1. Current Research William J. Rapaport http://www.cse.buffalo.edu/~rapaport CVA Research Group SNePS Research Group (SNeRG) Center for Cognitive Science

2. Cognitive Science = def interdisciplinary study of mind/cognition –(AI, PHI, PSY, LIN, etc.) Artificial Intelligence (“good old-fashioned classical symbolic AI”) –Computational philosophy –Knowledge representation for natural-language understanding Computational linguistics Knowledge representation and reasoning

3. Computational Philosophy Philosophy as source of computational problems; computational solutions to philosophical problems Understanding understanding: Syntax suffices for semantics –How a computational cognitive agent can pass a Turing Test E.g., SNePS/Cassie –& overcome the Chinese-Room-Argument objections to the Turing Test It’s possible to pass TT without really thinking

4. Knowledge Representation for Natural-Language Understanding Computational contextual vocabulary acquisition (CVA) –Based on Karen Ehrlich’s 1995 CS PhD dissertation –$$ from:NSF ROLE Program Research On Learning and Education In STEM –Science, Technology, Engineering, and Mathematics –Formerly known as “SMET” –Joint research with Michael Kibby, GSE/LAI

5. CVA: From algorithm to curriculum People do “incidental” CVA: –Know more words than explicitly taught –Learn the meanings of most words from context –Unconsciously –How?

6. CVA: From Algorithm to Curriculum (continued) People do “deliberate” CVA –You’re reading; –You understand everything you read, until… –You come across a new word –Not in dictionary –No one to ask –So, you try to figure out its meaning from context + background knowledge –How?

7. What does ‘brachet’ mean?

8. (From Malory’s Morte D’Arthur [page # in brackets]) 1. There came a white hart running into the hall with a white brachet next to him, and thirty couples of black hounds came running after them. [66] 2.As the hart went by the sideboard, the white brachet bit him. [66] 3.The knight arose, took up the brachet and rode away with the brachet. [66] 4.A lady came in and cried aloud to King Arthur, “Sire, the brachet is mine”. [66] 10.There was the white brachet which bayed at him fast. [72] 18. The hart lay dead; a brachet was biting on his throat, and other hounds came behind. [86]

9. CVA: From algorithm… (continued) CVA studied by computational linguists –word-sense disambiguation Given ambiguous word and list of all meanings, determine the correct meaning Multiple-choice test –CVA as we do it: Given new word, compute its meaning Essay question

10. Implementation SNePS (Stuart C. Shapiro & SNeRG) : –Intensional, propositional semantic-network knowledge-representation & reasoning system –Node-based & path-based reasoning I.e., logical inference & generalized inheritance –SNeBR belief revision system Used for revision of definitions –SNaLPS natural-language input/output –“Cassie”: computational cognitive agent

11. How It Works SNePS represents: – background knowledge + text information in a single, consolidated semantic network Algorithms search network for slot-fillers for definition frame Search is guided by desired slots –E.g., prefers general info over particular info, but takes what it can get

12. Cassie learns what “brachet” means: Background info about:harts, animals, King Arthur, etc. No info about:brachets Input:formal-language version of simplified English A hart runs into King Arthur’s hall. In the story, B17 is a hart. In the story, B18 is a hall. In the story, B18 is King Arthur’s. In the story, B17 runs into B18. A white brachet is next to the hart. In the story, B19 is a brachet. In the story, B19 has the property “white”. Therefore, brachets are physical objects. (deduced while reading; Cassie believes that only physical objects have color)

13. --> (defineNoun "brachet") Definition of brachet: Class Inclusions: phys obj, Possible Properties: white, Possibly Similar Items: animal, mammal, deer, horse, pony, dog, I.e., a brachet is a physical object that can be white and that might be like an animal, mammal, deer, horse, pony, or dog

14. A hart runs into King Arthur’s hall. A white brachet is next to the hart. The brachet bites the hart’s buttock. The knight picks up the brachet. The knight carries the brachet. --> (defineNoun "brachet") Definition of brachet: Class Inclusions: animal, Possible Actions: bite buttock, Possible Properties: small, white, Possibly Similar Items: mammal, pony,

15. A hart runs into King Arthur’s hall. A white brachet is next to the hart. The brachet bites the hart’s buttock. The knight picks up the brachet. The knight carries the brachet. The lady says that she wants the brachet. The brachet bays at Sir Tor. [background knowledge: only hunting dogs bay] --> (defineNoun "brachet") Definition of brachet: Class Inclusions: hound, dog, Possible Actions: bite buttock, bay, hunt, Possible Properties: valuable, small, white, I.e. A brachet is a hound (a kind of dog) that can bite, bay, and hunt, and that may be valuable, small, and white.

