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The above described explicitation process results in Knowledge Models and Standard Specifications Models that enable their use for computer supported knowledge-aided design as well as for automated verification of designs.
this is done by m.kalpana
'''model types'''
At its highest-level, Knowledge Models can be categorized into following seven groups:
1 DIAGNOSTIC MODELS
This type of model is used for diagnosing problems by categorizing and framing problems in order to determine the root or possible cause.
Semantic: Complaint » Possible Cause(s)
Example: I have these symptoms. What is the problem?
2 EXPLORATIVE MODELS
This type of model is designed to produce possible options for a specific case. The options may be generated using techniques such as Genetic Algorithms or Monte Carlo simulation, or retrieved from a knowledge and/or case-base system.
Semantic: Problem Description » Possible Alternatives
Example: Ok, I know the problem. What are my options?
3 SELECTIVE MODELS
This type of model is used mainly for the decision-making process in order to assess or select different options. Of course, there would be always at least two alternatives; otherwise there is no need for making any decision.
A Selective Model distinguishes between cardinal and ordinal results. On one hand, when a cardinal model is used, the magnitude of the result’s differences is a meaningful quantity. On the other hand, ordinal models only capture ranking and not the strength of result. Selective Models can be used for rational Choice under Uncertainty or Evaluating and Selecting Alternatives. Such a selection process usually has to consider and deal with “conflicting objectives.”
Semantic: Alternatives » Best Option
Example: Now I know the options. Which one is the best for me?
4 ANALYTIC MODELS
Analytical Models are mainly used for analyzing pre-selected options. This type of model has the ability to assess suitability, risk or any other desire fitness attributes. In many applications, the Analytic Model is a sub-component of the Selective Model.
Semantic: Option » Fitness
Example: I picked my option. How good and suitable is it for my objective?
5 INSTRUCTIVE MODELS
This type of model provides guidance in a bidirectional or interactive process. Among the examples are many support solutions available in the market.
Semantic: Problem Statement » Solution Instruction
Example: How can I achieve that?
6 CONSTRUCTIVE MODELS
A Constructive Model is able to design or construct the solution, rather than instructing it. Some of the recently popularized Constructive Models are used for generating software codes for various purposes, from computer viruses to interactive multimedia on websites like MySpace.com.
Semantic: Problem Statement » Design Solution
Example: I need a <…> with these specifications <...>.
7 HYBRID MODELS
In many cases more advanced models are constructed by nesting or chaining several models together. While not always possible, but – ideally – each model should be designed and implemented as an independent component. This will allow for easier maintenance and future expansion. A sophisticated, full-cycle application may incorporate and utilize all the above models:
Diagnostic Model » Explorative Model » Selective Model » Analytic Model » Constructive Model
'''Technology Options'''
As a best practice approach knowledge models should stay implementation neutral and provide KCM experts with flexibility of picking the appropriate technology for each specific implementation.
In general the technology solutions can be categorized into Case-based systems and knowledge-based systems. Case-based approach focuses on solving new problems by adapting previously successful solutions to similar problems and focuses in gathering knowledge from case histories. To solve a current problem: the problem is matched against similar historical cases and adjusted accordingly to specific attributes of new case. As such they don’t require an explicit knowledge elicitation from experts.
Expert or knowledge-based systems (KBS) on the other hand focuses on direct knowledge elicitation from experts.
There are a variety of methods and technologies that can be utilized in Knowledge Modeling, including some practices with overlapping features. Highlighted below are the most commonly used methods.
1 DECISION TREE & AHP
A Decision Tree is a graph of options and their possible consequences used to create a plan in order to reach a common goal. This approach provides designers with a structured model for capturing and modeling knowledge appropriate to a concrete-type application.
Closely related to a Decision Tree, AHP (Analytic Hierarchy Process) developed by Dr. Thomas Saaty bestows a powerful approach to Knowledge Modeling by incorporating both qualitative and quantitative analysis.
2 BAYESIAN NETWORKS & ANP
Influence-based systems such as Bayesian Network (Belief Network) or ANP (Analytic Network Process) provide an intuitive way to identify and embody the essential elements, such as decisions, uncertainties, and objectives in effort to better understand how each one influence the other.
3 ARTIFICIAL NEURAL NETWORK
An Artificial Neural Network (ANN) is a non-linear mathematical or computational model for information processing. In most cases, ANN is an adaptive system that changes its structure based on external or internal information that flows through the network. It also addresses issues by adapting previously successful solutions to similar problems.
4 GENETIC & EVOLUTIONARY ALGORITHMS
Inspired by biological evolution, including inheritance, mutation, natural selection, and recombination (or crossover), genetic and evolutionary algorithms are used to discover approximate solutions that involve optimization and problem searching in Explorative Models (refer to Model Types).
5 EXPERT SYSTEMS
Expert Systems are the forefathers of capturing and reusing experts’ knowledge, and they typically consist of a set of rules that analyze information about a specific case. Expert Systems also provide an analysis of the problem(s). Depending upon its design, this type of system will produce a result, such as recommending a course of action for the user to implement the necessary corrections.
6 STATISTICAL MODELS
Statistical Models are mathematical models developed through the use of empirical data. Included within this group are 1) simple and/or multiple linear regression, 2) variance-covariance analysis, and 3) mixed model
==References==
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