Wednesday, November 11, 2009

Representing Uncertainty

In one of my earlier blog post Is AI a Possibility I discussed the need of a 3rd state. For past few days I am thinking about scenario where just returning a Boolean value ie True or False is not good enough.

Normally a function evaluates to either "True" or "False" based on whether the attributes of the entities meet the conditions defined in the rule or not. But it may happen at times that the entity does not contain the attributes required by the rule to evaluate it properly. Then in that case we need to have a 3rd (Not Available) and 4th (Not Applicable) state as rule outcome. When a function returns Not Available then the it implies that the entity does not contain the attribute needed for the rule to execute or process the object. On the other hand Not Applicable means the rule does not apply to the type of the entity in context.

So in total we have 4 return values for the function:
- True
- False
- Not Applicable
- Not Available
A Boolean value (outcome) is not the possible solution here. So we need an alternate representation here for the function result. All functions cannot evaluate to True/False. For those rules (functions) that cannot be evaluated we need to find out the what was the state (Not Applicable or Not Available). When we apply multiple rules to the same object in a sequence (workflow) the outcome is a set of conclusions. But the conclusion must include which rule was evaluated and which ones could not be evaluated.

I am still puzzled as how to represent the two more state considering that internally everything is represented as either 1 or 0 and that does not leave room for representing uncertainty.

Until Next Time....