1. 2002.09.05 - SLIDE 1IS 202 - Fall 2002 Lecture 04: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30 am - 12:00 am Fall 2002 SIMS 202: Information Organization and Retrieval Credits to Warren Sack for some of the slides in this lecture

2. 2002.09.05 - SLIDE 2IS 202 - Fall 2002 Today Review of Categorization From Cognitive Science to AI The Vocabulary Problem Artificial Intelligence, Knowledge Representation,and Commonsense Photo Project Assignment 2 Check-In

3. 2002.09.05 - SLIDE 3IS 202 - Fall 2002 Categorization Processes of categorization are fundamental to human cognition Categorization is messier than our computer systems would like Human categorization is characterized by –Family resemblances –Prototypes –Basic-level categories Considering how human categorization functions is important in the design of information organization and retrieval systems

4. 2002.09.05 - SLIDE 4IS 202 - Fall 2002 Categorization Classical categorization –Necessary and sufficient conditions for membership –Generic-to-specific monohierarchical structure Modern categorization –Characteristic features (family resemblances) –Centrality/typicality (prototypes) –Basic-level categories

5. 2002.09.05 - SLIDE 5IS 202 - Fall 2002 Properties of Categorization Family Resemblance –Members of a category may be related to one another without all members having any property in common Prototypes –Some members of a category may be “better examples” than others, i.e., “prototypical” members

6. 2002.09.05 - SLIDE 6IS 202 - Fall 2002 Basic-Level Categorization Perception –Overall perceived shape –Single mental image –Fast identification Function –General motor program Communication –Shortest, most commonly used and contextually neutral words –First learned by children Knowledge Organization –Most attributes of category members stored at this level

7. 2002.09.05 - SLIDE 7IS 202 - Fall 2002 Information Hierarchy Wisdom Knowledge Information Data

8. 2002.09.05 - SLIDE 8IS 202 - Fall 2002 Information Hierarchy Knowledge Information Wisdom Data

9. 2002.09.05 - SLIDE 9IS 202 - Fall 2002 Today’s Thinkers/Tinkerers George Furnas http://www.si.umich.e du/~furnas/ Marvin Minsky http://web.media.mit. edu/~minsky/ Doug Lenat http://www.cyc.com/st aff.html

10. 2002.09.05 - SLIDE 10IS 202 - Fall 2002 Psychology Methodology Theorizing Experimenting

11. 2002.09.05 - SLIDE 11IS 202 - Fall 2002 Computer Science Methodology Theorizing System Building

12. 2002.09.05 - SLIDE 12IS 202 - Fall 2002 Cognitive Science Methodology Theorizing Experimenting System Building

13. 2002.09.05 - SLIDE 13IS 202 - Fall 2002 What is Cognitive Science? A definition from Howard Gardner (1986) The Mind’s New Science; the five symptoms of cognitive science; the first two are central, the next three are strategic –(1) Mental representations –(2) Computers –(3) Emphasis –(4) Epistemology –(5) Interdisciplinarity

14. 2002.09.05 - SLIDE 14IS 202 - Fall 2002 Symptom 1 of Cognitive Science: Mental Representations To study human cognition it is necessary to posit mental representations and examine those representations separately from the “low level” biological or neurological, on one hand, and also separately from the “high level” social or cultural, on the other hand. (adapted from Gardner, 1986)

15. 2002.09.05 - SLIDE 15IS 202 - Fall 2002 Symptom 2 of Cognitive Science: Computers Computers are central to any understanding of the human mind. They are essential both as tools, but also as models of how the mind works. (adapted from Gardner, 1986)

16. 2002.09.05 - SLIDE 16IS 202 - Fall 2002 Symptom 3 of Cognitive Science: Emphasis Cognitive scientists deliberately de- emphasize certain factors which may be important for cognitive functioning but whose inclusion would unnecessarily complicate the cognitive-scientific enterprise. These de-emphasized factors include emotional affect, historical, cultural, and other types of context (e.g., issues of embodiment and the senses). (adapted from Gardner, 1986)

17. 2002.09.05 - SLIDE 17IS 202 - Fall 2002 Symptom 4 of Cognitive Science: Epistemology Cognitive science is concerned with an area that has historically been a part of philosophy, namely the domain of epistemology. (adapted from Gardner, 1986)

18. 2002.09.05 - SLIDE 18IS 202 - Fall 2002 Symptom 5 of Cognitive Science: Interdisciplinarity Cognitive science is an interdisciplinary enterprise. (adapted from Gardner, 1986)

19. 2002.09.05 - SLIDE 19IS 202 - Fall 2002 Disciplines of Cognitive Science Philosophy Psychology Artificial Intelligence Linguistics Anthropology Neuroscience

20. 2002.09.05 - SLIDE 20IS 202 - Fall 2002 The Birth of Cognitive Science Symposium on Information Theory, MIT, 10-12 September 1956 –Allen Newell & Herbert Simon, “Logic Theory Machine” –Noam Chomsky, “Three Models of Language” –George Miller, “The Magical Number Seven”

21. 2002.09.05 - SLIDE 21IS 202 - Fall 2002 The Birth of AI Rockefeller-sponsored Institute at Dartmouth College, Summer 1956 –John McCarthy, Dartmouth (->MIT->Stanford) –Marvin Minsky, MIT (geometry) –Herbert Simon, CMU (logic) –Allen Newell, CMU (logic) –Arthur Samuel, IBM (checkers) –Alex Bernstein, IBM (chess) –Nathan Rochester, IBM (neural networks) –Etc.

