Chapter 5 Knowledge Representation

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Chapter 5 Knowledge Representation

• Xiu-jun GONG (Ph. D)
• School of Computer Science and Technology,
  Tianjin University
• gongxj_at_tju.edu.cn
• http//cs.tju.edu.cn/faculties/gongxj/course/ai/

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Outline

• Knowledge Knowledge representation
• Methodology for KR
• Logic
• Production System
• Semantic Net
• Frame
• Script
• Object-Oriented
• Summary

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What is Knowledge ?
Knowledge
Knowledge
Information
Data
Signal
Knowledge FactsRulesControl Strategy
(sometimes ) Faiths
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Taxonomy of Knowledge

• Facts declarative knowledge
• thief(john), likes(john, wine)
• Rules procedural knowledge
• may_steal(X, Y) if thief(X) and likes(X, Y)
• Control Strategy meta, super knowledge
• reasoning strategy
• note form
• search strategy

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Attributes of Knowledge

• Range Special ?? General
• Intend Expository ?? Instructional
• Certainty Certain ?? Uncertain
• Contain/Conflict ??Contain Conflict(in faith)

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Knowledge Representation

• Knowledge representation is an issue that arises
  in both cognitive science and AI.
• In cognitive science it is concerned with how
  people store and process information.
• In AI, the primary aim is to store knowledge so
  that programs can process it and achieve the
  verisimilitude of human intelligence.
• AI researchers have borrowed representation
  theories from cognitive science.

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Some issues in KR

• How do people represent knowledge?
• What is the nature of knowledge and how do we
  represent it?
• Should a representation scheme deal with a
  particular domain or should it be general
  purpose?
• How expressive is a representation scheme?
• Should the scheme be declarative or procedural?

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Methodology of KR

• Logic
• Production System
• Semantic Net
• Frame
• Script

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Propositional Logic

• Propositional logic uses true statements to form
  or prove other true statements.
• Representation (syntax) How to represent a
  proposition.
• Reasoning (algorithm) How to create or prove new
  propositions.
• Representation of propositional logic
• A propositional symbol and connectives (!, , ,
  gt, ltgt )
• Example
• C Its cold outside C is a proposition
• O Its October O is a proposition
• If O then C if its October then its cold
  outside

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Predicate Logic

• Same connectives as propositional logic
• Propositions have structure Predicate/Function
  arguments.
• R, 2 Terms. Terms are not individuals, not
  propositions
• Red(R), (Red R) A proposition, written in two
  ways
• (southOf UnicornCafe UniHall) a proposition
• ( 2 2) Term, since the function ranges over
  numbers
• Quantifiers enable general axioms to be written
• (forall ?x (iff (Triangle ?x) (and (polygon
  ?x) (numberOfSides ?x
  3)))

Easy to inference
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Logic as a KR language

• advantages
• With a semantics
• Expressiveness
• Disadvantages
• Inefficient
• Undecidability
• Unable to express procedural knowledge
• Unable to do default reasoning
• No abduction

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Production System (1)

• Production rules are one of the most popular and
  widely used knowledge representation languages
• Production rule system consists of three
  components
• working memory contains the information that the
  system has gained about the problem thus far.
• rule base contains information that applies to
  all the problems that the system may be asked to
  solve.
• interpreter solves the control problem, i.e.,
  decide which rule to execute on each
  selection-execute cycle.
• Used both for KR and Problem solving system

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Production System (2)

• Advantages
• Naturalness of expression
• Modularity
• Restricted syntax
• Ability to Represent Uncertain Knowledge
• Disadvantages
• Inefficient
• Less expressive

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Semantic Nets

• Intuition base
• An important feature of human memory is the high
  number of connections or associations between the
  different pieces of information contained in it.
• There are two types of primitive
• Nodes correspond to objects, or classes of
  objects, in the world
• Links are unidirectional connections between
  nodes and correspond to relationships between
  these objects

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Semantic Nets

• Major problem with semantic nets is that although
  the name of this knowledge representation
  language is semantic nets, there is not,
  ironically, clear semantics of the various
  network representations. For the above example,
• it can be interpreted as the representation of a
  specific bird named Tweety, or
• it can be interpreted as a representation of some
  relationship between Tweety, birds and animals.

