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appropriate problems for decision tree learning - artificial intelligenceit is a expert job in ai to select accurately the right learning
avoiding over fitting - artificial intelligenceas we discussed in the last lecture over fitting is a normal
reading decision treesthere is a link between decision tree representation and logical representations which may be exploited to form it more easy
full resolution rule - artificial intelligencenow that we know about unification we can correctly describe the complete edition of
unification algorithm - artificial intelligenceto merge two statements we should get a substitution which forms the two sentences similar remember
unification - artificial intelligencewe have said that the laws of inference for propositional logic detailed in the previous lecture can also be
conjunctive normal form -artificial intelligence for the resolution law to determine two sentences they should both be in a normalized format
binary resolutionwe looked at unit resolution a propositional inference law in the last lectureaorb notb awe may have this a bit further to
resolution method - artificial intelligencea minor miracle happened in 1965 when alan robinson published his resolution function this function uses a
proof by contradiction - artificial intelligenceso both backward chaining andforward chaining have drawbacks another approach is to think regarding
backward chaining - artificial intelligencegiven that we are just interested in constructing the path we may set our initial state to be the theorem
forward chaining - artificial intelligenceimagine we have a set of axioms which we know are true statements regarding the world if we set these to
first-order inference rules -artificial intelligencenow we have a perfect definition of a first-order model isin the same way we may define soundness
variables and quantifiers for first-order models -artificial intelligenceso what do sentences containing variables mean in other words how does first
first-order models - artificial intelligencewe proposed first-order logic like good knowledge representation language rather than propositional logic
and-elimination-introduction rule - artificial intelligenceand-eliminationin english this says that if you know that many things are all true then
propositional inference rules -artificial intelligence equivalence rules are specifically useful because of the vice-versa aspectthat means we can
de morgans lawscontinuing with the relationship between and and or we can also use de morgans law to rearrange sentences involving negation
double negation - artificial intelligencealways parents are correcting their children for the use of double negatives but we have to be very alert
associativity of connectivesin order to tell us brackets are useful when to perform calculations in arithmetic and when to evaluate the truth of
commutatively of connectivesyou will be aware from the fact that some arithmetic operators have a property that it does not matter which way around
equivalences amp rewrite rules - artificial intelligencealong with allowing us to verify trivial theorems tautologies make us able to establish that
truth tables - artificial intelligencein propositional logic where we are limited to expressing sentences where propositions are true or false - we
deductive inferences - artificial intelligencewe have described how knowledge can be represented in first-order logic and how in logic rule-based
logic-based expert systems - artificial intelligenceexpert systems are agents which are programmed to make decisions about real world situations they