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and-elimination rulein generally english says that if you know that lots of things are all true so you know like any one of them is also true because
implication connective - modus ponens rulewe notice that this is a trivial example so it highlights how we use truth tables as the first line is the
propositional inference rulespropositional inference rules equivalence rules are mostly useful because of the vice-versa aspect that means like we
eequivalences rulesthis conveys a meaning that is actually much simpler so than you would think on first inspection hence we can justify this by
equivalencesin this following miscellaneous equivalence rules are often useful during rewriting sessions so there the first two allow us to
double negation all parents are forever correcting their children for the find of double negatives there we have to be very alert with them in
associativity of connectives here brackets are very important in order to tell us where to perform calculations in arithmetic and logic by using
extension of propositional logicaway from proving theorems directly and the other main use for rewrite rules is to prepare a statement just for
rule in a single direction - equivalences ruleshence there the power to replace sub expressions always allows use to prove theorems with
replacement and substitutionhowever equivalences allow us to change one sentence with another without affecting the meaning it means we know already
avoiding overfitting however remember there that in the previous lecture there is over fitting that common problem in machine learning furthermore
appropriate problems for decision tree learning however remember there that is a skilled job in ai to choose exactly the right learning
biological motivation however remember there that in discussion first lecture is about how people have answered the question like how are we going to
two layer artificial neural networkshowever decision trees are whenever powerful they are as a simple representation scheme whereas graphical on the
multi-layer network architecturesas we considered we saw in the previous lecture that perceptrons have limited scope in the type of concepts that
multi-layer artificial neural networks however we can now look at more sophisticated anns that are known as multi-layer artificial neural networks it
algorithmic complexity theorymoreover a similar situation occurs in broad to specific ilp systems when the inference rules are deductive thus they
pruning and sortingthis means we can test where each hypothesis explains as entails a common example that we can associate to a hypothesis a set of
problem specification hence given the above context for ilp there we can state the learning problem as follows that we are given a set of positive
prior conditions - logic programshowever firstly there we must make sure that our problem has a solution whether one of the negative examples can be
background examples and hypothesisnow we will switch off with three logic programs so very firstly we will have the logic program representing a set
logic programsa subset of first order logic is logic programs however logic program having a set of horn clauses that are implication conjectures
problem context and specification however the development of inductive logic programming has been heavily formal in mathematical in nature it means
creation deductive inferences here we have shown how knowledge can be represented in first-order logic or how rule-based expert systems expressed in
prologstill 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