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
•
•
•
•
S.C. Shapiro
Knowledge Representation and Reasoning
Cognitive Robotics
Natural-Language Understanding
Natural-Language Generation.
Goal
• A computational cognitive agent that can:
–
–
–
–
–
–
–
–
S.C. Shapiro
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.
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
S.C. Shapiro
Motion
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
UXO
Drop-off zone
NonUXO object
Battery
meter
Corner flag
Recharging
Station
S.C. Shapiro
Corner flag
Cassie
Safe zone
Crystal Space Environment
S.C. Shapiro
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 Robot
from
iRobot
S.C. Shapiro
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)
•
•
•
•
S.C. Shapiro
First person pronouns: *YOU
Second person pronouns: *I
“here”: location of *YOU
Present/Past relative to *NOW.
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.
S.C. Shapiro
Use of Indexicals 3
Who am I?
Your name is ‘Stu’
and you are a person.
Who have you talked to?
I am talking to you.
Talk to Bill.
I am talking to you, Bill.
Come here.
S.C. Shapiro
Use of Indexicals 4
Come here.
I found you.
I am looking at you.
S.C. Shapiro
Use of Indexicals 5
Come here.
I found you.
I am looking at you.
I came to you.
I am near you.
S.C. Shapiro
Use of Indexicals 6
Who am I?
Your name is ‘Bill’
and you are a person.
Who are you?
I am the FEVAHR
and my name is ‘Cassie’.
Who have you talked to?
I talked to Stu
and I am talking to you.
S.C. Shapiro
Current Research Issues:
Distinguishing Perceptually
Indistinguishable Objects
Ph.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 & Reasoning
with Arbitrary Objects
Stuart C. Shapiro
in 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