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Question 2

Suppose that in the output of a neural network, we assign any value over 0.5 to be true and any less than 0.5 to be false (i.e., any positive value before the activation function is true, and a negative value is false).

Given the final parameter settings, of Figure 11.7 of the text, give a logical formula (or a decision tree or a set of rules) that represents the Boolean function that is the value for the hidden units and the output units. This formula or set of rules shouldn't refer to any real numbers.

Hint: one brute-force method is to go through the 16 combinations of values for the inputs to each hidden unit and determine the truth value of the output. A better method is to try to understand the functions themselves. Your meta-interpreter for parameterized logic programs may help here.

Solution to Question 2