Knowledge Representation
and Organization
Chapter 8
Outline
1. Organization of Declarative Knowledge
1. Concepts and Categories
2. Semantic Network Models
3. Schematic Representations
2. Representations of Procedural Knowledge
3. Integrative Models for Representing
Declarative and Nondeclarative Knowledge
1. Combining Representations: ACT-R
2. Models Based on the Human Brain
3. Parallel Processing: The Connectionist Model
?
As quickly and as legibly as possible, write your
normal signature, from the first letter of your first
name to the last letter of your last name.
Now as quickly and as legibly as possible, write
your signature backward.
Which signature was more easily and accurately
created?
Procedural Knowledge versus
Declarative Knowledge
• Declarative Knowledge
– The what of knowledge representations
• Procedural Knowledge
– Involves some degree of skill, which increases as a
result of practice
– The how (the process) of knowledge representation
• Previous slide
– For both signatures, you had available extensive
declarative knowledge of which letters precede or
followed one another, but for the first task, you also
could call on procedural knowledge, based on years
of knowing how to sign your name
1. Organization of Declarative
Knowledge
•
1. Concepts and Categories
Concept
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–
•
Fundamental unit of symbolic knowledge
An idea about something that provides a means for
understanding the world
Often a single concept may be captured in a single
word
Category
–
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One way to organize concepts
Functions to organize or point out aspects of
equivalence among other concepts based on
common features or similarity to a prototype
1. Organization of Declarative
Knowledge
•
1. Concepts and Categories
Feature-Based Categories: A Defining View
–
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Classic view of conceptual knowledge involves
disassembling a concept into a set of featural
components, which are singly necessary and jointly
sufficient to define the category
Defining features constitute the definition of a
category, according to the feature-based,
componential point of view
•
•
–
a defining feature is a necessary one: For a think to be an
“X”, it must have that feature
E.g. Bachelor has defining features: male, unmarried, adult
This theory can not capture typicality within
categories (e.g. robin versus penguin)
1. Organization of Declarative
Knowledge
•
1. Concepts and Categories
Prototype Theory: A Characteristic View
–
•
Suggests that categories are formed on the basis of
characteristic features, which describe the typical
model of the category
A prototype
–
–
The original item on which subsequent models are
based
Model that best represents the class on which the
category is based
1. Organization of Declarative
Knowledge
•
1. Concepts and Categories
Prototype Theory: A Characteristic View
– Characteristic features
•
•
•
Whereas a defining feature is possessed by
every instance of a category, a characteristic
feature need not be
Many or most instances possess each
characteristic features
e.g. the ability to fly is typical of birds, but it is not
a defining feature of a bird (ostrich can not fly)
1. Organization of Declarative
Knowledge
•
1. Concepts and categories
Prototype Theory: A Characteristic View
–
Two kinds of categories:
Classical concepts
•
•
Categories that can be readily defined through defining
features, such as bachelor
Inventions that experts have devised for labeling a class
Fuzzy concepts
•
•
Categories that cannot be so easily defined, largely
because the borders of what constitutes them are fuzzy
Tend to evolve naturally (e.g. fruit)
?
Divide the following items into the category
of fruits and the category of vegetables:
apples, carrots, tomatoes, cauliflower,
oranges, pumpkin, cucumber
1. Organization of Declarative
Knowledge
•
2. Semantic Network Models
Knowledge is represented in terms of a
hierarchical semantic network
– A web of interconnected elements consisting
of
•
•
Nodes - The elements representing concepts
Labeled relationships which might involve
category membership, attributes, or some other
semantic relationship
1. Organization of Declarative
Knowledge
•
2. Semantic Network Models
Concepts appear to have a basic level of
specificity within a hierarchy that is preferred to
other levels (Eleanor Rosh, 1978)
–
–
–
The basic level is neither the most abstract nor the
most specific (e.g. apple)
The basic level can be manipulated by context and
expertise
Basic level is the one that has the largest number of
distinctive features, which set it off from other
concepts at the same level
1. Organization of Declarative
Knowledge
•
3. Schematic Representations
Schemas
– A mental framework organizing knowledge,
creating a meaningful structure of related
concepts
– Explain who concepts are related in the
mind
– E.g. kitchen
1. Organization of Declarative
Knowledge
•
3. Schematic Representations
Scripts
–
–
A structure that describes appropriate sequences of
events in a particular context
Scripts include default values for the actors, the
props, the setting, and the sequence of events
•
E.g. the restaurant script
–
–
–
–
–
Props – tables, a menu, a food
Roles to be played – a customer, a waiter, a cook
Opening conditions for the script – the customer is hungry and
has money
Scenes – entering, ordering, eating, exhibiting
Results – customer has less money, the owner has more
money
2. Representations of Procedural
Knowledge
• Procedural knowledge can be organized in
the form of sets of rules governing a
production (generation and output of a
procedure)
• These rules are organized into
– Routines
• Instructions regarding procedures for implementing
a task
– Subroutines
• Instructions for implementing a subtask within a
larger task governed by a routine
2. Representations of Procedural
Knowledge
• Production system
– comprises the entire set of rules for executing
the task or using the skill
– E.g. a production system for a pedestrian to
cross the street at an intersection with a traffic
light
• Traffic-light red  stop
• Traffic-light green  move
• Move and left foot on pavement  step with right
foot
• Move and right foot on pavement  step with left
foot
3. Integrative Models for
Representing Declarative and
Nondeclarative Knowledge
• Combining Representations: ACT-R (Anderson)
– Adaptive control of thought model of knowledge
representation and information processing
– A model of information processing that integrates a
network representation for declarative knowledge and
a production-system representation for procedural
knowledge
– Declarative network includes storing and retrieving of
information; Procedural system implements the
processes of proceduralization (automatization)
3. Integrative Models for
Representing Declarative and
Nondeclarative Knowledge
• Models based on human brain
– Nondeclarative knowledge may encompass a
broader range of mental representation than
just procedural knowledge
•
•
•
•
Perceptual, motor, and cognitive skills
Simple associative knowledge (conditioning)
Simple nonassociative knowledge (habituation)
priming
3. Integrative Models for
Representing Declarative and
Nondeclarative Knowledge
• Models based on human brain
– Amnesic patients:
• Patient who is given repeated practice in reading
mirror writing will improve as a result of practice
but will not recall ever having engaged in practice
• As your explicit access to nondeclarative
knowledge decreases, your speed and ease of
gaining implicit access to that knowledge increases
3. Integrative Models for
Representing Declarative and
Nondeclarative Knowledge
• Parallel Processing: The connectionist
model
– Parallel processing – multiple operations go
on all at once
– Parallel distributed processing (PDP) models
or connectionist models
• We may be able to process information as
efficiently as we do because we can handle very
large numbers of cognitive operations at once
through a distributed network
3. Integrative Models for
Representing Declarative and
Nondeclarative Knowledge
• Parallel Processing: The connectionist
model
– Networks consist of nodes and connections
between them
– The more often a particular connection is
activated, the greater is the strength of the
connection
– The basic aspect of these models is learning
3. Integrative Models for
Representing Declarative and
Nondeclarative Knowledge
• Domain general versus Domain specific
systems
– Domain general
• The same mental processes underlie various
cognitive tasks
– Domain specific
• There are specific mental processes underlying
various cognitive tasks