ARTIFICIAL INTELLIGENCE
[INTELLIGENT AGENTS PARADIGM]
KNOWLEDGE TYPES
AND REPRESENTATION
Professor Janis Grundspenkis
Riga Technical University
Faculty of Computer Science and Information Technology
Institute of Applied Computer Systems
Department of Systems Theory and Design
E-mail: Janis.Grundspenkis@rtu.lv
What Is Knowledge?
• Knowledge is an abstract term that attempts
to capture an individual’s understanding
of a given subject.
• In the world of intelligent systems the
domain-specific knowledge is captured.
• Domain is a well-focused subject area.
Types of Knowledge
Declarative Concepts Describes what is known
knowledge Facts
about a problem. This includes
Objects simple statements that are
asserted to be either true or
false. This also includes a list
of statements that more fully
describes some object or
concept (object-attribute-value
triplet).
Types of Knowledge
Procedural Rules
knowledge Strategies
Agendas
Procedures
Describes how a
problem is solved.
This type of
knowledge provides
direction on how to
do something.
Types of Knowledge
Heuristic Rules of Describes a rule-of-thumb
that guides the reasoning
Thumb
Knowledge
process. Heuristic
knowledge is often called
shallow knowledge. It is
empirical and represents
the knowledge compiled by
an expert through the
experience of solving past
problems.
Types of Knowledge
Knowledge
MetaKnowledge about the
other types
of
knowledge
and how to
use them
Describes knowledge about
knowledge. This type of
knowledge is used to pick
other knowledge that is best
suited for solving a problem.
Experts use this type of
knowledge to enhance the
efficiency of problem solving
by directing their reasoning in
the most promising area.
Types of Knowledge
Structural Rule sets
Knowledge Concept
relationships
Concept to
object
relationships
Describes knowledge
structures. This type of
knowledge describes an
expert’s overall mental
model of the problem. The
expert’s mental model of
concepts, sub-concepts,
and objects is typical of
this type of knowledge.
Knowledge Based Systems
• The central component of a knowledge-based
system is its knowledge base
• Informally, a knowledge base is a set of
representations of facts about the world
• Each individual representation is called a sentence
• The sentences are expressed in a language called a
knowledge representation language
Knowledge Representation
• In general, a representation is a set of
conventions about how to describe a class of
things.
• A description makes use of the conventions of a
representation to describe some particular thing.
• The function of any representation scheme is to
capture essential features of a problem domain
and make that information available to a problem
solving procedure.
Knowledge Representation
• Knowledge representation is the method
used to encode knowledge in an intelligent
system’s knowledge base.
• The object of knowledge representation is
to express knowledge in computer-tractable
form, such that it can be used to help
intelligent system perform well.
Knowledge Representation
It is obvious that a representation language
must allow the programmer to express the
knowledge needed for a problem solution.
Knowledge representation languages should
provide a natural framework for expressing
problem-solving knowledge. Such
framework should make that knowledge
available to the computer and assist the
programmer in its organization.
Knowledge Representation
A knowledge representation language is
defined by two aspects:
• The syntax of a language describes the
possible configurations that can constitute
sentences.
• The semantics determines the facts in the
world which the sentences refer.
Knowledge Representation
Without semantics, a sentence is,
for instance, just a collection of
marks on a page.
With semantics, each sentence
makes a claim about the world.
Knowledge Representation
For example, the syntax of the language of
arithmetic expressions says that if x and y
are expressions denoting numbers, then x <
y is a sentence about numbers.
The semantics of the language says that x < y
is false when x is an equal or a bigger
number than y, and true otherwise.
Knowledge Representation
• Cognitive psychologists have formed a number of
theories to explain how humans solve problems
This work uncovered the types of knowledge
humans commonly use, how they mentally
organize this knowledge, and how they use it
efficiently to solve a problem.
• Researchers in artificial intelligence have used the
results of these studies to develop techniques to
best represent different knowledge types in the
computer.
• Intelligent systems during problem solving must
process knowledge.
Knowledge Representation
• To process knowledge, the knowledge is
represented in some symbolic form that can be
manipulated by an intelligent system.
• Just as there is no single theory to explain human
knowledge organization or a best technique for
structuring data, no single knowledge
representation structure is ideal.
• One of more important responsibilities of
knowledge engineer is to choose the knowledge
representation technique best suited for the
given application.
Knowledge Representation
A representation consists of four
fundamental parts:
• A lexical part that determines which
symbols are allowed in the representation’s
vocabulary.
• A structural part that describes constraints
on how the symbols can be arranged.
Knowledge Representation
• A procedural part that specifies access
procedures that enable to create
descriptions, to modify them, and to answer
questions using them.
• A semantic part that establishes a way of
associating meaning with the description.
The Quality of Representation
Good representations are the key to good
problem solving:
• Good representations make the important
objects and relations explicit: it is possible
to see what is going on at a glance.
• They expose natural constraints: it is
possible to express the way one object or
relation influences another.
The Quality of Representation
• They bring objects and relations together:
it is possible to see all needed at one time.
• They suppress irrelevant details: it is
possible to keep rarely used details out of
sight, but still get to them when necessary.
The Quality of Representation
• They are transparent: it is possible to
understand what is being said.
• They are complete: it is possible to say all
that needs to be said.
• They are concise: it is possible to say what
is needed to say efficiently.
The Quality of Representation
• They are fast: it is possible to store and
retrieve information rapidly.
• They are computable: it is possible to
create them with an existing procedure.