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Computer Science CPSC 422 Intelligent Systems Cristina Conati

Computer Science CPSC 422 Intelligent Systems Cristina Conati. Super Brief Intro. Advanced AI course Builds upon 322 (and 312) 322 gave a broad, high level overview of main research areas in AI (logic, search, planning, reasoning under uncertainty, decision making)

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Computer Science CPSC 422 Intelligent Systems Cristina Conati

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  1. Computer Science CPSC 422 Intelligent Systems Cristina Conati

  2. Super Brief Intro • Advanced AI course • Builds upon 322 (and 312) • 322 gave a broad, high level overview of main research areas in AI (logic, search, planning, reasoning under uncertainty, decision making) • We will go into more depth on some of the topics • Look at “Learning” • Study some applications, in the field of Intelligent User Interfaces

  3. Overview • Administrivia • Let’s connect back to 322: what is AI? • Refresher • Examples

  4. People • Instructor • Cristina Conati ( conati@cs.ubc.ca;  office CICSR 125) • Teaching Assistant • HajirRoozbehani (hajir@cs.ubc.ca)

  5. Course Pages • Course website: • http://www.cs.ubc.ca/~conati/422/422-2010World/422-2010.html • This site also includes a calendar with a tentative scheduling of topics. • http://www.cs.ubc.ca/~conati/422/422-2010World/schedule-422-2010.html CHECK IT OFTEN! • Lecture slides • Assignments/Solutions • Other material

  6. Course Material • Main Textbook • Artificial Intelligence: A Modern Approach (AIMA).3rd edition, Russell and Norvig • Can buy cheaper e-version (http://www.coursesmart.com/9780136067337) • Additional textbook: Artificial Intelligence: Foundations of Computational Agents. by Poole and Mackworth. (P&M) • I’ll post the relevant chapters in WebCT as needed • This textbook it is not a substitute for the AIMA textbook

  7. Course Material • Lecture Slides • I'll  post a version of each lecture's slides just after class.  • But I won't post  material that I write on the slides or on the board during class. You'll have to come to class to get that . • You are responsible for all the material in the readings for each class, regardless of whether it has been explicitly covered in class. • You will also need to know all the material covered in class, whether or not it is included in the readings or available on-line.  

  8. Readings • It is strongly recommended that you read the assigned readings before each class. It will help you understand the material better when I lecture • However, there will be some classes that are centered around the discussion of one or more research papers. • You MUST read the papers before coming to class, because • you will have to come up with questions on them and participate to class discussion (more on this later)

  9. How to Get Help? • Use the WebCT Discussion Board for questions on course material (so check it frequently) • That way others can learn from your questions and comments • Use email for personal questions (e.g., grade inquiries or health problems). • Go to office hours (Discussion Board is NOT a good substitute for this) – times below are still tentative, will be finalized next week • Cristina: likely Tu: 3:30-4:30 • Hajir: TBA • Can schedule by appointment if you have a class conflict with the official office hours

  10. Evaluation • Final exam (45%) • midterm exams (30%) • Assignments (15 %) • Class participation and questions for classes based on paper discussion (10%) • But, if your final grade is 20% higher than your midterm grade: • Midterm: 15% • Final: 60 % • To pass: at least 50% in both your overall grade and your final • exam grade

  11. Coursework: Assignments • Submit via Handin by the appointed deadline. • See syllabus for late assignment policy • You will have one Late Assignment Bonus, i.e. you will be allowed to submit one of the assignments up to 2 days late with no penalty (see details in syllabus).

