323-670 AI lecture 1-3 no figure

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323-670 ปัญญาประดิษฐ์ (Artificial Intelligence) : 

323-670 ปัญญาประดิษฐ์ (Artificial Intelligence) ดร.วิภาดา เวทย์ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์ ห้องทำงาน : M100/3 โทรศัพท์ : 074-288689 E-mail : wwettayaprasit@yahoo.com

วัตถุประสงค์ : 

323-670 Artificial Intelligence Lecture 1-3 Page 2 วัตถุประสงค์ 1. ให้นักศึกษามีความรู้ความเข้าใจเกี่ยวกับปัญญาประดิษฐ์และสาขาต่างๆของปัญญาประดิษฐ์ 2. ให้นักศึกษาสามารถพัฒนางานทางด้านปัญญาประดิษฐ์ได้ 3. ให้นักศึกษาสามารถค้นคว้าเพิ่มเติมด้วยตนเองได้ วิธีการเรียนการสอน : การบรรยาย การสัมมนา การศึกษาค้นคว้าด้วยตัวเอง การวัดผล : สอบกลางภาค 30% สอบปลายภาค 30% สัมมนา 20% Assignment 20% เวลาเรียน : จันทร์ 9 - 10 ห้อง M307 พฤหัสบดี 9– 10 ศุกร์ 9 - 10 ห้อง M207 (ชดเชย พฤหัสบดี 9– 10 ห้อง M111) ตำรา : Artificial Intelligence second edition, Elaine Rich and Kevin Knight, McGraw-Hill Inc., 1991.

Slide 3: 

323-670 Artificial Intelligence Lecture 1-3 Page 3 คาบ Problem and Search 1-2 Chapter 1 : What is Artificial Intelligence? 3-4 Chapter 2 : Problems, Problem Spaces, and Search 5-6 Chapter 3 : Heuristic Search techniques 7-8 Chapter 4 : Knowledge Representation Issues Assignment Knowledge Representation 9-10 Chapter 5 : Using Predicate Logics 11-12 Chapter 6 : Representing Knowledge Using Rules 13-14 Chapter 7 : Symbolic Reasoning under Uncertainty 15-16 Chapter 8 : Statistical Reasoning 17-18 Chapter 9 : Weak Slot-and-Filler Structures 19-20 Chapter 10 : Strong Slot-and-Filler Structures 21-22 Chapter 11 : Knowledge Representation Summary เนื้อหาวิชา

เนื้อหาวิชา : 

323-670 Artificial Intelligence Lecture 1-3 Page 4 คาบ Advance Topics 23-24 Chapter 12 : Game Playing 25-26 Chapter 13 : Planning 27-28 Chapter 14 : Understanding 29-31 Chapter 15 : Natural Language Processing สัมมนา 32-33 Chapter 16: Parallel and Distributed AI 34-35 Chapter 17 : Learning 36-38 Chapter 18: Connectionist Models สัมมนา 39-40 Chapter 19 : Common Sense 41-43 Chapter 20 : Expert Systems สัมมนา 44-45 Chapter 21 : Perception and Action สัมมนา เนื้อหาวิชา

เอกสารอ้างอิง : 

323-670 Artificial Intelligence Lecture 1-3 Page 5 1. Neural Networks : A Comprehensive Foundation, Simon Haykin, Macmillan College Publishing Company Inc. 1994. 2. Artificial Intelligence : Structures and Strategies for Complex Problem Solving, Second Edition, 1993. 3. Expert Systems and Applied Artificial Intelligence, Efraim Turban, Macmillan Publishing Company, 1992. 4. Understanding Decision Support Systems and Expert Systems, Efrem G. Mallach, Richard D. Irwin, Inc. 1994 5. Introduction to Natural Language Processing, Mary Dee Harries, 1985. เอกสารอ้างอิง

Chapter 1What is Artificial Intelligence? : 

Chapter 1What is Artificial Intelligence? 323-670 ปัญญาประดิษฐ์ (Artificial Intelligence) ดร.วิภาดา เวทย์ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์

AI Areas : 

323-670 Artificial Intelligence Lecture 1-3 Page 7 AI Areas Artificial Intelligence (AI) : the branch o f computer science that is concerned with the automation of intelligent behavior. AI Areas : Game Playing Automated Reasoning and Theorem Proving Expert Systems Natural Language Understanding and Semantics Modeling Modeling Human Performance Planning and Robotics Machine Leaning Neural Networks

Task Domain of AI : 

323-670 Artificial Intelligence Lecture 1-3 Page 8 Task Domain of AI Mundane Tasks Perception : Vision, Speech Natural language :Understanding, Generation, Translation Commonsense reasoning Robot control Formal Tasks Games: Chess Mathematics : Logic, Geometry Expert Tasks Engineering : Design, Fault finding, Manufacturing planning Scientific analysis Medical diagnosis Financial analysis

Intelligence require knowledge : 

323-670 Artificial Intelligence Lecture 1-3 Page 9 Intelligence require knowledge It is voluminous. Is i shard to characterize accurately. It is constantly changing. It differs from data by being organized in a way that corresponds to the ways it will be used.

