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. Introduction. Long-Term Goal. Theory and Implementation of Natural-Language-Competent Computerized Cognitive Agent and Supporting Research in - PowerPoint PPT PresentationTRANSCRIPT
S.C. Shapiro
cse@buff
alo
Knowledge Representation and Reasoning
Stuart C. ShapiroProfessor, CSE
Director, SNePS Research Group
Member, Center for Cognitive Science
S.C. Shapiro
cse@buff
alo
Introduction
S.C. Shapiro
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Long-Term Goal
• Theory and Implementation of
Natural-Language-Competent
Computerized Cognitive Agent
• and Supporting Research in
Artificial Intelligence
Cognitive Science
Computational Linguistics.
S.C. Shapiro
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alo
Research Areas
• Knowledge Representation and Reasoning
• Cognitive Robotics
• Natural-Language Understanding
• Natural-Language Generation.
S.C. Shapiro
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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.
S.C. Shapiro
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Cassie
• A computational cognitive agent– Embodied in hardware– or Software-Simulated– Based on SNePS and GLAIR.
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GLAIR Architecture
Knowledge Level
Perceptuo-Motor Level
Sensory-Actuator Level NL
Vision
Sonar
MotionProprioception
Grounded Layered Architecture with Integrated Reasoning
SNePS
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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.
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Example Cassies& Worlds
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BlocksWorld
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FEVAHR
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FEVAHRWorld Simulation
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UXO Remediation
CassieCorner flag
NonUXO object
Corner flag
UXO
Batterymeter
Corner flag
Drop-off zone
Field
Safe zone
RechargingStation
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Crystal Space Environment
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Sample Research Issues:Complex Categories
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Complex Categories 1
• Noun Phrases:
<Det> {N | Adj}* N
Understanding of the modification must be left to reasoning.
Example:
orange juice seat
Representation must be left vague.
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: 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.
Complex Categories 2
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: 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.
Complex Categories 3
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: `former' is a negative adjective.
I understand that `former' is a negative adjective.
: Is Lucy a teacher?
No, Lucy is not a teacher.
Complex Categories 4
Also note representation and use of knowledge about words.
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Sample Research Issues:Indexicals
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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.
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Analysis of Indexicals(in input)
• First person pronouns: *YOU• Second person pronouns: *I• “here”: location of *YOU• Present/Past relative to *NOW.
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Generation of Indexicals
• *I: First person pronouns
• *YOU: Second person pronouns
• *NOW: used to determine tense and aspect.
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Come here.
Use of Indexicals 1
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Come here.I came to you, Stu.I am near you.
Use of Indexicals 2
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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.
Use of Indexicals 3
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Come here.
I found you.I am looking at you.
Use of Indexicals 4
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Come here.
I came to you.I am near you.
I found you.I am looking at you.
Use of Indexicals 5
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Who am I?
I talked to Stuand I am talking to you.
Your name is ‘Bill’and you are a person.
Who are you?I am the FEVAHRand my name is ‘Cassie’.
Who have you talked to?
Use of Indexicals 6
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Current Research Issues: Distinguishing Perceptually
Indistinguishable ObjectsPh.D. Dissertation, John F. Santore
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Some robots in a suite of rooms.
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• Are these the same two robots?• Why do you think so/not?
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Next Steps
• How do people do this?– Currently doing protocol experiments
• Getting Cassie to do it.
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Current Research Issues: Belief Revision
in aDeductively Open Belief SpacePh.D. Dissertation, Frances L. Johnson
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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/inconsistencies
• BUT …
– Guaranteeing consistency is an ideal concept
– Real world systems are not ideal
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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
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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
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Belief Revision in a DOBS Theory Example – Reconsideration
College A is better than College B. (Source: Ranking 1)
College B is better than College A. (Source: Ranking 2)
Ranking 1 is more credible that Ranking 2.
Ranking 1 was flawed, soRanking 2 is more credible than Ranking 1.Need to reconsider!
Ranking 1 is more credible that Ranking 2.
College B is better than College A. (Source: Ranking 2)
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Next Steps
• Implement reconsideration
• Develop benchmarks for implemented krr systems.
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Current Research Issues: Default Reasoning
byPreferential Ordering of Beliefs
M.S. Thesis, Bharat Bhushan
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Small Knowledge Base
• Birds have wings.
• Birds fly.
• Penguins are birds.
• Penguins don’t fly.
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KB Using Default Logic
x(Bird(x) Has(x, wings))
x(Penguin(x) Bird(x))
x(Penguin(x) Flies(x))
• Bird(x): Flies(x)
Flies(x)
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KB Using Preferential Ordering
x(Bird(x) Has(x, wings))
x(Penguin(x) Bird(x))
x(Penguin(x) Flies(x))
x(Bird(x) Flies(x))
• Precludes(x(Penguin(x) Flies(x)),
x(Bird(x) Flies(x)))
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Next Steps
• Finish theory and implementation.
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Current Research Issues: Representation & Reasoning
with Arbitrary ObjectsStuart C. Shapiro
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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?
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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))
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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)
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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)))
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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, …
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For More Information
• Shapiro: http://www.cse.buffalo.edu/~shapiro/
• SNePS Research Group: http://www.cse.buffalo.edu/sneps/