Unfortunately the world is an uncertain place.
Any AI system that seeks to model and reasoning in such a world must be able to deal with this.
In particular it must be able to deal with:
• Incompleteness — compensate for lack of knowledge.
• Inconsistencies — resolve ambiguities and contradictions.
• Change — it must be able to update its world knowledge base over time.
Clearly in order to deal with this some decision that a made are more likely to be true (or false) than others and we must introduce methods that can cope with this uncertainty.
There are three basic methods that can do this:
• Symbolic methods.
• Statistical methods.
• Fuzzy logic methods.
We will look at symbolic methods in this lecture and look the others in the next lecture.