450-101 Management Information System Artificial Intelligence & Expert Systems ผศ. ดร....

450-101 Management Information System450-101 Management Information System

Artificial Intelligence &Expert Systems

ผศ.ดร. วิ�ภาดา เวิทย์ ประสิ�ทธิ์��

Office :CS320, Computer Science BuildingEmail :wwettayaprasit@yahoo.comWebsite :http://staff.cs.psu.ac.th/wiphadaPhone :0-7428-8596

323-670 Artificial Intelligence Chapter 12

Artificial Intelligence

artificial intelligencen. (Abbr. AI) The ability of a computer or other machine

to perform those activities that are normally thought to require intelligence.

The branch of computer science concerned with the development of machines having this ability.

• The subfield of computer science concerned with understanding the nature of intelligence and constructing computer systems capable of intelligent action.

• It embodies the dual motives of furthering basic scientific understanding and making computers more sophisticated in the service of humanity.

• Many activities involve intelligent action

—problem solving, perception, learning, planning and other symbolic reasoning, creativity, language, and so forth—and therein lie an immense diversity of phenomena.

• Computer Encyclopedia • (Artificial Intelligence) Devices and

applications that exhibit human intelligence and behavior including robots, expert systems, voice recognition, natural and foreign language processing. It also implies the ability to learn and adapt through experience.

WikipediaThe term Artificial Intelligence (AI)

was first used by John McCarthy who considers it to mean "the science and engineering of making intelligent machines".[1]

It can also refer to intelligence as exhibited by an artificial (man-made, non-natural, manufactured) entity.

WikipediaAI is studied in overlapping fields

of computer science, psychology, neuroscience and engineering, dealing with intelligent behavior, learning and adaptation and usually developed using customized machines or computers.

tic tac toe

Tic Tac Toe

3D Tic Tac Toe

Artificial Intelligence Fields

1.1. Natural Language Processing Natural Language Processing

Neural NetworksNeural NetworksMachine LearningMachine LearningRoboticsRoboticsComputer VisionComputer VisionExpert SystemsExpert Systems

1 Natural Language Processing

• Wikipedia

• Natural language processing (NLP) is a subfield of artificial intelligence and linguistics. It studies the problems of automated generation and understanding of natural human languages.

• Natural language generation systems convert information from computer databases into normal-sounding human language, and natural language understanding systems convert samples of human language into more formal representations that are easier for computer programs to manipulate.

Natural Language Processing

• have the same surface grammatical structure. However, in one of them the word they refers to the monkeys, in the other it refers to the bananas:

• the sentence cannot be understood properly without knowledge of the properties and behaviour of monkeys

• We gave the monkeys the bananas because they were hungry and We gave the monkeys the bananas because they were over-ripe.

• A string of words may be interpreted in myriad ways. For example,

1. time moves quickly just like an arrow does; 2. measure the speed of flying insects like you

would measure that of an arrow - i.e. (You should) time flies like you would an arrow.;

3. measure the speed of flying insects like an arrow would - i.e. Time flies in the same way that an arrow would (time them).;

4. measure the speed of flying insects that are like arrows - i.e. Time those flies that are like arrows;

5. a type of flying insect, "time-flies," enjoy arrows (compare Fruit flies like a banana.)

Time flies like an arrow

• English and several other languages don't specify which word an adjective applies to.

• For example, in the string "pretty little girls' school". – Does the school look little? – Do the girls look little? – Do the girls look pretty? – Does the school look pretty?

•"pretty little girls' school"

Question Answering 2

•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 favorite dress.

ELIZA Q1:What did Mary go shopping for?A1: .............................................Q2:What did Mary find she liked?A2:.............................................Q3: Did Mary buy anything ?A3:.............................................

Figure14.5: More Interaction among Components

saw DET

PP with a telescope

in the park

John saw the boy in the park with a telescope.

saw DET

with a dogin the park

John saw the boy in the park with a dog.

John saw the boy in the park with a statue.

saw DET

with a statue

in the park

2 Neural Networks

• neural network also neural net n. • A real or virtual device, modeled

after the human brain, in which several interconnected elements process information simultaneously, adapting and learning from past patterns

Neural Network

• Computer Encyclopedia • neural network •A modeling technique based

on the observed behavior of biological neurons and used to mimic (imitate) the performance of a system.

Neural Network

• It consists of a set of elements that start out connected in a random pattern, and, based upon operational feedback, are molded into the pattern required to generate the required results.

• It is used in applications such as robotics, diagnosing, forecasting, image processing and pattern recognition.

