1 / 52

Artificial Intelligence : An Introduction for CS570 Artificial Intelligence

Artificial Intelligence : An Introduction for CS570 Artificial Intelligence. Jin Hyung Kim KAIST Computer Science Dept. Definition of AI. Automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning, … (Bellman, 1978)

ardelis
Download Presentation

Artificial Intelligence : An Introduction for CS570 Artificial Intelligence

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Artificial Intelligence : An Introduction for CS570 Artificial Intelligence Jin Hyung Kim KAIST Computer Science Dept.

  2. Definition of AI • Automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning, … (Bellman, 1978) • Study of how to make computers do things at which, at the moment, people are better (Rich & Knight, 1991) • A Branch of Computer Science that is concerned with the automation of intelligent behavior (Luger & Stubblefield, 1993) • The study of mental faculties through the use of computational models (Charniak & McDormott, 1995)

  3. Computer Science Body of Knowledge 31 43 16 10 10 38 Total 280 Core Hours 3 8 21 31 15 18 36 Source : IEEE/ACM Computing Curricula 2001 Computer Science

  4. Computing Disciplines,before and after 1990s

  5. AI : Engineering Definition • Study of how to make machine do things which require intelligence when human do • things requiring intelligence ? • Making computer MORE smart • Making thinking computer • Can machine think ? • Focus on how good it performs

  6. AI : Cognitive Scientific Definition • Studying intelligence by computational means • Programmed Human intelligence • Artificial Mind • Focus on how similar it works as human

  7. Intelligent System Perception, Recognition, Understanding Making decisions, Acting Flexibility Automation Optimization Aims via

  8. Examples of AI systems • Language Translation systems • Natural Language Question answering systems • Diagnosis Expert systems • Avionic Expert systems vs. fly-by-wire • Space shuttle mission planning • Robots in factory, Auto-navigation robots • Intelligent Traffic control system • OCR, Handwriting Recognition System • Speech Recognition System • …

  9. Categorization of AI definitions

  10. Go Playing Programs • Selecting next move • By analysis of all alternative moves • By Analysis of Board Pattern (rule-based) • Which one is better ? • Which one can be better ? • Engineering (mathematical) How well does it perform ? Performance is the key concern. Don’t care of what method used • Cognitive Scientific How similarly does it do as human ? Simulation of Behavior

  11. Acting Humanly : Turing Test • Turing (1950) “Computing Machinery and Intelligence” • Can machine think ?  Can machine behave intelligently ? • Operational test of intelligent behavior : imitation game • Predicted that by 2000, a machine might have 30% chance of fooling a lay person in 5 minutes

  12. Imitation Game

  13. Issues on Turing Test • Intelligent as much as Human • Is dog intelligent ? • Searle’s Chinese Room argument • Strong AI and Weak AI • “ELIZA - a friend you could never have before” • http://www-ai.ijs.si/eliza-cgi-bin/eliza_script • Imitation of Client-centered Rogerian Therapy • Suggested major component of AI : knowledge, reasoning, language, understanding, learning • Any man-made system passed Turing Test ?

  14. Thinking Rationally : Laws of Thought • Normative (or prescriptive ) rather than descriptive • Several school of Greek schools developed various forms of logic, notation and rules of derivation of thoughts • Mathematics and Philosophies of modern AI • Problems • Not all intelligent behavior is mediated by logical deliberation • What is purpose of thinking ? What thought should I have ? • Rational Behavior : doing the right thing • “right” – expected to maximize goal achievement given available information

  15. Hype Cycle (Boom-Bust-Build) Science Fiction Hangover Productivity Curiosity

  16. The Hype Cycle of Emerging Technologies ※자료 : Gartner, 2002 ※자료 : Gartner, 2002

  17. Approaches to Intelligent system development u Knowledge-based Approach u Data Driven Approach u

  18. Knowledge-base Systems u Represent Human knowledge as symbol combination u Knowledge Acquisition and Representation u Logic, Expert System, Fuzzy Logic u

