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Propositional Logic - artificial intelligence:
This is a limited logic, which permit us to write sentences about propositions - statements about the world - which can either be false or true. The symbols in this logic are (i) connectives which are: and (∧), or (∨), not (¬), and implies (->) (ii) capital letters such as Q,P and R which represent propositions such as: "It is raining" and "I am wet", (iii) brackets and (iv) T which stands for the proposition "true", and F which stands for the proposition "false". The syntax of this logic are the rules specifying where in a sentence the connectives may go, for example ∨ might go between two propositions, or between a bracketed conjunction of propositions and so on .
The semantics of this logic are rules about how to assign truth values to a sentence if we know whether the propositions mentioned in the sentence are true or not. One rule is that the sentence P ∨ Q is true in the situation only when P and Q both are true. The rules also dictate how to use brackets. As a very simple example, we may represent the knowledge in English that "I always get wet and annoyed when it rains" as:
I am wet -> It is raining ∧ I am annoyed
If we program our agent with the semantics of propositional logic, then if at some stage, we tell it that it is raining; it can infer that I will get wet and annoyed.
First-Order Models: Here if we proposed first-order logic as a good knowledge representation language than propositional logic is just because there is more expressive than we
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