  12. Coursework: assignments • Is collaboration on assignments allowed? Yes, you may work with one (and only one) other person. • That person must also be a student in CPSC 422 this term. • You will have to officially declare that you have collaborated with this person when submitting your assignment. • There will be more details about collaboration policy included in the assignment itself. • Talking about the assignments with anybody other than an official teammate, looking at existing solutions, submitting solutions not worked out by the team members constitute plagiarism • See UBC official regulations on what constitutes plagiarism (pointer in syllabus)

  13. Coursework: discussion-based classes • Discussion-based classes • There will be a few classes during the course that will be centered on reading and discussing one or more research papers • You will have to • come up with critical questions (discussion points) on each of the assigned readings (I will give you the exact number for each set of readings) • Hand in your questions (I’ll give you details on when and how to do this as we go) • Be prepared to present and discuss your questions in class

  14. Coursework: discussion-based classes • First discussion-based class: Tuesday, Jan. 19 • Paper (available on-line from class schedule): • Conati C., Gertner A., VanLehn K., 2002. Using Bayesian Networks to Manage Uncertainty in Student Modeling. User Modeling and User-Adapted Interaction. 12(4) p. 371-417. • Make sure to have at least two questions on this  reading to  discuss  in class.  • Send you questions to *both* conati@cs.ubc.ca and hajir@cs.ubc.ca by 9am on Tuesday, Jan. 19 • Hand in a written copy of the questions at the end of class

  15. Questions on papers • Clarification questions are welcome, but  there should be  at least two that can be used as “discussion points”, i.e. that • Question elements of the presented research (i.e. point out weaknesses) • make connections with the relevant techniques presented in class (Bnets in case of the first discussion paper) • Make connections/comparisons with other papers (once we have covered enough papers to do this) • Examples of questions of different quality are available from the syllabus

  16. Missing Assignments or Exams • If serious circumstances (like an illness or other personal matters) cause you to be late for an assignment or to miss an exam: • If you miss an assignment, your score will be reweighed to exclude that assignment • If you miss the midterm, its weight will be shifted to the final • thus, your total grade will be 75 % final, 25% coursework • If you miss the final, you'll have to write a make-up final as soon as possible • If you have flu-like symptoms, you do not need to bring a doctor’s note, but you do need to report your illness at http://www.students.ubc.ca/health/flu.cfm • For all other circumstamces, you do need official certification (e.g. doctor’s note)

  17. To Summarize • All the course logistics are described in the course syllabus • http://www.cs.ubc.ca/~conati/422/422-2010World/422-2010.html • Make sure to read it and that you agree with the course rules before deciding to take the course

  18. Department of Computer ScienceUndergraduate Events This Week How to Prepare for the Tech Career Fair Date: Wed. Jan 6 Time: 5 – 6:30 pm Location: DMP 110 Resume Writing Workshop (for non-coop students) Date: Thurs. Jan 7 Time: 12:30 – 2 pm Location: DMP 201 CSSS Movie Night Date: Thurs. Jan 7 Time: 6 – 10 pm Location: DMP 310 Movies: “Up” & “The Hangover” (Free Popcorn & Pop) Drop-In Resume Edition Session Date: Mon. Jan 11 Time: 11 am – 2 pm Location: Rm 255, ICICS/CS Bldg Industry Panel Speakers: Managers from Google, IBM, Microsoft, TELUS, etc. Date: Tues. Jan 12 Time: Panel: 5:15 – 6:15 pm; Networking: 6:15 – 7:15 pm Location: Panel: DMP 110; Networking: X-wing Undergrad Lounge Tech Career Fair Date: Wed. Jan 13 Time: 10 am – 4 pm Location: SUB Ballroom

  19. Overview • Administrivia • Let’s connect back to 322: what is AI? • Refresher • Examples

  20. What is Artificial Intelligence? From: Russell S. and Norvig, P.``Artificial Intelligence: A Modern Approach.''

  21. Systems that act like humans • Turing test (1950): Can a human interrogator tell whether (written) responses to her (written) questions come from a human or a machine? • Natural Language Processing • Knowledge Representation • Automated Reasoning • Machine Learning • Total Turing Test (extended to include physical aspects of human behavior) • Computer Vision • Robotic

  22. Has any AI System Passed the Tutoring Test? • Not the full blown one (see http://www.loebner.net/Prizef/loebner-prize.html) • Loebner Prize initiative has a 100,000 and a Gold Medal for the first computer whose responses were indistinguishable from a human's.No one has won this yet • Each year a monetary prize and a bronze medal are awarded to the most human-like computer. • The winner is the best entry relative to other entries that year, irrespective of how good it is in an absolute sense • Variations restricted to specific tasks requiring some form of intelligence • Check Winner 2009 (http://www.worldsbestchatbot.com/Do_Much_More) • And you can play with winner from 2008 (Elbot)