  19. Data Driven Approach u Extract common characteristics from collected examples u Training u Statistical Methods, Artificial Neural Network

  20. Generality vs Power • Aims Powerful and general solutions • General Problem Solver • Early attempt : failed • Complexity : Toy Problems Only • Specialized Approach to get Power • Knowledge Based Approach • “Practical” Expert Systems

  21. State of the Art • Which of the following can be done at present ? • Play a decent game of table tennis • Drive along a curvy mountain road • Drive in the center of Seoul city • Play decent game of Go • Discover and prove a new mathematical theorem • Write an intentionally funny story • Give competent legal advice in a specialized area of law • Translate spoken Korean into spoken Japanese in real time

  22. Axes of AI Research Theory Methodology System Application

  23. Major research areas (Methodology) • Symbolic Programming • Knowledge Representation • Search & Planning • Automated Reasoning • Machine Learning, knowledge Discovery • Artificial Neural Net • Genetic Algorithm • …...

  24. Major research areas (Applications) • Natural Language Understanding • Image, Speech and pattern recognition • Uncertainty Modeling • Expert systems • Virtual Reality • …..

  25. Symbolic Programming • Program as Representation of world • Symbol as basic element of representation • atom, property, relationship • Symbolic Expression as method of combination • LISP for Symbolic programming • PROLOG for logic programming • Object-Oriented Concept

  26. Knowledge Representation • What kind of Knowledge needed for Problem solving ? • Structure of knowledge ? • declarative vs procedural • Representation techniques ? • explicit vs (implicit + inference) • logic, frame, object-oriented, semantic net, script • Knowledge acquisition and update

  27. Search Theory • An Optimization method • Analyze alternative cases and select one • Cope with Exponential complexity, NP classes • Try likely one first (Heuristic Search) • Utilize local information (Hill Climbing Method) • Optimal solution vs good solution • Genetic Algorithm, Simulated Annealing • Stochastic search

  28. Automated Reasoning • Qualitative Reasoning • Utilization of qualitative knowledge such as • Non-monotonic Reasoning • Ostrich flys ? • Plausible Reasoning • Information fusion under uncertainty • Case-based Reasoning • Utilization of Experience

  29. Machine Learning • Performance improvement by experience • How much of knowledge required to start learning ? • Method of acquiring new knowledge and merging it to existing knowledge-base • Role of teacher • Role of examples and experience • Parameter Adjustment • Inductive learning • Computational Learning Theory • Quality of generalization capability in terms of Training data • Used in Practice such as Data Mining

  30. Data Mining Knowlegre extraction for decision making Data Decision Making Information / knowledge • 인구통계 • Point of Sale • ATM • 금융통계 • 신용정보 • 문헌 • 첩보자료 • 진료기록 • 신체검사기록 • A상품 구매자의 80%가 B상품도 구매한다 • 미국시장의 자동차 구매력이 6개월간 감소 • A상품의 매출 증가가 B상품의 2배 • 탈수 증상을 보이면 위험 • 광고전략은 ? • 상품의 진열 • 최적의 예산 할당은 ? • 시장점유의 확대방안은 ? • 고객의 이탈 방지책은 ? • 처방은 ?

  31. Neural Network • Computational model of Neurons • Power comes from Connection of simple processing element - connectionism X1 w1 w2 X2 F(X1, X2, …, Xn) S . . . wn Xn

  32. Neural Network • learning = link weigh adjustment • Error-back-propagation : supervised learning • Any Functional Mapping is learnable • Strong at Sensory Data Processing • Symbolic Grounding • Old Horse on the race again • Massive parallelism, graceful degradation

  33. Job(1/0) good age medium Salary bad #mouth Debt Neural Network Classifier Input layer Hidden layer Output layer

  34. Genetic Algorithm • Computational model of life evolution • Stochastic optimization technique • Initial chromosome creation • New generations are made (cross over, mutation) • survival of the fittest • Base of artificial life research • study evolution of life, by simulation

  35. History of AI • 50 years of rise and fall of New technologies after invention of computer • Logic • Optimization • Proabilistic Modeling • Search theory • Rule-based system • Expert systems • Fuzzy Theory • Neural Netwrok • Genetic Algorithm • Chaos theory • Artificial life • .....