Knowledge Representation and Search for AI : 

323-670 Artificial Intelligence Lecture 1-3 Page 10 Knowledge Representation and Search for AI The knowledge captures generalizations. It can be understood by people who must provide it. It can easily be modified to correct errors and to reflect changes in the world. It can be used in many situations even if it is not totally accurate or complete. It can use to narrow the range of possibilities that must usually be considered.

Common Features of AI Problems : 

323-670 Artificial Intelligence Lecture 1-3 Page 11 Common Features of AI Problems The use of computer to do the symbolic reasoning. A focus on problems that do not respond to algorithmic solutions.  Heuristic search. Manipulate the significant quantitative features of a situation rather than relying on numeric methods. Dealing with semantic meaning. Answer that are neither exact nor optimal but “sufficient”. Domain specific knowledge in solving problems. Use meta-level knowledge.

Homework 1 : 

323-670 Artificial Intelligence Lecture 1-3 Page 12 Homework 1 Read program 1, 2 and 3 and discuss the following criteria. Their Complexity Their use of generalization. The clarity of their knowledge. The extensibility of their approach. Tic-Tac-Toe

Tic-Tac-Toe : Program 1 : 

323-670 Artificial Intelligence Lecture 1-3 Page 13 Tic-Tac-Toe : Program 1 Board : nine element vector representation. 0 = blank, 1 =X, 2 = O Moveable : Their Complexity = 39 = 19,683 view vector board as a ternary number (base three)

Tic-Tac-Toe : Program 2 : 

323-670 Artificial Intelligence Lecture 1-3 Page 14 Tic-Tac-Toe : Program 2 Board : nine element vector representation. 2 = blank, 3 =X, 5 = O an integer indicating which move of the game is about to played. 1 indicate the first move. 9 indicate the last move. Board[5] = 2  mean blank Poswin(p) : If it produce (3*3*2) =18  X can win p = 0 if the player can not win on his next move. Poswin(p) : If it produce (5*5*2) =50 O can win Go(n) : Make a move on square n. TURN is odd  if it is playing X TURN is even  if it is playing O More efficient in term of space.

Tic-Tac-Toe : Program 2’ : 

323-670 Artificial Intelligence Lecture 1-3 Page 15 Tic-Tac-Toe : Program 2’ Board : nine element vector representation. 2 = blank, 3 =X, 5 = O an integer indicating which move of the game is about to played. 1 indicate the first move. 9 indicate the last move. Board[5] = 2  mean blank Poswin(p) : If it produce MAGIC SQUARE (8 + 3 + 4) =15 p = 0 if the player can not win on his next move. Go(n) : Make a move on square n. TURN is odd  if it is playing X TURN is even  if it is playing O More efficient in term of space.

Tic-Tac-Toe : Program 3 : 

323-670 Artificial Intelligence Lecture 1-3 Page 16 Tic-Tac-Toe : Program 3 Board_Position : nine element vector representing the board, a list of board positions that could result from the next move, and a number representing as estimate of how likely the board position is lead to an ultimate win for the player to move. Minimax Procedure : in chapter 12. Search tree : need more time Use AI technique : Decide which of a set of board positions is best. find highest possible rating. consider all the moves the component could make next.  See which move is worst for us.... (Assume the opponent will make that move)

Question Answering 1 : 

323-670 Artificial Intelligence Lecture 1-3 Page 17 Question Answering 1 Russia massed troops on the Czech border. POLITICS program [Corbonell,1980) Q1: Why did Russia do this? A1: รัสเซียต้องการเข้าแทรกแซงการเมืองในเชคโกสโลวาเกีย..... Q1: What should the United States do? A2: ส่งทหารไปช่วยรบกับเชคโกสโลวาเกีย OR A2:ประณามการกระทำของรัสเซีย

Question Answering 2 : 

323-670 Artificial Intelligence Lecture 1-3 Page 18 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:.............................................

The level of the model : 

323-670 Artificial Intelligence Lecture 1-3 Page 19 The level of the model What is the goal in trying to produce programs that do intelligent things that people do? Are we trying to produce programs that do the tasks the same way people do? Are we attempting to produce programs that simply do the tasks in whatever way appears easiest?

Model human performance : 

323-670 Artificial Intelligence Lecture 1-3 Page 20 Model human performance To test psychological theories of human performance. วิเคราะห์นิสัยเกเรของคนไข้ PAPPY {Colby, 1975] 2. To enable computers to understand human reasoning. อ่านหนังสือพิมพ์และวิเคราะห์ข่าวได้ To enable computers to understand computer reasoning. เข้าใจว่า คอมพิวเตอร์ประมวลผลลัพธ์ต่างๆมาได้อย่างไร

Criteria for success : 

323-670 Artificial Intelligence Lecture 1-3 Page 21 Criteria for success How will we know if we have succeeded? Turing test. Human Computer Person asking? DENDRAL : is a program that analyzes organic compounds to determine their structure. HUMAN CHEMIST COMPUTER

Homework 2 : 

323-670 Artificial Intelligence Lecture 1-3 Page 22 Homework 2 Given the meaning of Artificial Intelligence from your point of view. You may add citation from searching documents in the web or from the text book. Given all AI fields with some explanations. End Chapter 1