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Common used links

• IS-A
• PART-OF
• MODIFILES on, down, up, bottom, moveto,
• Link types are set up for specific domain
  knowledge

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Examples of Semantic Net (1)

• Represent a table

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Analysis of Semantic Net

• For a particular Domain, you
• make up a set of link-types
• create a set of nodes
• connect them together
• ascribe meaning
• Write Programs to manipulate the knowledge
• Lisp
• CL

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Examples of Semantic Net (2)

• My car is tan and Johns car is green

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Inference in a Semantic Net (1)

• Inheritance
• the is-a and instance-of representation provide a
  mechanism to implement this.
• Inheritance also provides a means of dealing with
  default reasoning

IS-A
IS-A
IS-A
B
A
C
A
C
can
can
IS-A
clyde
bird
bird
fly
clyde
fly
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Inference in a Semantic Net (2)

• Intersection search
• The notion that spreading activation out of two
  nodes and finding their intersection finds
  relationships among objects.
• Many advantages including entity-based
  organization and fast parallel implementation.
• However very structured questions need highly
  structured networks

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Inference in a Semantic Net (3)
I
I
owner
owner
color
color
car1
what?
car1
tan
is-a
is-a
tan
car
car
is-a
color
car2
green
What color is the car1?
owner
john
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Frame representation

• Frame a knowledge representation technique which
  attempts to organize concepts into a form which
  exploits interrelatioships and common beliefs
• frame-based KR is analogous to object-oriented
  programming the difference is the entities
  encoded
• A frame is similar to a record data structure or
  database record
• Frame has slot names and slot fillers, and
  usually arranged in a hierarchy

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Structure of frame (1)

• Frame name
• slot value , value,
• .
• .
• .
• slot
• facet value, value,
• facet value, value,

• Frame printer
• superset office-machine
• subset laser-printer, ink-jet-printer
• energy-source wall-outlet
• maker Epson
• date 1-April-2003

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Structure of frame (2)

• Frames often allowed slots to contain procedures.
• if-needed procedures, run when value needed
• if-added procedures, run when a value is added
  (to update rest of data, or inform user).

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Class and instance frames

• (frame) instance representing lowest-level
  object a single object or entity
• (frame) class describes different frames (either
  instances or classes)
• every instance has an is-a link, pointing to
  its class
• possibly more than one is-a

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Example of frames (1)
Bird
Frame Name
Class frame
Colour
Unknown
Properties
Wings
2
Flies
True
Tweety
Frame Name
Bird
Class
Instance frame
Colour
Yellow
Properties
Wings
1
Flies
False
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Example of frames (2)
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Capability of frame representation

• Advantages
• Domain knowledge model reflected directly
• Support default reasoning
• Efficient
• Support procedural knowledge
• Disadvantages
• Lack of semantics
• Expressive limitations

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Scripts for KR

• Rather similar to frames uses inheritance and
  slots describes stereotypical knowledge, (i.e.
  if the system isn't told some detail of what's
  going on, it assumes the "default" information is
  true), but concerned with events.
• Somewhat out of the mainstream of expert systems
  work. More a development of natural-language-proce
  ssing research.

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Definition of scripts

• A script is a remembered precedent, consisting of
  tightly coupled, expectation-suggesting
  primitive-action and state-change frames
  Winston, 1992
• A script is a structured representation
  describing a stereotyped sequence of events in a
  particular context Luger, Stubble?eld,1998

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Why scripts? (1)

• Because real-world events do follow stereotyped
  patterns. Human beings use previous experiences
  to understand verbal accounts computers can use
  scripts instead.
• Because people, when relating events, do leave
  large amounts of assumed detail out of their
  accounts. People don't find it easy to converse
  with a system that can't fill in missing
  conversational detail

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Why scripts? (2)

• Scripts predict unobserved events.
• Scripts can build a coherent account from
  disjointed observations.
• Applications
• This sort of knowledge representation has been
  used in intelligent front-ends, for systems whose
  users are not computer specialists.
• It has been employed in story-understanding and
  news-report-understanding systems.

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Components of Scripts

• Script name
• Entry conditions
• Roles
• Props
• Scene 1
• Scene 2
• Results

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Script restaurant example (1)
Scene 1Entering ?????? ???? ????? ????? ?????
Script RESTAURANT Track Coffee Shop Props
Tables Menu Food
Check Money Roles Customer
Waiter Cook Cashier
Owner
(Customer ask for Menu) ??????? ??????? ?????????
??????? ??????? ??????????
Scene 2Ordering (Menu on table) ?????? ??????? ??
?????
??????????
??????
????? ??????? ??????
??Scene 4 ???????
??Scene 3
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Script restaurant example (2)
Entry conditions Customer is hungry
Customer has money Results Customer has
less money Owner has more money Customer is
not hungry Customer is pleased(optional)
Scene 3Eating ?????????? ???????? ??????
??Scene 2
Scene 4Leaving ?????? ????????? ?????????? ??????
?? ??????????
???????
??????
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Summary KR as

• Logic (Declarative)
• Propositional
• Predicate
• Procedural
• Rules
• Productions systems
• Structure
• Frames
• Scripts
• Associations
• Semantic net

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