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Deductive Inferences - Artificial intelligence:
We have described how knowledge can be represented in first-order logic, and how in logic rule-based expert systems expressed can be used and constructed. Now, we look at how to take home known facts regarding a domain and deduce new facts from them. In turn, this will enable agents to prove things, for example, to begin with a set of statements we believe to be true (axioms) and deduce whether another statement (theorem) is true or false. We will first look at how to tell whether a sentence in propositional logic is true or false. This will advise some equivalences among propositional sentences, which let us to rewrite sentences to other sentences which mean the similar thing, regardless of the truth or meaning of the specific propositions they contains. These are reversible inferencesthat can be applied in deduction either way. In general, we then look at propositional and first-order inference rules, which enable us deduce new sentences if we know that certain things are true, and which cannot be reversible.
Binary Resolution: However we saw unit resolution for a propositional inference rule in the previous lecture: (A? B, ¬B) /A Thus we can take this a little further to
Prolog: Still we can take our card game from the previous lecture like a case study for the implementation of a logic-based expert system. So there the rules were: four cards
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