Neural Network

• Accounting Dictionary • Neural Networks • Technology in which computers

actually try to learn from the data base and operator what the right answer is to a question.

Neural Network

• The system gets positive or negative response to output from the operator and stores that data so that it will make a better decision the next time.

• While still in its infancy, this technology shows promise for use in accounting, fraud detection, economic forecasting, and risk appraisals.

• The idea behind this software is to convert the order-taking computer into a "thinking" problem solver.

Neural Network

• Britannica Concise Encyclopedia • neural network • Type of parallel computation in which

computing elements are modeled on the network of neurons that constitute animal nervous systems.

• This model, intended to simulate the way the brain processes information, enables the computer to "learn" to a certain degree.

Neural Network

• A neural network typically consists of a number of interconnected processors, or nodes. Each handles a designated sphere of knowledge, and has several inputs and one output to the network. Based on the inputs it gets, a node can "learn" about the relationships between sets of data, sometimes using the principles of fuzzy logic.

Neural Network

•Neural networks have been used in pattern recognition, speech analysis, oil exploration, weather prediction, and the modeling of thinking and consciousness.

Neural Network

A Neuron

• The n-dimensional input vector x is mapped into variable y by means of the scalar product and a nonlinear function mapping

weighted sum

Inputvector x

output y

Activationfunction

weightvector w

Multi-Layer Perceptron

Output nodes

Input nodes

Hidden nodes

Output vector

Input vector: xi

jiijj OwI

Neural Network Training: A Detailed ViewNeural Network Training: A Detailed View

Neural Networks

• Advantages– prediction accuracy is generally high– robust, works when training examples contain

errors– output may be discrete, real-valued, or a vector of

several discrete or real-valued attributes– fast evaluation of the learned target function

• Criticism– long training time– difficult to understand the learned function

(weights)– not easy to incorporate domain knowledge

Network Training• The ultimate objective of training

– obtain a set of weights that makes almost all the tuples in the training data classified correctly

• Steps– Initialize weights with random values – Feed the input tuples into the network..... one by one– For each unit

• Compute the net input to the unit as a linear combination of all the inputs to the unit

• Compute the output value using the activation function

• Compute the error• Update the weights and the bias

Feed-Forward Neural NetworkFeed-Forward Neural Network

Neural Network Training: A Conceptual ViewNeural Network Training: A Conceptual View

3 Machine Learning

• Sci-Tech Dictionary

• machine learning (mə′shēn ′lərn·iŋ)

• (computer science) The process or technique by which a device modifies its own behavior as the result of its past experience and performance.

Machine Learning• Wikipedia • machine learning is concerned

with the development of algorithms and techniques that allow computers to "learn".

• At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods extract rules and patterns out of massive data sets.

Machine Learning• inductive, • Logic.

– The process of deriving general principles from particular facts or instances.

• Mathematics. – A two-part method of proving a theorem

involving an integral parameter . First the theorem is verified for the smallest admissible value of the integer. Then it is proven that if the theorem is true for any value of the integer, it is true for the next greater value. The final proof contains the two parts .

Machine Learning• inductive, • reasoning from detailed facts to gen

eral principles– Rule induction is an area of machine

learning in which formal rules are

extracted from a set of observations .

Machine Learning• deductive. Logic.

– The process of reasoning in which a conclusion follows necessarily from the stated premises; inference by reasoning from the general to the specific .

– reasoning from the general to the particular

– Deduction is the process of drawing conclusions from premises

Machine Learning– Deduction The process of reaching a co

nclusion through reasoning from genera l premises to a specific premise.

– An example of deduction is present in th e following syllogism:

– Premise: All mammals are animals. – Premise: All whales are mammals .– Conclusion: Therefore, all whales are an

imals.

Machine Learning

• deduction, in logic, form of inference

such that the conclusion must be true if the premises are true .

• For example, – if we know that….. all men have two legs – And that …………..John is a man,

– it is then logical to deduce that

……………………..John has two legs.

4 Robotics

• Shakey the Robot Developed in 1969 by the Stanford Research Institute, Shakey was the first fully mobile robot with artificial intelligence. Seven feet tall, Shakey was named after its rather unstable movements. (Image courtesy of The Computer History Museum, www.computerhistory.org)

Robotics

• A legged game from RoboCup 2004 in Lisbon, Portugal

• Team ENSCO's entry in the first Grand Challenge, DAVID

Robotics• The DARPA Grand Challenge is

a race for a $2 million prize where cars drive themselves across several hundred miles of challenging desert terrain without any communication with humans, using GPS, computers and a sophisticated array of sensors. In 2005 the winning vehicles completed all 132 miles of the course in just under 7 hours.

