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Unification Algorithm:
Here if notice for instance that to unify two sentences as we must find a substitution that makes the two sentences the same. Furthermore remember there that we write V/T to signify in which we have substituted term T for variable V as read the "/" sign as "is substituted by". So now here our purpose of this algorithm will be to produce a substitution as a set of pairs V/T for a given pair of sentences. Here notice for instance example like the output for the pair of sentences as:
knows(john,X)
knows(john, mary)
will be: {X/mary}. Whether for the two sentences above involving jack and the function should fail as there was no way to unify the sentences.
There the function isa_variable(x) checks where x is a variable.
Here if we considered for instance that either a predicate or type a function or a connective usually contains subparts. Hence the subparts of a predicate or function are the arguments. Now there subparts of a connective are the things it connects. In fact we write args(x) for the subparts of compound expression x. So always remember that args(x) outputs a list: as the list of subparts.
ARQ is transmitted in the event of: (A) Loss of signal (B) Error in received data (C) Improve reliability (D) During time out
Structural Classification Flynn's classification examine the behavioural concept and does not receive into consideration the computer's structure. Parallel computers can be cla
What is verilog case (1) ? wire [3:0] x; always @(...) begin case (1'b1) x[0]: SOMETHING1; x[1]: SOMETHING2; x[2]: SOMETHING3; x[3]: SOMETHING4; endcase
Minimize the logic function F(A, B, C, D) = ∑ m(1,3,5,8,9,11,15) + d(2,13) using NAND gate with help of K-map. Ans. Realization of given expression by using NAND gates: In
Your code will be tested using a command script. The script is available on Blackboard in the archive MIPSimTest.zip. It contains a ReadMe file that explains how to run the script
Disadvantages of macro processor
explain in RAID
What is called static and dynamic branch prediction? The branch prediction decision is always the similar every time a given instruction is implemented. Any approach that has t
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Q. Example on Cyclic Distribution of data? !HPF$ PROCESSORS P1(4) !HPF$ TEMPLATE T1(18) !HPF$ DISTRIBUTE T1(CYCLIC) ONTO P1 The result of these instructions is display
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