Home | | Artificial Intelligence | | Computational Intelligence | | Artificial Intelligence | Framework of Knowledge Representation (Poole 1998)

Chapter: Artificial Intelligence(AI) - Knowledge Inference

Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail

Framework of Knowledge Representation (Poole 1998)

Computer requires a well-defined problem description to process and provide well-defined acceptable solution.

Framework of Knowledge Representation (Poole 1998)

.

 

Computer requires a well-defined problem description to process and provide well-defined acceptable solution.

 

To collect fragments of knowledge we need first to formulate a description in our spoken language and then represent it in formal language so that computer can understand. The computer can then use an algorithm to compute an answer. This process is illustrated below.


 

The steps are

 

The informal formalism of the problem takes place first.

 

It is then represented formally and the computer produces an output.

 

This output can then be represented in a informally described solution that user understands or checks for consistency.

 

Note : The Problem solving requires

 

formal knowledge representation,  and conversion of informal knowledge to formal knowledge , that is conversion of implicit knowledge to explicit knowledge.

 

Knowledge and Representation

.                                              

Problem     solving       requires     large  amount     of  knowledge  and      some mechanism for manipulating that knowledge.                  

The Knowledge  and  the Representation  are         distinct  entities,  play a central      but distinguishable roles in intelligent system.     

 

Knowledge is a description of the world;

 

it determines a system's competence by what it knows. − Representation is the way knowledge is encoded;

 

it defines the system's performance in doing something.

 

In simple words, we :

 

need to know about things we want to represent , and

 

need some means by which things we can manipulate.

 

◊ know things to represent

‡ Objects   - facts about objects in the domain.

‡ Events    - actions that occur in the domain.

‡ Performance   - knowledge about how to do things

‡ Meta-      - knowledge about what we know          knowledge

 

◊ need means

‡ Requires - to what we represent ; to manipulate   some formalism

 

Thus, knowledge representation can be considered at two levels :

 

knowledge level at which facts are described, and

 

symbol level at which the representations of the objects, defined in terms of symbols, can be manipulated in the programs.

 

Note : A good representation enables fast and accurate access to knowledge and understanding of the content.

 

Mapping between Facts and Representation

 

 

Knowledge is a collection of facts” from some domain.

 

We need a representation of "facts" that can be manipulated by a program. Normal English is insufficient, too hard currently for a computer program to draw inferences in natural languages.

 

Thus some symbolic representation is necessary.

 

Therefore, we must be able to map "facts to symbols" and "symbols to facts" using forward and backward representation mapping.

 

Example : Consider an English sentence




 

Forward and Backward Representation

 

 

The forward and backward representations are elaborated below :


 

The doted line on top indicates the abstract reasoning process that a program is intended to model.

 

The solid lines on bottom indicates the concrete reasoning process that the program performs.

 

 

KR System Requirements

 

 

A good knowledge representation enables fast and accurate access to knowledge and understanding of the content.

 

A knowledge representation system should have following properties.

Representational Adequacy:

The ability to represent all kinds of knowledge that are needed in that domain.

Inferential Adequacy:

The ability to manipulate the representational structures to derive new structure corresponding to new knowledge inferred from old .

◊ Inferential Efficiency :

The ability to incorporate additional information   into the knowledge structure that can be used to focus the attention of the inference mechanisms in the most promising direction.

◊ Acquisitional Efficiency: 

The  ability  to  acquire  new  knowledge  using automatic methods wherever possible rather than reliance on human intervention.

Note : To date no single system can optimizes all of the above properties.


Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail


Copyright © 2018-2020 BrainKart.com; All Rights Reserved. Developed by Therithal info, Chennai.