Robotics

• ro·bot A mechanical device that sometimes resembles a human and is capable of performing a variety of often complex human tasks on command or by being programmed in advance.

• A machine or device that operates automatically or by remote control.

• A person who works mechanically without original thought, especially one who responds automatically to the commands of others.

Robotics

• Computer Encyclopedia • robot • A stand-alone hybrid computer system that

performs physical and computational activities. Capable of performing many different tasks, it is a multiple-motion device with one or more arms and joints.

• Robots can be similar in form to a human, but industrial robots do not resemble people at all.

Robotics

• Huey, Dewey and Louie• Named after Donald

Duck's famous nephews, robots at this Wayne, Michigan plant apply sealant to prevent possible water leakage into the car. Huey (top) seals the drip rails while Dewey (right) seals the interior weld seams. Louie is outside of the view of this picture. (Image courtesy of Ford Motor Company.)

Robotics• Inspect Pipes from the

Inside• Developed by SRI for

Osaka Gas in Japan, this Magnetically Attached General Purpose Inspection Engine (MAGPIE) goes inside gas pipes and looks for leaks. This unit served as the prototype for multicar models that perform temporary repairs while capturing pictures. (Image courtesy of SRI International.)

Robotics

• Computers Making Computers

• Robots, whose brains are nothing but chips, are making chips in this TI fabrication plant. (Image courtesy of Texas Instruments, Inc.)

Robotics• How Small Can They

Get?• By 2020, scientists at

Rutgers University believe that nano-sized robots will be injected into the bloodstream and administer a drug directly to an infected cell. This robot has a carbon nanotube body, a biomolecular motor that propels it and peptide limbs to orient itself.

Robotics• ASIMO,• a humanoid robot

manufactured by Honda.

5 Computer Vision

Computer Vision

• Computer vision • The technology concerned with

computational understanding and use of the information present in visual images.

• In part, computer vision is analogous (similar) to the transformation of visual sensation into visual perception in biological vision.

Computer Vision

• For this reason the motivation, objectives, formulation, and methodology of computer vision frequently intersect with knowledge about their counterparts in biological vision. However, the goal of computer vision is primarily to enable engineering systems to model and manipulate the environment by using visual sensing.

Computer Vision

• Field of robotics in which programs attempt to identify objects represented in digitized images provided by video cameras, thus enabling robots to "see."

• Much work has been done on stereo vision as an aid to object identification and location within a three-dimensional field of view. Recognition of objects in real time.

Computer Vision Vision based

biological species identification systems

Computer Vision

• Artist's Concept of Rover on Mars,

• an example of an unmanned land-based vehicle. Notice the stereo cameras mounted on top of the Rover. (credit: Maas Digital LLC)

Image Processing Fields

Digital Image Processing• Image

– An image is a two-dimensional signal

]1,0[]1,0[]1,0[: f

Digital Image Processing

• Digital Image– A digital image is a two-dimensional signal with a

countable domain and a countable range

}255,...,1,0{}479,...,1,0{}639,...,1,0{: f

Histogram

Image EnhancementImage Enhancement

Line Detection

• horizontal,... +45 degree,.. vertical... and -45 degree masks

• Horizontal mask will result with max response when a line passed through the middle row of the mask with a constant background.

• the similar idea is used with other masks.• note: the preferred direction of each mask is weighted with a

larger coefficient ....(i.e.,2) than other possible directions.

Image Segmentation

Example

7 Expert Systems

• expert systemn. Computer Science.

•A program that uses available information, heuristics, and inference to suggest solutions to problems in a particular discipline.

Expert Systems• Expert systems • Methods and techniques for

constructing human-machine systems with specialized problem-solving expertise.

• The pursuit of this area of artificial intelligence research has emphasized the knowledge that underlies human expertise and has simultaneously decreased the apparent significance of domain-independent problem-solving theory. In fact, new principles, tools, and techniques have emerged that form the basis of knowledge engineering.

Expert Systems• Expertise consists of knowledge about a

particular domain, understanding of domain problems, and skill at solving some of these problems.

• Knowledge in any specialty is of two types, public and private.

• Public knowledge includes the published definitions, facts, and theories which are contained in textbooks and references in the domain of study. But expertise usually requires more than just public knowledge.

Expert Systems

• Human experts generally possess private knowledge which has not found its way into the published literature.

