LabWizard™ based on RippleDown™ Technology
LabWizard is a knowledge acquisition and management product which is based on the unique RippleDown technology which overcomes many of the traditional weaknesses of rules based "Expert Systems". LabWizard is particularly suitable for applications which have the following characteristics:
| Decision Support: |
The application of expertise to a defined situation with the objective of achieving a decision as the outcome. |
| Complexity: |
The decision is based on multiple inputs which may require the application of thousands of rules. |
| High Accuracy: |
It is essential that the decision is the correct one in the vast majority of cases, typically in excess of 99.9%. |
| High Volume: |
The requirement is for frequent decisions which exceeds the practical availability of an expert. |
| Dynamic: |
The subject matter is frequently updated and new knowledge is added through on-going research, changed circumstances or business environment. |
| Dispersed: |
The requirement is for dispersed knowledge either across large organisations, geographic distribution or multiple users without direct access to an expert. |
| Expert Available: |
Access to the required expertise for review of outcomes and to teach the system how to adjust itself when refinements are necessary or new knowledge is acquired. |
Another Expert System?
Many expert systems failed to deliver expected outcomes.
This is because...
- They require a 'knowledge engineer' (specialist programmer) to extract rules from the domain expert.
- For complex domains (eg, agronomy) it is impossible for the expert to explain the combination of factors involved in assessing a situation and determining appropriate action.
- Filling knowledge gaps means figuring out which rules to change and determining the impact on related rules.
LabWizard is different, it...
- Does not try to reach the unachievable goal of organising and assembling all knowledge in a domain.
- Does not require a 'knowledge engineer' to assemble the domain rules.
- Does not require the expert to know how new knowledge is incorporated.
- Does use a refinement structure to automatically locate rules to ensure that they are used in the context in which they were first created.
- Does assess whether new knowledge conflicts with previous knowledge in order to prompt for discriminating factors.
- Does grow over time with new knowledge captured as part of normal work flow.
For more information on knowledge transfer and knowledge management services available to support your business or organisation contact Bruce Howie at C-Qual Agritelligence Pty Limited.
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