  23. But why do we want an intelligent system to act like a human? • Because for many tasks, humans are still the Gold Standard

  24. But why do we want an intelligent system to act like a human?

  25. Generating Multimedia Presentations • Zhou, Wen, and Aggarwal. A Graph-Matching Approach to Dynamic Media Allocation • in Intelligent Multimedia Interfaces. Best Paper Award at Intelligent User Interfaces 2005. • Algorithm to effectively allocate text and graphics in multimedia presentations • Empirical Validation • System (RIA) output on 50 user queries (real estate and tourist guide application) • Media allocation on same queries by two multimedia UI designers • Third expert “blindly” ranked all responses • Results • RIA best/co-best in 17 cases • Minor differences in 28 of the remaining 33 cases

  26. Why Replicate Human Behavior, Including its “Limitations”? • AI and Entertainment • E.g. Façade, a one-act interactive dramahttp://www.quvu.net/interactivestory.net/#publications • Sometime these limitations can be useful, e.g. • Supporting Human Learning via Peer interaction (Goodman, B., Soller, A., Linton, F., and Gaimari, R. (1997) Encouraging Student Reflection and Articulation using a Learning Companion.Proceedings of the AI-ED 97 World Conference on Artificial Intelligence in Education, Kobe, Japan, 151-158.) • Supporting Human Learning via teachable agents • (Leelawong, K., & Biswas, G. Designing Learning by Teaching Agents: The Betty's Brain System, International Journal of Artificial Intelligence in Education, vol. 18, no. 3, pp. 181-208, 2008

  27. What is Artificial Intelligence?

  28. Systems That Think Like Humans • Use Computational Models to Understand the Actual Workings of Human Mind • Devise/Choose a sufficiently precise theory of the mind • Express it as a computer program • Check match between program and human behavior (actions and timing) on similar tasks • Tight connections with Cognitive Science • Also known as descriptive approaches to AI

  29. Some Examples • Newell and Simon’s GPS (General Problem Solver, 1961) to test means-end approach as general problem solving strategy • John Anderson’s ACT-R cognitive architecture (http://act-r.psy.cmu.edu/) • Anderson, J. R. & Lebiere, C. (1998). The atomic components of thought. Erlbaum; • Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y . (2004). An integrated theory of the mind. Psychological Review 111, (4). 1036-1060. • SOAR cognitive architecture (http://sitemaker.umich.edu/soar) • Newell, A. 1990. Unified Theories of Cognition. Cambridge, Massachusetts: Harvard University Press.

  30. ACT-R Models for Intelligent Tutoring Systems

  31. Cognitive Science Computer Science (AI, HCI) ILE Education ACT-R Models for Intelligent Tutoring Systems Intelligent Tutoring Systems (ITS) • Intelligent agents that support human learning and training • By autonomously and intelligentlyadapting to learners’ specific needs, like good teachers do

  32. ACT-R Models for Intelligent Tutoring Systems • One of ACT-R main assumptions: Cognitive skills (procedural knowledge) are represented as production rules: • IFthis situation is TRUE, THEN do X • An ACT-R model representing expertise in a given domain requires writing a set of production rules mimicking how a human would reason to perform tasks in that domain • An ACT-R model for an ITS encodes all the reasoning steps necessary to solve problems in the target domain • Example: rules describing how to solve • 5x+3=30

  33. ACT-R Models for Intelligent Tutoring Systems • Eq: 5x+3=30 ; Goals: [Solve for x] • Rule: To solve for x when there is only one occurrence, unwrap (isolate) x. • Eq:5x+3=30 ; Goals: [Unwrap x] • Rule: To unwrap ?V, find the outermost wrapper ?W of ?V and remove ?W • Eq: 5x+3=30; Goals: [Find wrapper ?W of x; Remove ?W] • Rule: To find wrapper ?W of ?V, find the top level expression ?E on side of equation containing ?V, and set ?W to part of ?E that does not contain ?V • Eq: 5x+3=30; Goals: [Remove “+3”] • Rule: To remove “+?E”, subtract “+?E” from both sides • Eq: 5x+3=30; Goals: [Subtract “+3” from both sides] • Rule: To subtract “+?E” from both sides …. • Eq: 5x+3-3=30-3