• This private knowledge consists largely of rules of thumb or heuristics.

• Heuristics enable the human expert to make educated guesses when necessary, to recognize promising approaches to problems, and to deal effectively with erroneous or incomplete data.

Expert Systems

Category Problem addressed

InterpretationsInferring situation descriptions from sensor

PredictionInferring likely consequences of given

situations

Diagnosis Inferring system malfunctions from observables

Design Configuring objects under constraints

Planning Designing actions

MonitoringComparing observations to plan

vulnerabilities

Debugging Prescribing remedies for malfunctions

RepairExecuting a plan to administer a prescribed

remedy

InstructionDiagnosing, debugging, and repairing

students' knowledge

Jim Miller

Reference

Artificial Intelligence second edition, Elaine Rich and Kevin Knig ht,

- McGraw Hill Inc., 1991.

James A. O’Brien and George M. Marakas, Management Information Systems, 8th edition, McGraw-Hill

/Irwin, 2008

http://www.thai2english.com

http://translate.google.com

Text to speech

http://teachrose.com/rose/src/talk.php

Expert systems

http://www.youtube.com/watch?v -= e21jpWhqVM&feature=related

Car Tracking

http://www.youtube.com/watch?v=2GBMAtGaGdg

Robotics

Virtual Reality

http://www.youtube.com/watch?v=OKSodRhEvA8&feature=related

Car Tracking

http://www.tonprikinfo.org/application/index.php?id=16

Gotoknow.org

450-101 Management Information System Artificial Intelligence & Expert Systems ผศ. ดร....

Documents

Transcript of 450-101 Management Information System Artificial Intelligence & Expert Systems ผศ. ดร....

ติวมาม่า16th ชีววิทยา PAT2 (ผศ.ดร.สมาน แก้วไวยุทธ)

ผศ.ดร.อินทิรา รอบรู้ - Suan Sunandha Rajabhat University · 2017. 8. 10. · ผศ.ดร.อินทิรา รอบรู้ 3. แถบกำรตั้งค่ำ

PowerPoint Presentation · 2018-01-22 · ดร.สมยศ เด่นจิตเจริญ ประธานสภาคณาจารย์ฯ ผศ.ดร.กิตติศักดิ์ชัย

วันพฤหัสบดีที่ 5 สิงหาคม 2553 ผศ. ดร. สมชาย ปฐมศิริ

1. PPT ผศ.ดร.วชิระ

สรุปคำบรรยายหลังเรียน (ผศ.ดร.วิทยา)

ผศ.ดร.ธนพรรณ ธานี รองอธิการบดีฝ่ายบริหารทรัพยากรมนุษย์ มหาวิทยาลัยอุบลราชธานี

ผศ.ดร.ประชา คุณธรรม ดี คณะเศรษฐศาสตร์ มหาวิทยาลัยธรรมศาสตร์

Agile Development for Startup โดย ผศ.ดร.รัฐกร พูลทรัพย์

คาร์โบไฮเครตเมแทบอลิซึม (Carbohydrate metabolism) โดย ผศ . ดร. คมศร ลมไธสง

อ. ดร. สมใจ ศิระ กมล ผศ.สุพิศ รุ่งเรืองศรี

3.PPT ผศ.ดร.ทิพย์สิริ

ผ้วิจัยู นางขัติยาภรณ์ เจริญวัฒน์อนันต์ ที่ปรึกษา ผศ.ดร. ... · กลุ่มตัวอย่างต่างๆ

344-571 ปัญญาประดิษฐ์ (Artificial Intelligence) ผศ. ดร. วิภาดา เวทย์ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์

พฤติกรรมองค์การTitle: พฤติกรรมองค์การ Author: ผศ.ดร. ณรัฐพันธ์ เขจรนันทน์

การจัดการศึกษาสาขาวิชาสารสนเทศศาสตร์ที่มีคุณภาพ โดย ผศ.ดร.อรรจน์

ฉบับที่ ๒ ป ๒๕๕๖ – ๒๕๕๙ · 2013-04-22 · ผศ.ดร.สงครามชัย ลีทองดี, ผศ.ดร.จีระศักดิ์

ผศ.ดร.ศศิธร สุวรรณเทพ กรรมการเครือข่ายการศึกษาทั่วไปแห่งประเทศไทย

ความหลากหลายทางชีวภาพ (BIODIVERSITY) · PDF fileความหลากหลายทางชีวภาพ (biodiversity) ผศ. ดร.

ผศ. ดร. ยุทธนา เศรษฐ ปราโมทย์