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Propositional Logic:
This is a fairly restrictive logic, that allows us to be write sentences about ¬propositions - statements about the world - that can either be true or false. The symbols use in this logic are (i) capital letters like as P, Q and R which represent propositions such as: "It is raining" and "I am wet", (ii)connectives which are: and (^),or (?),implies (→)and not (¬).(iii) brackets and (iv) T that stands for the proposition "true", and F that stands for the proposition "false". The syntax of this logic are the rules specifying where that in a sentence the connectives can go, for example must be go among of two propositions, or between a bracketed conjunction of propositions, etc.
The semantics of this logic are rules just about how to consign truth values to a sentence if we know whether we have to mentioned the propositions in the sentence are may be true or not. For this instance, one rule is which the sentence P^Q is true only in the situation whether both P and Q are true. The rules also dictate how to need brackets. As a most easy example, we can represent the knowledge in
English which is "I always get wet and annoyed when it rains" as:
It is raining → I am wet ^ I am annoyed.
However, if at some stage if we just program our agent with the semantics of propositional logic, then we tell it that it's raining; it can infer which I will get wet and annoyed.
Arbitrary categorisation - learning decision trees: Through visualising a set of boxes with some balls in. There if all the balls were in a single box so this would be nicely
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