Ken Pembleton
CSC416: Artificial Intelligence Programming
10/6/16

1. "Some facts are hard to represent. Or to be more precise, some facts are hard to represent in a way that allows those facts to be reasoned with."
2. "For Example, a simple fact like 'John believes no-one likes brussel sprouts' cam be represented as a simple string, using the english language. But how can we reason with this representation, and conclude that John believes Mary doesn't like brussel sprouts?"
3. "The requirement for a clear and precise way of representing knowledge means that we need to have a well-defined syntax and semantics."
4. "We can't represent explicitly everything that the system might ever need to know -- some things should be left implicit, to be deduced by the system as and when needed in problem solving."
5. "In practice the choice of language depends on the reasoning task(Just as the choice of a programming language depends on the problem)."
6. "Broadly speaking, there are three main approaches to knowledge representation in AI. The most arguably the use of logic to represent things."
7. "A logic, almost by definition, has a well defined syntax and semantics and is concerned with truth preserving inference, so seems like a good candidate as a method to represent and reason with knowledge."
8. "Another important methods for representing knowledge is the use of IF-THEN or condition-action rules, within a rule-base system."
9. "A rule-based language will provide algorithms for reasoning with such rules, so that new conclusions can be drawn in a controlled manner."
10. "Although condition-action rules may be similar to logical implications, the emphasis of rule-based representation languages tends to be different, with more emphasis on what you do with the rules and less on what they mean."


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