Please enable JavaScript.
Coggle requires JavaScript to display documents.
Knowledge Representations (Requirements for KR Languages (Representation…
Knowledge Representations
Medium for Human Expression
An intelligent system must have KRs that
can be interpreted by humans
Incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build
We need to be able to encode
information in the knowledge base
We need to be able to understand what the system knows and how it draws its conclusions
What is representation? What do we need to represent?
We need to represent a thing in the natural world when we don’t have, for some reason, the possibility to use the original ’thing
The objective of knowledge representation is to express the problem in computer-understandable form
Representation refers to a symbol or thing which represents (’refers to’, ’stands for’) something else.
Well defined syntax/semantics
Syntactic
Possible (allowed) constructions
Each individual representation is often called a sentence
e.g. color(my_car,red), my_car(red), red(my_car) etc.
Semantic
What does the representation mean (maps the sentences to the world)
e.g. color(my_car, red) -> 'my car is red', 'paint my car red' etc.
Inferential = The interpreter
Decides what kind of conclusions can be drawn
For example: Modus ponens (P, P->Q, therefore Q)
Requirements for KR Languages
Representation adequacy - should allow for representing all the required knowledge
Inferential adequacy - should allow inferring new knowledge
Inferential efficiency - inferences should be efficient
Clear syntax and semantics - unambiguous and well defined syntax and semantics/
Naturalness - easy to read and use
Types of knowledge
Declarative knowledge
A representation of facts and assertions, knowing that something is the case - e.g. Paris is the capital of France
Procedural knowledge
Information about courses of action
Involves knowing how to do something - e.g. Riding a bike
Posteriori knowledge
A knowledge that is known by human experience, acquire after experience
Priori knowledge
A knowledge that is acquired before real experience - e.g. Single vs Married, Talking about children (special needs), tolerance levels
Tacit knowledge
A knowledge not easily expressed by language, we can know more than we can tell
Knowledge Representation Methods
Production Rules
Semantic Nets
Frames
Logic