22. 2002.09.05 - SLIDE 22IS 202 - Fall 2002 Definition of AI “... artificial intelligence [AI] is the science of making machines do things that would require intelligence if done by [humans]” (Minsky, 1963)

23. 2002.09.05 - SLIDE 23IS 202 - Fall 2002 The Goals of AI Are Not New Ancient Greece –Daedalus’ automata Judaism’s myth of the Golem 18 th century automata –Singing, dancing, playing chess? Mechanical metaphors for mind –Clock –Telegraph/telephone network –Computer

24. 2002.09.05 - SLIDE 24IS 202 - Fall 2002 Some Areas of AI Knowledge Representation Programming Languages Natural Language Understanding Speech Understanding Vision Robotics Planning Machine Learning Expert Systems Qualitative Simulation

25. 2002.09.05 - SLIDE 25IS 202 - Fall 2002 Furnas: The Vocabulary Problem People use different words to describe the same things –“If one person assigns the name of an item, other untutored people will fail to access it on 80 to 90 percent of their attempts.” –“Simply stated, the data tell us there is no one good access term for most objects.”

26. 2002.09.05 - SLIDE 26IS 202 - Fall 2002 The Vocabulary Problem How is it that we come to understand each other? –Shared context –Dialogue How can machines come to understand what we say? –Shared context? –Dialogue?

27. 2002.09.05 - SLIDE 27IS 202 - Fall 2002 Vocabulary Problem Solutions? Furnas et al. –Make the user memorize precise system meanings –Have the user and system interact to identify the precise referent Minsky and Lenat –Give the system “commonsense” so it can understand what the user’s words can mean

28. 2002.09.05 - SLIDE 28IS 202 - Fall 2002 Lenat on the Vocabulary Problem “The important point is that users will be able to find information without having to be familiar with the precise way the information is stored, either through field names or by knowing which databases exist, and can be tapped.”

29. 2002.09.05 - SLIDE 29IS 202 - Fall 2002 Minsky on the Vocabulary Problem “To make our computers easier to use, we must make them more sensitive to our needs. That is, make them understand what we mean when we try to tell them what we want. […] If we want our computers to understand us, we’ll need to equip them with adequate knowledge.”

30. 2002.09.05 - SLIDE 30IS 202 - Fall 2002 Commonsense Commonsense is background knowledge that enables us to understand, act, and communicate Things that most children know Minsky on commonsense: –“Much of our commonsense knowledge information has never been recorded at all because it has always seemed so obvious we never thought of describing it.”

31. 2002.09.05 - SLIDE 31IS 202 - Fall 2002 Commonsense Example “I want to get inexpensive dog food.” The food is not made out of dogs. The food is not for me to eat. Dogs cannot buy their own food. I am not asking to be given dog food. I am not saying that I want to understand why some dog food is inexpensive. The dog food is not more than $5 per can.

32. 2002.09.05 - SLIDE 32IS 202 - Fall 2002 Engineering Commonsense Use multiple ways to represent knowledge Acquire huge amounts of that knowledge Find commonsense ways to reason with it (“knowledge about how to think”)

33. 2002.09.05 - SLIDE 33IS 202 - Fall 2002 CYC Decades long effort to build commonsense knowledge-base Storied past 100,000 basic concepts 1,000,000 assertions about the world The validity of Cyc’s assertions are context-dependent (default reasoning)

34. 2002.09.05 - SLIDE 34IS 202 - Fall 2002 Cyc’s Top-Level Ontology Fundamentals Top Level Time and Dates Types of Predicates Spatial Relations Quantities Mathematics Contexts Groups "Doing" Transformations Changes Of State Transfer Of Possession Movement Parts of Objects Professions Composition of Substances Agents Organizations Actors Roles Emotion Propositional Attitudes Social Biology Chemistry Physiology General Medicine http://www.cyc.com/cyc-2-1/toc.html Materials Waves Devices Construction Financial Food Clothing Weather Geography Transportation Information Perception Agreements Linguistic Terms Documentation

35. 2002.09.05 - SLIDE 35IS 202 - Fall 2002 OpenCYC Cyc’s knowledge-base is now coming online –http://www.opencyc.org/ How could Cyc’s knowledge-base affect the design of information organization and retrieval systems?

36. 2002.09.05 - SLIDE 36IS 202 - Fall 2002 Multiple Representations Minksy –“I think this is what brains do instead: Find several ways to represent each problem and to represent the required knowledge. Then when one method fails to solve a problem, you can quickly switch to another description.” Furnas –“But regardless of the number of commands or objects in a system and whatever the choice of their ‘official’ names, the designer must make many, many alternative verbal access routes to each.”

37. 2002.09.05 - SLIDE 37IS 202 - Fall 2002 AI or IA? Artificial Intelligence (AI) –Make machines as smart as (or smarter than) people Intelligence Amplification (IA) –Use machines to make people smarter

38. 2002.09.05 - SLIDE 38IS 202 - Fall 2002 Assignment 0 Check-In Deliverables –Personal web page –Assignments page –Email address –Focus statement –Online Questionnaire Feedback –Spell-check and grammar-check –Simple vs. skeletal

39. 2002.09.05 - SLIDE 39IS 202 - Fall 2002 Assignment 2 Check-In Deliverables –Persona description (brief) –Scenario description (brief) –Annotated user experience storyboard –Group web site –Work distribution table on your group web site –Photos for your application idea Feedback –Questions, comments, problems?

40. 2002.09.05 - SLIDE 40IS 202 - Fall 2002 Homework (!) Read –Chapters 3 and 5 in The Organization of Information (OI) Assignment 2: Photo Use Scenario –Due by Thursday, September 12

41. 2002.09.05 - SLIDE 41IS 202 - Fall 2002 Next Time Metadata Introduction (RRL)