Knowledge Representation and Organization Chapter 8

Knowledge Representation and Organization

Chapter 8

Outline

1. Organization of Declarative Knowledge1. Concepts and Categories2. Semantic Network Models3. Schematic Representations

2. Representations of Procedural Knowledge3. Integrative Models for Representing

Declarative and Nondeclarative Knowledge1. Combining Representations: ACT-R2. Models Based on the Human Brain3. 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

– 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

– 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. Concepts and Categories• Feature-Based Categories: A Defining View

– 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. 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. 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. 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


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


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


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


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)


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



• 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


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



• 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



• 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

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Knowledge Representation and Organization Chapter 8