2. Connectionist AI

Designers teach an Artificial Neural Network (ANN) what the AI needs to know Designers teach an Artificial Neural Network (ANN) what the AI needs to know.

A network of connected simulated neurons, which is similar to a natural mind.

Strength: Working with imperfect data.g g p

Weakness: Working with logical problems.

3. Evolutionary AI

Designers give the AI the ability to refine itself.

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Will constantly try to improve its efficiency by testing a modified version against an unmodified version.

Whichever has the best efficiency is the one that is used from then on.

Knowledge RepresentationS ti N t k S h TSemantic Networks

Focusing on the relationships between objects.

A directed graph is used for represention

Search Trees

A structure that represents alternatives inadversarial situations, such as game playing.

A directed graph is used for represention.

Objects in the network represent objects in thereal world that we are representing.

The paths down a search tree represent aseries of decisions made by the players.

Two ways to prune the search space: The relationships are based on the real world

questions that we would like to ask.

Two ways to prune the search space:

– Depth-first: Searching down the paths ofa tree prior to searching across levels

– Breadth-first: Searching across levels ofa tree prior to searching down specific paths

Breadth first tends to yield the best results Breadth-first tends to yield the best results.

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Intelligent Agents

A t th t i it i t l f it d i t t ith it i t lli tl A system that perceives its environment, learns from it, and interacts with it intelligently.

Intelligent agents can be divided into two broad categories: software agents & physical agents

Software Agents

A set of programs that are designed to do particular tasks.

For example, a software agent can check the contents of received e-mails and classify them intodifferent categories (e.g. junk, less important, important, very important, and so on).

A th l i h i d t h th i t t d fi d th b it th t id Another example is a search engine used to search the internet and find the websites that can provideinformation about a requested subject.

Physical Agents (Robots)

A programmable system that can be used to perform a variety of tasks.

Simple robots can be used in manufacturing to do routine jobs (e.g. assembling, welding, or painting).

Some organizations use mobile robots for doing routine delivery jobs (such as distributing mail).

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Mobile robots are also used for underwater activities (e.g. to prospect for oil).

A humanoid robot is an autonomous mobile robot that is supposed to behave like a human.

Programming Languages

Alth h ll l ( h C C d J ) b d Although some all-purpose languages (such as C, C++ and Java) can be used to create intelligent software, there are languages specifically designed for AI:

LISP (LISt Programming):

– invented by John McCarthy (American computer scientist and cognitive scientist) in 1958)

– simple knowledge representation (list)

– easy to apply functionality to represented elements

PROLOG (PROgraming in LOGic):

– conceived by Alain Colmerauer (French computer scientist) in 1970 John McCarthy (1927‐2011)

– logic-based

– facts (a database) and rules (knowledge base) easily represented

y ( )

– built-in search engine

Specialized Languages:

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Rule languages (e.g. CLIPS)

Planning languages (e.g. STRIPS)

AI Mapping

M d AI i b d h ( h f th k b t ibl l ti ) d k l d Modern AI is based on search (search for the unknown best possible solution) and knowledgerepresentation (map the real world problem into the computable program):

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