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Knowledge Representation and Reasoning

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Title: Knowledge Representation and Reasoning


1
Knowledge Representation and Reasoning
  • Stuart C. Shapiro
  • Professor, CSE
  • Director, SNePS Research Group
  • Member, Center for Cognitive Science
  • Fellow, AAAI
  • Chair, ACM/SIGART, 1991-1995
  • President, KR., Inc., 1998-2000

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
NL
SNePS
Perceptuo-Motor Level
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
Interaction with Cassie
(Current) Set of Beliefs SNePS
English (Statement, Question, Command)
Reasoning Clarification Dialogue Looking in World
GATN Parser
(Updated) Set of Beliefs SNePS
Actions SNeRE
(New Belief) SNePS
Answer SNIP
GATN Generator
Reasoning
English sentence expressing new belief
answering question reporting actions
10
Example Cassies Worlds
11
Cassie, the BlocksWorld Robot
12
FEVAHR Award-Winning Embodied Cassie Project
13
FEVAHRWorld Simulation
14
UXO Remediation Cassie
Corner flag
Field
Drop-off zone
UXO
NonUXO object
Battery meter
Corner flag
Corner flag
Cassie
Recharging Station
Safe zone
15
Crystal Space Environment
16
UB Virtual Site Museum
  • The 9th-Century BC Northwest Palace at
    Nimrud-Iraq is the best preserved and documented
    of all the Assyrian palaces.
  • Its audience halls were originally created as the
    backdrop for differing royal activities.
  • Completely immersive re-creation of this palace
    with animated characters and interactive story
    boards.
  • T. Kesavadas S. Paley

Modeling of King - Animation in Real time VR
17
Sample Research IssuesIntensional Entities
18
Intensional Entities 1
  • Rather than represent objects in the world,
    represent mental entities.
  • Includes Imaginary and Fictional Entities.
  • Multiple mental entities may correspond to one
    world object.
  • Intensional entities may be co-extensional.
  • But must be kept separate.

19
Intensional Entities 2
  • The morning star is the evening star.
  • I understand that the morning star is the
    evening star.
  • The evening star is Venus.
  • I understand that Venus is the evening star.
  • Clark Kent is Superman.
  • I understand that Superman is Clark Kent.

20
Intensional Entities 3
  • Lois Lane saw Clark Kent.
  • I understand that Lois Lane saw Clark Kent.
  • Did Lois Lane see Superman?
  • I don't know.
  • Did Lois Lane see Clark Kent?
  • Yes, Lois Lane saw Clark Kent.

Note Open World Assumption.
21
Intensional Entities 4
  • Superman went to the morning star.
  • I understand that Superman went to Venus.
  • Did Clark Kent go to Venus?
  • Yes, Superman went to Venus.

22
Intensional Entities 5
  • Buck Rogers went to the evening star.
  • I understand that Buck Rogers went to Venus.
  • Who went to Venus?
  • Buck Rogers went to Venus
  • and Superman went to Venus.

23
Intensional Entities 6
The evening star
The morning star
Venus
Go to
Go to
Clark Kent
Superman
Buck Rogers
See
Lois Lane
24
Sample Research IssuesComplex Categories
25
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.

26
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.

27
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.

28
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.

29
PseudoRepresentation of Complex Categories
  • Isa(B30, CompCat(orange, CompCat(juice, seat)))
  • Isa(Pat, CompCat(excellent, teacher))
  • Isa(Lucy, CompCat(former, teacher))

30
Sample Research IssuesPossession
31
Possession 1
  • One mans meat is another mans poison.

32
Possession 2
  • Richard's meat is Henry's poison.
  • I understand that Henry's poison is Richard's
    meat.
  • Edward ate Richard's meat.
  • I understand that Edward ate Richard's meat.
  • Did Edward eat Henry's poison?
  • Yes, Edward ate Henry's poison.

33
Possession 3
  • Did Edward eat Henrys meat?
  • I dont know.
  • Did Edward eat Richard's poison?
  • I dont know.

Moral Possession is a three-place relation.
34
PseudoRepresentation of Possession
  • Has(Richard, meat, B35)
  • Has(Henry, poison, B37)
  • Equiv(B35, B37)

35
Sample Research IssuesPropositions about
Propositions
36
Propositions about Propositions 1
  • Propositions are first-class mental entities.
  • They can be discussed, just like other mental
    entities.
  • And must be represented like other mental
    entities.

