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Knowledge Representation and Reasoning. Stuart C. Shapiro Professor, CSE Director, SNePS Research Group Member, Center for Cognitive Science Faculty Member, Interdisciplinary MS in Computational Linguistics. Introduction. Long-Term Goal. Theory and Implementation of

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Knowledge representation and reasoning

Knowledge Representation and Reasoning

Stuart C. Shapiro

Professor, CSE

Director, SNePS Research Group

Member, Center for Cognitive Science

Faculty Member, Interdisciplinary MS

in Computational Linguistics

S.C. Shapiro


S.C. Shapiro

Long term goal
Long-Term Goal

  • Theory and Implementation of


    Computerized Cognitive Agent/Robot

  • and Supporting Research in

    Artificial Intelligence

    Cognitive Science

    Computational Linguistics.

S.C. Shapiro

Research areas
Research Areas

  • Knowledge Representation and Reasoning

  • Cognitive Robotics

  • Natural-Language Understanding

  • Natural-Language Generation.

S.C. Shapiro


  • A computational cognitive agent that can:

    • Understand and communicate in English;

    • Discuss specific, generic, and “rule-like” information;

    • Reason;

    • Discuss acts and plans;

    • Sense;

    • Act;

    • Maintain a model of itself;

    • Remember and report what it has sensed and done.

S.C. Shapiro


  • A computational cognitive agent

    • Embodied in hardware

    • or Software-Simulated

    • Based on SNePS and GLAIR.

S.C. Shapiro

Glair architecture
GLAIR Architecture

Grounded Layered Architecture with Integrated Reasoning

Knowledge Level


Perceptuo-Motor Level


Sensory-Actuator Level





S.C. Shapiro


  • Knowledge Representation and Reasoning

    • Propositions as Terms

  • SNIP: SNePS Inference Package

    • Specialized connectives and quantifiers

  • SNeBR: SNePS Belief Revision

  • SNeRE: SNePS Rational Engine

  • Interface Languages

    • SNePSUL: Lisp-Like

    • SNePSLOG: Logic-Like

    • GATN for Fragments of English.

S.C. Shapiro

Example cassies worlds
Example Cassies& Worlds

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S.C. Shapiro


S.C. Shapiro

Uxo remediation
UXO Remediation

Corner flag


Drop-off zone


NonUXO object



Corner flag

Corner flag




Safe zone

S.C. Shapiro

Princess from the trial the trail
Princess from “The Trial, The Trail”

A VR drama by Josephine Anstey

S.C. Shapiro

Vacuum cleaner cassie
Vacuum Cleaner Cassie

Using Byron Weber Becker’s Java Karel

S.C. Shapiro

Magellan pro tm mobile robot from irobot
Magellan ProTM Mobile RobotfromiRobot

S.C. Shapiro

Sample research issues indexicals
Sample Research Issues:Indexicals

S.C. Shapiro

Representation and use of indexicals
Representation and Use of Indexicals

  • Words whose meanings are determined by occasion of use

  • E.g. I, you, now, then, here, there

  • Deictic Center <*I, *YOU, *NOW>

  • *I: SNePS term representing Cassie

  • *YOU: person Cassie is talking with

  • *NOW: current time.

S.C. Shapiro

Analysis of indexicals in input
Analysis of Indexicals(in input)

  • First person pronouns: *YOU

  • Second person pronouns: *I

  • “here”: location of *YOU

  • Present/Past relative to *NOW.

S.C. Shapiro

Generation of indexicals
Generation of Indexicals

  • *I: First person pronouns

  • *YOU: Second person pronouns

  • *NOW: used to determine tense and aspect.

S.C. Shapiro

Use of indexicals 1
Use of Indexicals 1

Come here.

S.C. Shapiro

Use of indexicals 2
Use of Indexicals 2

Come here.

I came to you, Stu.

I am near you.

S.C. Shapiro

Use of indexicals 3
Use of Indexicals 3

Whoam I?

Your name is ‘Stu’

and you are a person.


I am talking to you.


I am talking to you, Bill.


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Use of indexicals 4
Use of Indexicals 4


I found you.

I am looking at you.

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Use of indexicals 5
Use of Indexicals 5


I found you.

I am looking at you.

I came to you.

I am near you.

S.C. Shapiro

Use of indexicals 6
Use of Indexicals 6


Your name is ‘Bill’

and you are a person.


I am the FEVAHR

and my name is ‘Cassie’.


I talked to Stu

and I am talking to you.

S.C. Shapiro

Current Research Issues: Distinguishing Perceptually Indistinguishable ObjectsPh.D. Dissertation, John F. Santore

S.C. Shapiro

S.C. Shapiro

Next steps
Next Steps

  • How do people do this?

    • Currently analyzing protocol experiments

  • Getting Cassie to do it.

S.C. Shapiro

Current Research Issues: Representation & Reasoningwith Arbitrary ObjectsStuart C. Shapiroin conjunction with Development of SNePS 3

S.C. Shapiro

Classical representation
Classical Representation

  • Clyde is gray.

    • Gray(Clyde)

  • All elephants are gray.

    • x(Elephant(x)  Gray(x))

  • Some elephants are albino.

    • x(Elephant(x) & Albino(x))

  • Why the difference?

S.C. Shapiro

Representation using arbitrary indefinite objects
Representation Using Arbitrary & Indefinite Objects

  • Clyde is gray.

    • Gray(Clyde)

  • Elephants are gray.

    • Gray(any x Elephant(x))

  • Some elephants are albino.

    • Albino(some x Elephant(x))

S.C. Shapiro

Structural subsumption among arbitrary indefinite objects
Structural Subsumption Among Arbitrary & Indefinite Objects

(any x Elephant(x))

(any x Albino(x) & Elephant(x))

(some x Albino(x) & Elephant(x))

(some x Elephant(x))

If x subsumes y, then P(x)  P(y)

S.C. Shapiro

Example runs in sneps 3
Example (Runs in SNePS 3)

Hungry(any x Elephant(x)

& Eats(x, any y Tall(y)

& Grass(y)

& On(y, Savanna)))

Hungry(any u Albino(u)

& Elephant(u)

& Eats(u, any v Grass(v)

& On(v, Savanna)))

S.C. Shapiro

Axiomatic subsumption runs in sneps 3
Axiomatic Subsumption(Runs in SNePS 3)

Animal(any x Mammal(x))

Hairy(any x Mammal(x))

Mammal(any x Dog(x))


Hairy(any x Dog(x))



S.C. Shapiro

Next steps1
Next Steps

  • Finish theory and implementation of arbitrary and indefinite objects.

  • Extend to other generalized quantifiers

    • Such as most, many, few, no, both, 3 of, …

S.C. Shapiro

For more information
For More Information

  • Shapiro:

  • SNePS Research Group:

    • Meets Fridays 9-11, 242 Bell Hall

    • Join us!

S.C. Shapiro