16. General Comments System’s behavior  human protocols System’s definition  OED’s definition: = A brachet is “a kind of hound which hunts by scent” Our inferential search algorithms are “syntactic semantics” in action

17. CVA: … to curriculum CVA studied by: –Psychologists –Reading specialists –L1 researchers –L2 researchers/educators (e.g., ESL) Contextual cues to aid in CVA: –Spatial/temporal info, class membership, etc. But what to do with them?

18. CVA: … to curriculum Is this an algorithm? (Clarke & Nation 1980): 1.Look at word & context a)determine POS 2.Look at grammatical context a)who does what to whom? 3.Look at wider context a)Search for spatial/temporal/classification cues… 4.Guess the word; check your guess

19. CVA: From Algorithm to Curriculum “guess the word” = “then a miracle occurs” Surely we computer scientists can “be more explicit”!

20. CVA: From algorithm to curriculum … and back again! Treat “guess” as a procedure call –Fill in the details with our algorithm –Convert the algorithm into a curriculum To enhance students’ abilities to use deliberate CVA strategies –To improve reading comprehension of STEM texts And use knowledge gained from CVA case studies to improve the algorithm I.e., use Cassie to learn how to teach humans & use humans to learn how to teach Cassie

21. Problem in Converting Algorithm into Curriculum “A knight picks up a brachet and carries it away …” Cassie: –Has “perfect memory” –Is “perfect reasoner” –Automatically infers that brachet is small People don’t always realize this: –May need prompting: How big is the brachet? –May need relevant background knowledge –May need help in drawing inferences Teaching CVA =? teaching general reading comprehension –Vocabulary knowledge correlates with reading comprehension

22. CVA & Science Education Original goal: CVA in & for science education –Use CVA to improve reading of STEM materials A side effect: CVA as science education –There are no ultimate authorities to consult No answers in the back of the book of life! As true for physics as for reading –  Goal of education = To learn how to learn on one’s own Help develop confidence & desire to use that skill –CVA as sci. method in miniature furthers this goal: Find clues/evidence (gathering data) Integrate them with personal background knowledge Use together to develop new theory (e.g., new meaning) Test/revise new theory (on future encounters with word)

23. Conclusion Developing a computational theory of CVA, which can become … a useful educational technique for improving [STEM] vocabulary and reading comprehension a model of the scientific method a useful tool for learning on one’s own.

24. Meetings & Websites SNeRG: −Fridays, 9:00-11:00, Bell 242  starting Aug. 29 (tomorrow)  www.cse.buffalo.edu/sneps Center for Cognitive Science: −Wednesdays, 2:00-4:00, Park 280  starting Sept. 3  wings.buffalo.edu/cogsci CVA: −Mondays, 2:00-3:30, Baldy 17  starting Sept. 8  www.cse.buffalo.edu/~rapaport/cva.html

25. Courses Fall 2003: –CSE 663: Knowledge Representation & Reasoning Spring 2004: –CSE 510: Philosophy of Computer Science –CSE 7xx: Seminar (probably on CVA)

26. Question (objection): Why not use a dictionary? Because: People are lazy (!) Dictionaries are not always available Dictionaries are always incomplete Dictionary definitions are not always useful –‘chaste’ = df pure, clean /  “new dishes are chaste” Most words learned via incidental CVA, not via dictionaries

27. Question (objection): Teaching computers  teaching humans! But : Our goal: –Not: teach people to “think like computers” –But: to explicate computable & teachable methods to hypothesize word meanings from context AI as computational psychology: –Devise computer programs that are essentially faithful simulations of human cognitive behavior –Can tell us something about human mind. We are teaching a machine, to see if what we learn in teaching it can help us teach students better.