Facts and Certainty Factors (CF) – Overall Certainty
Any results that Rainbird produces after completing a query will have a “Certainty Factor” . The Certainty Factor represents how sure Rainbird is of the result it has returned. The Certainty Factor can vary depending on the data inputted by the user, the rules of a knowledge map, and whether Rainbird is working with incomplete data.
Data input into Rainbird is also associated with a certainty factor. End-users can answer questions and provide a certainty level ,which reflects how sure they are of their answer, during a query. The knowledge map’s facts will also have a certainty factor associated with them, and whether Rainbird is more or less certain about a fact will impact the certainty factor of the results Rainbird produces.
This article describes how the “Certainty Factor” of an outcome is calculated, and how it can be used and interpreted by the user. More information about how the certainty factor can be affected by end-users and facts can be found in the allowCF article and the fact / concept instance articles.
Build String Concepts
Isaac wants fruit for his dessert after dinner. He is very picky about fruits, and only has:
- Green Apples
- Yellow Apples
- Green Pears
He keeps his fruits in his garage, where there’s no light as the light bulb is broken. He can barely see the colours, and can’t differentiate the shape of the fruit by sight.
When picking a fruit, he’ll try to guess what kind of fruit to pick by looking at it, guessing the colour and touching it to guess the type of fruit.
The following Rainbird model estimates what fruit Isaac has picked and returns a certainty factor of how sure Rainbird is, using Certainty Factor sliders and rules with different certainty factors. Here is what the model looks like:
Figure 1: Fruit selector model
Here is how it behaves:
Figure 2: Fruit selector model behaviour
In the example above, Rainbird is 81% certain that Isaac picked a green apple.
The Certainty Factor can also be used to calculate the probability of an outcome. Let’s assume that Isaac knows roughly how many apples and pears he has, and what proportions of the fruit are what colour. Isaac now wants to estimate the probability of picking a certain fruit:
Isaac has the following amount of fruit in his garage:
- Green Apples: 50
- Yellow Apples: 25
- Green Pears: 5
- Yellow Pears: 20
Isaac has a total of 75 apples and 25 pears. He knows that he has a 75% chance to pick an apple, and if he does, there will be a 66.66% chance of the apple being green. The overall chance of picking a green apple therefore is 50%.
Click on the ‘Export.rbird’ button to download the ‘Overall Certainty’ map used in this example. The knowledge map can then be imported into your Rainbird Studio.
Query and Results
The relationships ‘pick a fruit’, ‘has probability to pick (colour) fruit’, and ‘has probability to pick fruit’, are the three relationships which should be queried when using the model:
- The outcome of running the query on the relationship ‘pick a fruit’, will show the confidence factor of picking the right fruit , dependent on the confidence level the end-user sets.
- The outcome of running the query on the relationship ‘has probability to pick green knowing fruit’ (or yellow) will show the probability to pick the right fruit.
- To run the combined rules for both green and yellow fruits query the relationship “has probability to pick fruit”.
Version 1.02 – Last Update: 26/03/2021