### Artificial IntelligenceAIMA Exercises

Consider the following data set comprised of three binary input attributes ($A_1, A_2$, and $A_3$) and one binary output:
$$\begin{array} {|r|r|}\hline \textbf{Example} & A_1 & A_2 & A_3 & Output\space y \\ \hline \textbf{x}_1 & 1 & 0 & 0 & 0 \\ \textbf{x}_2 & 1 & 0 & 1 & 0 \\ \textbf{x}_3 & 0 & 1 & 0 & 0 \\ \textbf{x}_4 & 1 & 1 & 1 & 1 \\ \textbf{x}_5 & 1 & 1 & 0 & 1 \\ \hline \end{array}$$ Use the algorithm in Figure DTL-algorithm (page DTL-algorithm) to learn a decision tree for these data. Show the computations made to determine the attribute to split at each node.

Consider the following data set comprised of three binary input attributes ($A_1, A_2$, and $A_3$) and one binary output:
$$\begin{array} {|r|r|}\hline \textbf{Example} & A_1 & A_2 & A_3 & Output\space y \\ \hline \textbf{x}_1 & 1 & 0 & 0 & 0 \\ \textbf{x}_2 & 1 & 0 & 1 & 0 \\ \textbf{x}_3 & 0 & 1 & 0 & 0 \\ \textbf{x}_4 & 1 & 1 & 1 & 1 \\ \textbf{x}_5 & 1 & 1 & 0 & 1 \\ \hline \end{array}$$ Use the algorithm in Figure DTL-algorithm (page DTL-algorithm) to learn a decision tree for these data. Show the computations made to determine the attribute to split at each node.

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