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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

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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

S.C. Shapiro


Introduction

S.C. Shapiro


Long-Term Goal

  • Theory and Implementation of

    Natural-Language-Competent

    Computerized Cognitive Agent/Robot

  • and Supporting Research in

    Artificial Intelligence

    Cognitive Science

    Computational Linguistics.

S.C. Shapiro


Research Areas

  • Knowledge Representation and Reasoning

  • Cognitive Robotics

  • Natural-Language Understanding

  • Natural-Language Generation.

S.C. Shapiro


Goal

  • 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


Cassie

  • A computational cognitive agent

    • Embodied in hardware

    • or Software-Simulated

    • Based on SNePS and GLAIR.

S.C. Shapiro


GLAIR Architecture

Grounded Layered Architecture with Integrated Reasoning

Knowledge Level

SNePS

Perceptuo-Motor Level

NL

Sensory-Actuator Level

Vision

Sonar

Proprioception

Motion

S.C. Shapiro


SNePS

  • 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

S.C. Shapiro


BlocksWorld

S.C. Shapiro


FEVAHR

S.C. Shapiro


FEVAHRWorld Simulation

S.C. Shapiro


UXO Remediation

Corner flag

Field

Drop-off zone

UXO

NonUXO object

Battery

meter

Corner flag

Corner flag

Recharging

Station

Cassie

Safe zone

S.C. Shapiro


Crystal Space Environment

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Princess from “The Trial, The Trail”

A VR drama by Josephine Anstey

S.C. Shapiro


Vacuum Cleaner Cassie

Using Byron Weber Becker’s Java Karel

S.C. Shapiro


Magellan ProTM Mobile RobotfromiRobot

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Sample Research Issues:Indexicals

S.C. Shapiro


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)

  • First person pronouns: *YOU

  • Second person pronouns: *I

  • “here”: location of *YOU

  • Present/Past relative to *NOW.

S.C. Shapiro


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

Come here.

S.C. Shapiro


Use of Indexicals 2

Come here.

I came to you, Stu.

I am near you.

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Use of Indexicals 3

Whoam I?

Your name is ‘Stu’

and you are a person.

Whohaveyoutalkedto?

I am talking to you.

TalktoBill.

I am talking to you, Bill.

Comehere.

S.C. Shapiro


Use of Indexicals 4

Comehere.

I found you.

I am looking at you.

S.C. Shapiro


Use of Indexicals 5

Comehere.

I found you.

I am looking at you.

I came to you.

I am near you.

S.C. Shapiro


Use of Indexicals 6

WhoamI?

Your name is ‘Bill’

and you are a person.

Whoareyou?

I am the FEVAHR

and my name is ‘Cassie’.

Whohaveyoutalkedto?

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


Some robots in a suite of rooms.

S.C. Shapiro


  • Are these the same two robots?

  • Why do you think so/not?

S.C. Shapiro


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

  • 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

  • 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

(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)

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)

Animal(any x Mammal(x))

Hairy(any x Mammal(x))

Mammal(any x Dog(x))

Dog(Fido)

Hairy(any x Dog(x))

Hairy(Fido)

Animal(Fido)

S.C. Shapiro


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

  • Shapiro:http://www.cse.buffalo.edu/~shapiro/

  • SNePS Research Group:http://www.cse.buffalo.edu/sneps/

    • Meets Fridays 9-11, 242 Bell Hall

    • Join us!

S.C. Shapiro


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