  36. AI Prehistory • Philosophy • Logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality • Mathematics • Formal representation of proof, algorithms, computation, decidability, tractability, probability • Psychology • Adaption, phenomena of perception and motor control, experimental techniques • Linguistics • Knowledge representation, grammar • Neurosicence : Physical substrate for mental activity • Control Theory : homeostatic systems, stability, optimal designs

  37. Potted History of AI (I) • 1943 : McCulloch & Pitts : Boolean Circuit model of Brain • 1950 : Turing’s “Computing Machinery and Intelligence” • 1950s : Early AI programs – Samuel’s checker program, Newell & Simon’s Logic Theorist • 1956 : Dartmouth meeting “Artificial Intelligence” adopted • 1965 : Robinson’s algorithm for logical reasoning

  38. Potted History of AI (II) • 1966-74 : AI discovers computational complexity • 1969-79 : Early development of knowledge-based systems • 1980-88 : Expert systems industry booms, AI Programming Machine • 1983 – 1993 : Japan initiated 5th generation computer project • 1988-93 : Expert systems industry burst : “AI Winter” • 1985-95 : Neural Network back to the race • 1988 : Resurgence of probabilistic and decision-theoretic methods, Rapid increase of technical depth of mainstrean AI “Nouville AI : Alife, Genetic Algorithm, Soft computing

  39. AI Success Story • Evans ANOLOGY • Symbolic Algebra • Macsyma (http://www.macsyma.com/) • Chess Program DEEP BLUE defeat Gary Kasparov (1996) • Automatic Theorem Proving contest (1999)

  40. AI Success Story (Planning) • MARVEL (Schwuttke, 1992) • Real-time Space shuttle Mission planning • Berth assignment (KAL, 1997) • Unmanned Vehicle • Ground and air • Pathfinder Rover, 1996 • Asimo – a walking robot

  41. Autonomous Land Vehicle(DARPA’s GrandChallenge contest)

  42. AI Success Story (Language Processing) • PEGASUS (Zue, 1994) • Spoken Natural language for airline reservation • Limited context, free representation • Japanese-Korea Hotel reservation(KT, 1995) • Chatter Bot • 자연언어로 대화 (typing)하는 회사소개 에이젼트 등 • Many machine translation • 일한 실용화 완료, 영한 - 시제품

  43. AI Success Story : Medical expert systems Programs listed by Special Field • Antibiotics & InfectiousDiseases • Cancer • Chest pain • Dentistry • Dermatology • Drugs & Toxicology • Emergency • Epilepsy • Family Practice • Genetics • Geriatrics • Gynecology • Imaging Analysis • Internal Medicine • Intensive Care • Laboratory Systems • Orthopedics • Pediatrics • Pulmonology & Ventilation • Surgery & Post-Operative Care • Trauma Management

  44. Pattern Recognition Applications • Handwriting and document recognition • forms, postal mail, historic documents • PDA pen recognition • Signature, biometrics (finger, face, iris, etc.) • Gesture, facial expression • As a Human computer intertraction • EEG, EKG, X-ray • Trafic monitoring, Remote Sensing • Smart Weapon – guided missile, target homing

  45. Automatic Target Recognizer

  46. Postal Address Recognition

  47. 전자 펜으로 수식 입력 수식 인식 Handwriting Understanding

  48. 次世代 PC : e-Book, Tablet PC, PDA, M-phone

  49. Ubiquitous 전자교실

  50. BioInformatics / Protein Structure Analysis

More Related