  34. Model Tracing • Given a rule-based representation of a target domain (e.g. algebra), • an “expert model” can trace student performance by firing rules and do a stepwise comparison of rule outcome with student action • Mismatches signal incorrect student knowledge that requires tutoring • Knowledge tracing extends model tracing to assess probability that a student knows domain rules given observed actions • These models showed good fit with student performance, indicating value of the ACT-R theory • Also, the Cognitive Tutors based on this model are great examples of AI success – used in thousands of high schools in the USA (http://www.carnegielearning.com/success.cfm)

  35. What is Artificial Intelligence?

  36. Systems that Think Rationally • Logic: formalize right thinking, i.e. irrefutable reasoning processes. • Logistic tradition in AI aims to build computational frameworks based on logic. • Then use these frameworks to build intelligent systems • You have seen some examples in 322 (Propositional Logic) and 312 (Logic Programming) • We will look at more advanced logic-based representations • Semantic Networks • Ontologies

  37. Systems that Think Rationally • Main Research Problems/Challenges • Proving Soundness and Completeness of various formalisms • How to represent often informal and uncertain domain knowledge and formalize it in logic notation • Computational Complexity • Tradeoff between expressiveness andtractability in logic-based systems H. J. Levesque and R. J. Brachman. Expressiveness and tractability in knowledge representation and reasoning. Computational Intelligence, 3(2):78--93, 1987.

  38. What is Artificial Intelligence?

  39. Systems that Act Rationally • The “think rationally” approach focuses on correct inference • But more is needed for rational behavior, e.g. • How to behave when there is no provably correct thing to do (i.e. reasoning under uncertainty) • Fully reactive behavior (instinct vs. reason)

  40. AI as Study and Design of Intelligent Agents (Poole and Mackworth, 1999) • An intelligent agent is such that • Its actions are appropriate for its goals and circumstances • It is flexible to changing environments and goals • It learns from experience • It makes appropriate choices given perceptual limitations and limited resources • This definition drops the constraint of cognitive plausibility • Same as building flying machines by understanding general principles of flying (aerodynamic) vs. by reproducing how birds fly • Normative vs. Descriptive theories of Intelligent Behavior

  41. Intelligent Agents • In AI, artificial agents that have a physical presence in the world are usually known as Robots • Robotics is the field primarily concerned with the implementation of the physical aspects of a robot (i.e. perception of the physical environment, actions on the environment) • Another class of artificial agents include interface agents, for either stand alone or Web-based applications (e.g. intelligent desktop assistants, recommender systems, intelligent tutoring systems) • Interface agents don’t have to worry about interaction with the physical environment, but share all other fundamental components of intelligent behavior with robots • We will focus on these agents in this course

  42. Knowledge Representation Machine Learning Natural Language Generation + Robotics + Human Computer /Robot Interaction Intelligent Agents in the World Reasoning + Decision Theory Natural Language Understanding + Computer Vision Speech Recognition + Physiological Sensing Mining of Interaction Logs

  43. The “Act Rationally” view • This is the view that was adopted in cpsc322, and that we will • continue to explore in the first part of the course • Reasoning under uncertainty: Bayesian networks and Hidden Markov Models • Brief review, some applications, approximate inference • Decision Making: planning under uncertainty • Markov Decision Processes, review • Partially Observable Markov Decision Processes (POMDP) • Learning • Decision Trees, Neural Networks, Learning Bayesian Networks, Reinforcement Learning

  44. Next Class • Review of Bayesian networks, and representational issues • IMPORTANT: You must be familiar with the basic • concepts of probability theory. I won’t go over them. • For are refresher: Ch. 13 in textbook and review slides • posted in the schedule page (Th. slot)

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