Knowledge Representation and Reasoning

1
Knowledge Representation and Reasoning

• Stuart C. Shapiro
• Professor, CSE
• Director, SNePS Research Group
• Member, Center for Cognitive Science

2
Introduction
3
Long-Term Goal

• Theory and Implementation of
• Natural-Language-Competent
• Computerized Cognitive Agent
• and Supporting Research in
• Artificial Intelligence
• Cognitive Science
• Computational Linguistics.

4
Research Areas

• Knowledge Representation and Reasoning
• Cognitive Robotics
• Natural-Language Understanding
• Natural-Language Generation.

5
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
• Remember and report what it has sensed and done.

6
Cassie

• A computational cognitive agent
• Embodied in hardware
• or Software-Simulated
• Based on SNePS and GLAIR.

7
GLAIR Architecture
Grounded Layered Architecture with Integrated
Reasoning
Knowledge Level
SNePS
Perceptuo-Motor Level
NL
Sensory-Actuator Level
Vision
Sonar
Motion
Proprioception
8
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.

9
Example Cassies Worlds
10
BlocksWorld
11
FEVAHR
12
FEVAHRWorld Simulation
13
UXO Remediation
Corner flag
Field
Drop-off zone
UXO
NonUXO object
Battery meter
Corner flag
Corner flag
Cassie
Recharging Station
Safe zone
14
Crystal Space Environment
15
Sample Research IssuesComplex Categories
16
Complex Categories 1

• Noun Phrases
• ltDetgt N Adj N
• Understanding of the modification must be left to
  reasoning.
• Example
• orange juice seat
• Representation must be left vague.

17
Complex Categories 2

• Kevin went to the orange juice seat.
• I understand that Kevin went to the orange juice
  seat.
• Did Kevin go to a seat?
• Yes, Kevin went to the orange juice seat.

18
Complex Categories 3

• Pat is an excellent teacher.
• I understand that Pat is an excellent teacher.
• Is Pat a teacher?
• Yes, Pat is a teacher.
• Lucy is a former teacher.
• I understand that Lucy is a former teacher.

19
Complex Categories 4

• former' is a negative adjective.
• I understand that former' is a negative
  adjective.
• Is Lucy a teacher?
• No, Lucy is not a teacher.

Also note representation and use of knowledge
about words.
20
Sample Research IssuesIndexicals
21
Representation and Use of Indexicals

• Words whose meanings are determined by occasion
  of use
• E.g. I, you, now, then, here, there
• Deictic Center ltI, YOU, NOWgt
• I SNePS term representing Cassie
• YOU person Cassie is talking with
• NOW current time.

22
Analysis of Indexicals(in input)

• First person pronouns YOU
• Second person pronouns I
• here location of YOU
• Present/Past relative to NOW.

23
Generation of Indexicals

• I First person pronouns
• YOU Second person pronouns
• NOW used to determine tense and aspect.

24
Use of Indexicals 1
Come here.
25
Use of Indexicals 2
Come here.
I came to you, Stu. I am near you.
26
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.
27
Use of Indexicals 4
Come here.
I found you. I am looking at you.
28
Use of Indexicals 5
Come here.
I found you. I am looking at you.
I came to you. I am near you.
29
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.
30
Current Research Issues Distinguishing
Perceptually Indistinguishable ObjectsPh.D.
Dissertation, John F. Santore
31

• Some robots in a suite of rooms.

32

• Are these the same two robots?
• Why do you think so/not?

33
Next Steps

• How do people do this?
• Currently doing protocol experiments
• Getting Cassie to do it.

34
Current Research Issues Belief Revisionin
aDeductively Open Belief SpacePh.D.
Dissertation, Frances L. Johnson
35
Belief Revision in a Deductively Open Belief
Space

• Beliefs in a knowledge base must be able to be
  changed (belief revision)
• Add remove beliefs
• Detect and correct errors/conflicts/inconsistencie
  s
• BUT
• Guaranteeing consistency is an ideal concept
• Real world systems are not ideal

36
Belief Revision in a DOBS Ideal Theories vs.
Real World

• Ideal Belief Revision theories assume
• No reasoning limits (time or storage)
• All derivable beliefs are acquirable (deductive
  closure)
• All belief credibilities are known and fixed
• Real world
• Reasoning takes time, storage space is finite
• Some implicit beliefs might be currently
  inaccessible
• Source/belief credibilities can change

37
Belief Revision in a DOBS A Real World KR System

• Must recognize its limitations
• Some knowledge remains implicit
• Inconsistencies might be missed
• A source turns out to be unreliable
• Revision choices might be poor in hindsight
• After further deduction or knowledge acquisition
• Must repair itself
• Catch and correct poor revision choices

38
Belief Revision in a DOBS Theory Example
Reconsideration
Ranking 1 is more credible that Ranking 2.
Ranking 1 is more credible that Ranking 2.
College A is better than College B. (Source
Ranking 1)
College B is better than College A. (Source
Ranking 2)
College B is better than College A. (Source
Ranking 2)
Ranking 1 was flawed, so Ranking 2 is more
credible than Ranking 1.
Need to reconsider!
39
Next Steps

• Implement reconsideration
• Develop benchmarks for implemented krr systems.

40
Current Research Issues Default
ReasoningbyPreferential Ordering of
BeliefsM.S. Thesis, Bharat Bhushan
41
Small Knowledge Base

• Birds have wings.
• Birds fly.
• Penguins are birds.
• Penguins dont fly.

42
KB Using Default Logic

• ?x(Bird(x) ? Has(x, wings))

• ?x(Penguin(x) ? Bird(x))

• ?x(Penguin(x) ? ?Flies(x))

43
KB Using Preferential Ordering

• ?x(Bird(x) ? Has(x, wings))

• ?x(Bird(x) ? Flies(x))

• ?x(Penguin(x) ? Bird(x))

• ?x(Penguin(x) ? ?Flies(x))

• Precludes(?x(Penguin(x) ? ?Flies(x)),
• ?x(Bird(x) ? Flies(x)))

44
Next Steps

• Finish theory and implementation.

45
Current Research Issues Representation
Reasoningwith Arbitrary ObjectsStuart C. Shapiro
46
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?

47
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))

48
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)
49
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)))

50
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,

51
For More Information

• Shapiro http//www.cse.buffalo.edu/shapiro/
• SNePS Research Group http//www.cse.buffalo.edu/s
  neps/