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Material Type: Assignment; Class: Intro Artifical Intelligence; Subject: Computer Applications; University: Florida Atlantic University; Term: Unknown 1989;
Typology: Assignments
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CAP4630 Homework 4 (14 pts)
Due April 13
Question 1: Please show detailed steps that sentence B 1 (^) , 4 ⇔ ( P 1 , 3 ∨ P 2 , 4 )can be
decomposed into CNF (1 pts)
Question 2: Minesweeper, the well-known computer game, is closely related to the wumpus world. A minesweeper world is a rectangular grid of N squares with M invisible mines scattered among them. Any square may be probed by the agent; instance death follows if a mine is probed. Minesweeper indicates the presence of mines by revealing, in each probed square, the number of mines that are directly or diagonally adjacent. The goal is to have probed every unmined square.
a. Let Xi,j be true iff square [i,j] contains a mine. Write down the assertion that there are exactly two mines adjacent to [1,1] as a sentence involving some logical combination of Xi,j propositions (1 pt)
b. Generalize your assertion from (a) by explaining how to construct a CNF sentence asserting that k of n neighbors contain mines (1pt)
Question 3: What is a Horn Clause? (0.5 pt) What is the purpose of using horn clauses? (0.5 pt). Given the following knowledge base of Horn clauses, please draw the corresponding AND-OR graph (1 pt). Please using PL-FC-ENTAIL?(KB, q) to whether KB╞Q is true or not (show your solutions 2 pts)
P⇒Q; B∧L∧M⇒ P; A∧L⇒M; A∧B⇒L; A; B;
Question 4: What is a decision tree (0.25 pt), what is entropy (0.25), what is information gain (0.25 pt), what is information gain ratio (0.25 pt), what is the bias of the decision tree (0.25 pt), what is the bias of the information gain (0.25 pt), what is overfitting in decision tree construction (0.25 pt), how to avoid overfitting (0.25 pt).
Question 5: Given the following toy dataset, please construct a decision tree by using Information Gain Ratio as the attribute selection criteria (You must list the major steps of the three constructions, along with the final decision tree) (5 pts).
ID Outlook Temperature Humidity Wind Class 1 Sunny Hot High Weak No 2 Sunny Hot High Strong No 3 Overcast Hot High Weak Yes 4 Rain Mild High Weak Yes 5 Rain Cool Normal Weak Yes 6 Rain Cool Normal Strong No 7 Overcast Cool Normal Strong Yes 8 Sunny Mild High Weak No 9 Sunny Cool Normal Weak Yes 10 Rain Mild Normal Weak Yes 11 Sunny Mild Normal Strong Yes 12 Overcast Mild High Strong Yes 13 Overcast Mild Normal Weak No 14 Rain Hot High Strong Yes 15 Rain Mild High Strong No