Full exam for FOUNDATIONS OF ARTIFICIAL INTELLIGENCE in the Computer Engineering degree programme at Politecnico di Milano. The document covers: ARTIFICIAL INTELLIGENCE January 20, 2020 LAST NAME AND FIRST NAME ROW COLUMN ID NUMBER (CODICE PERSONA) • The exam is composed of three stapled paper sheets printed on both sides. • This front page must be filled with last name, first name, ID number, position (row and column
Full exam for FOUNDATIONS OF ARTIFICIAL INTELLIGENCE in the Computer Engineering degree programme at Politecnico di Milano. The document covers: ARTIFICIAL INTELLIGENCE January 20, 2020 LAST NAME AND FIRST NAME ROW COLUMN ID NUMBER (CODICE PERSONA) • The exam is composed of three stapled paper sheets printed on both sides. • This front page must be filled with last name, first name, ID number, position (row and column
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ARTIFICIAL INTELLIGENCE January 20, 2020 LAST NAME AND FIRST NAME ROW COLUMN ID NUMBER (CODICE PERSONA) • The exam is composed of three stapled paper sheets printed on both sides. • This front page must be filled with last name, first name, ID number, position (row and column communicated by the instructor), and signature. • Exams without a completely filled front page or with missing sheets will not be considered. • Answers can be written only on these sheets. If you need more space, please write on the last page. • Exam is closed books (i.e., no books, notebooks, notes, … are allowed). Cell phones, bags, cases, and wallets are not allowed on the desk during the exam. • All the answers must be justified. SIGNATURE Question 1 (8 points). Consider the following search problem: - initial state: 𝑠", - function ACTIONS() defined as: state 𝑠 𝑠" 𝑠# 𝑠$ 𝑠% 𝑠& 𝑠' ACTIONS(𝑠) {goto-1, goto-4} {goto-0, goto-2, goto-5} {goto-1, goto-3} {goto-2, goto-4} {goto-0, goto-3} {goto-1} - function RESULT() defined as: RESULT(𝑠, goto-𝑥) = 𝑠/, - goal test is satisfied by states 𝑠& and 𝑠' (and only by them), - step cost is unitary. (1) Represent as a graph the state space corresponding to the above problem. (2) Consider TREE-SEARCH with breadth-first search strategy. How many nodes at best and at worst are generated before a solution is found for the above search problem? Why? (3) Is the solution found in (2) guaranteed to be optimal? Why? (4) Consider TREE-SEARCH with depth-first search strategy. How many nodes at best and at worst are generated before a solution is found for the above search problem? Why? (5) Is the solution found in (4) guaranteed to be optimal? Why? (6) Consider GRAPH-SEARCH with greedy best-first search strategy. Is there any heuristic function ℎ() such that a node…
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