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Current Research William J. Rapaport http://www.cse.buffalo.edu/~rapaport CVA Research Group SNePS Research Group (SNeRG) Center for Cognitive Science Cognitive Science = def interdisciplinary study of mind/cognition (AI, PHI, PSY, LIN, etc.) Artificial Intelligence

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Current research l.jpg
Current Research

William J. Rapaport

http://www.cse.buffalo.edu/~rapaport

CVA Research Group

SNePS Research Group (SNeRG)

Center for Cognitive Science


Cognitive science l.jpg
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


  • Computational philosophy l.jpg
    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


    Knowledge representation for natural language understanding l.jpg
    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


    Cva from algorithm to curriculum l.jpg
    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?


    Cva from algorithm to curriculum continued l.jpg
    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?



    From malory s morte d arthur page in brackets l.jpg
    (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]

    • As the hart went by the sideboard, the white brachet bit him.[66]

    • The knight arose, took up the brachet and rode away with the brachet.[66]

    • A lady came in and cried aloud to King Arthur, “Sire, the brachet is mine”.[66]

    • There was the white brachet which bayed at him fast.[72]

      18. The hart lay dead; a brachetwas biting on his throat, and other hounds came behind.[86]


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    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 


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    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


    How it works l.jpg
    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


    Slide12 l.jpg

    Cassie learns what “brachet” means:Background info about: harts, animals, King Arthur, etc.No info about: brachetsInput: formal-language version of simplified EnglishA 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)


    Slide13 l.jpg

    --> (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


    Slide14 l.jpg

    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,


    Slide15 l.jpg

    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.


    General comments l.jpg
    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


    Cva to curriculum l.jpg
    CVA: … to curriculum

    • Is this an algorithm? (Clarke & Nation 1980):

      • Look at word & context

        • determine POS

      • Look at grammatical context

        • who does what to whom?

      • Look at wider context

        • Search for spatial/temporal/classification cues…

      • Guess the word; check your guess


    Cva from algorithm to curriculum18 l.jpg
    CVA: From Algorithm to Curriculum

    • “guess the word”

      =

      “then a miracle occurs”

    • Surely we computer scientists can “be more explicit”!


    Cva from algorithm to curriculum and back again l.jpg
    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


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    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


    Courses l.jpg
    Courses

    • Fall 2003:

      • CSE 663: Knowledge Representation & Reasoning

    • Spring 2004:

      • CSE 510: Philosophy of Computer Science

      • CSE 7xx: Seminar (probably on CVA)


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