37
Propositions about Propositions 2
  • That Bill is sweet is Mary's favorite
    proposition.
  • I understand that Mary's favorite proposition is
    that Bill is sweet.
  • Mike believes Mary's favorite proposition.
  • I understand that Mike believes that Bill is
    sweet.

38
Propositions about Propositions 3
  • That Mary's favorite proposition is that
    Bill is sweet is cute.
  • I understand that that Mary's favorite
    proposition is that Bill is sweet is cute.

39
Representing Propositions
  • Representation of Proposition
  • Not by a Logical Sentence
  • But by a Functional Term
  • Denoting a Proposition.

40
PseudoRepresentation of Propositions about
Propositions
  • Has(Mary, CompCat(favorite, proposition),
    HasProp(Bill, sweet))
  • Believes(Mike, HasProp(Bill, sweet))
  • HasProp(Has(Mary,
  • CompCat(favorite,
    proposition), HasProp(Bill, sweet)),
  • cute)

41
Sample Research IssuesConditional Plans
42
Conditional Plans
  • If a block is on a support then a plan to
    achieve that the support is clear is to pick up
    the block and then put the block on the table.
  • all(x, y)
  • (Block(x), Support(y), On(x, y)
  • gt GoalPlan(Clear(y),
  • Snsequence(Pickup(x),
  • Put(x, Table))))
  • STRIPS-like representation No times

43
Use of Conditional Plan
  • GoalPlan(Clear(B),
  • Snsequence(Pickup(A),
  • Put(A, Table)))
  • Remember (cache) derived propositions.

44
Use of Conditional Plan
  • GoalPlan(Clear(B),
  • Snsequence(Pickup(A),
  • Put(A, Table)))???
  • SNeBR to the rescue!

45
Sample Research IssuesIndexicals
46
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.

47
Analysis of Indexicals(in input)
  • First person pronouns YOU
  • Second person pronouns I
  • here location of YOU
  • Present/Past relative to NOW.

48
Generation of Indexicals
  • I First person pronouns
  • YOU Second person pronouns
  • NOW used to determine tense and aspect.

49
Use of Indexicals 1
Come here.
50
Use of Indexicals 2
Come here.
I came to you, Stu. I am near you.
51
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.
52
Use of Indexicals 4
Come here.
I found you. I am looking at you.
53
Use of Indexicals 5
Come here.
I found you. I am looking at you.
I came to you. I am near you.
54
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.
55
Sample Research IssuesTime
56
Motivating Joke
  • 93000 AM (Door-to-Door Salesman)
    May I interest you in a brush?
  • 93002 AM (Homeowner) Not now.
  • 93003 AM (Salesman) Now?

57
A Personal Sense of Time
  • NOW contains SNePS term representing current
    time.
  • NOW moves when Cassie acts or perceives a change
    of state.

58
The Pacemaker
  • PML process periodically increments variable
    COUNT.
  • COUNT some PML integer.
  • Reset to 0 when NOW moves.
  • Provides bodily feel of passing time.

59
Quantizing Time
  • Cannot conceptualize fine distinctions in time
    intervals.
  • So quantize, e.g. into half orders of magnitude
    (Hobbs, 2000).

60
Movement of Time with Pacemaker
q
t1
t2
KL
PML
hom
COUNT
n
NOW
0
61
The Vagueness of now
  • Im now giving a talk.
  • Im now on sabbatical.
  • Im now living in East Amherst.
  • Im now at UB.
  • Multiple nows at different granularities.

62
NOW-MTF
Maximal Temporal Frame based on NOW
NOW
Semi-lattice of times, all of which contain
NOW, any of which could be meant by
now Finite---only conceptualized times of
conceptualized states
63
Moving NOW with MTF
NOW
64
Current
65
Current Students
  • Bharat Bhushan, M.S. Candidate
  • Preferential Ordering of Beliefs for Default
    Reasoning
  • Debra T. Burhans, Ph.D. Candidate
  • A Question-Answering Interpretation of Resolution
    Refutation
  • Frances L. Johnson, Ph.D. Candidate
  • Belief Revision in a Deductively Open Belief
    Space
  • John F. Santore, Ph.D. Candidate
  • Distinguishing Perceptually Indistinguishable
    Objects

66
For More Information
  • URL http//www.cse.buffalo.edu/shapiro/
  • Group http//www.cse.buffalo.edu/sneps/
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