Question: This is the code for 8 puzzle public class EightPuzzleDemo { static EightPuzzleBoard boardWithThreeMoveSolution = new EightPuzzleBoard(new int[] { 1, 2, 5, 3, 4, 0,

This is the code for 8 puzzle

public class EightPuzzleDemo { static EightPuzzleBoard boardWithThreeMoveSolution = new EightPuzzleBoard(new int[] { 1, 2, 5, 3, 4, 0, 6, 7, 8 });;

static EightPuzzleBoard random1 = new EightPuzzleBoard(new int[] { 1, 4, 2, 7, 5, 8, 3, 0, 6 });

static EightPuzzleBoard extreme = new EightPuzzleBoard(new int[] { 0, 8, 7, 6, 5, 4, 3, 2, 1 });

public static void main(String[] args) { eightPuzzleDLSDemo(); eightPuzzleIDLSDemo(); eightPuzzleGreedyBestFirstDemo(); eightPuzzleGreedyBestFirstManhattanDemo(); eightPuzzleAStarDemo(); eightPuzzleAStarManhattanDemo(); eightPuzzleSimulatedAnnealingDemo(); }

private static void eightPuzzleDLSDemo() { System.out.println(" EightPuzzleDemo recursive DLS (9) -->"); try { Problem problem = new Problem(boardWithThreeMoveSolution, EightPuzzleFunctionFactory.getActionsFunction(), EightPuzzleFunctionFactory.getResultFunction(), new EightPuzzleGoalTest()); SearchForActions search = new DepthLimitedSearch(9); SearchAgent agent = new SearchAgent(problem, search); printActions(agent.getActions()); printInstrumentation(agent.getInstrumentation()); } catch (Exception e) { e.printStackTrace(); }

}

private static void eightPuzzleIDLSDemo() { System.out.println(" EightPuzzleDemo Iterative DLS -->"); try { Problem problem = new Problem(random1, EightPuzzleFunctionFactory.getActionsFunction(), EightPuzzleFunctionFactory.getResultFunction(), new EightPuzzleGoalTest()); SearchForActions search = new IterativeDeepeningSearch(); SearchAgent agent = new SearchAgent(problem, search); printActions(agent.getActions()); printInstrumentation(agent.getInstrumentation()); } catch (Exception e) { e.printStackTrace(); }

}

private static void eightPuzzleGreedyBestFirstDemo() { System.out.println(" EightPuzzleDemo Greedy Best First Search (MisplacedTileHeursitic)-->"); try { Problem problem = new Problem(boardWithThreeMoveSolution, EightPuzzleFunctionFactory.getActionsFunction(), EightPuzzleFunctionFactory.getResultFunction(), new EightPuzzleGoalTest()); SearchForActions search = new GreedyBestFirstSearch(new GraphSearch(), new MisplacedTilleHeuristicFunction()); SearchAgent agent = new SearchAgent(problem, search); printActions(agent.getActions()); printInstrumentation(agent.getInstrumentation()); } catch (Exception e) { e.printStackTrace(); }

}

private static void eightPuzzleGreedyBestFirstManhattanDemo() { System.out.println(" EightPuzzleDemo Greedy Best First Search (ManhattanHeursitic)-->"); try { Problem problem = new Problem(boardWithThreeMoveSolution, EightPuzzleFunctionFactory.getActionsFunction(), EightPuzzleFunctionFactory.getResultFunction(), new EightPuzzleGoalTest()); SearchForActions search = new GreedyBestFirstSearch(new GraphSearch(), new ManhattanHeuristicFunction()); SearchAgent agent = new SearchAgent(problem, search); printActions(agent.getActions()); printInstrumentation(agent.getInstrumentation()); } catch (Exception e) { e.printStackTrace(); }

}

private static void eightPuzzleAStarDemo() { System.out.println(" EightPuzzleDemo AStar Search (MisplacedTileHeursitic)-->"); try { Problem problem = new Problem(random1, EightPuzzleFunctionFactory.getActionsFunction(), EightPuzzleFunctionFactory.getResultFunction(), new EightPuzzleGoalTest()); SearchForActions search = new AStarSearch(new GraphSearch(), new MisplacedTilleHeuristicFunction()); SearchAgent agent = new SearchAgent(problem, search); printActions(agent.getActions()); printInstrumentation(agent.getInstrumentation()); } catch (Exception e) { e.printStackTrace(); }

}

private static void eightPuzzleSimulatedAnnealingDemo() { System.out.println(" EightPuzzleDemo Simulated Annealing Search -->"); try { Problem problem = new Problem(random1, EightPuzzleFunctionFactory.getActionsFunction(), EightPuzzleFunctionFactory.getResultFunction(), new EightPuzzleGoalTest()); SimulatedAnnealingSearch search = new SimulatedAnnealingSearch(new ManhattanHeuristicFunction()); SearchAgent agent = new SearchAgent(problem, search); printActions(agent.getActions()); System.out.println("Search Outcome=" + search.getOutcome()); System.out.println("Final State= " + search.getLastSearchState()); printInstrumentation(agent.getInstrumentation()); } catch (Exception e) { e.printStackTrace(); } }

private static void eightPuzzleAStarManhattanDemo() { System.out.println(" EightPuzzleDemo AStar Search (ManhattanHeursitic)-->"); try { Problem problem = new Problem(random1, EightPuzzleFunctionFactory.getActionsFunction(), EightPuzzleFunctionFactory.getResultFunction(), new EightPuzzleGoalTest()); SearchForActions search = new AStarSearch(new GraphSearch(), new ManhattanHeuristicFunction()); SearchAgent agent = new SearchAgent(problem, search); printActions(agent.getActions()); printInstrumentation(agent.getInstrumentation()); } catch (Exception e) { e.printStackTrace(); }

}

private static void printInstrumentation(Properties properties) { Iterator keys = properties.keySet().iterator(); while (keys.hasNext()) { String key = (String) keys.next(); String property = properties.getProperty(key); System.out.println(key + " : " + property); }

}

private static void printActions(List actions) { for (int i = 0; i < actions.size(); i++) { String action = actions.get(i).toString(); System.out.println(action); } }

}

2nd Code Random 8 puzzle

package aima.gui.demo.search;

import java.util.Random;

import aima.core.environment.eightpuzzle.EightPuzzleBoard;

/** * @author Ravi Mohan * */

public class GenerateRandomEightPuzzleDemo { public static void main(String[] args) { Random r = new Random(); EightPuzzleBoard board = new EightPuzzleBoard(new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8 }); for (int i = 0; i < 50; i++) { int th = r.nextInt(4); if (th == 0) { board.moveGapUp(); } if (th == 1) { board.moveGapDown(); } if (th == 2) { board.moveGapLeft(); } if (th == 3) { board.moveGapRight(); } } System.out.println(board); }

}

Question:

  1. Modify the DLS to simulate depth-first search. Find the best depth-limit prove that this is indeed the best depth-limit. Find the worth depth-limit based on your experiments. Provide a log of all experiments and the results to support you answer.
  2. Compare the performance of A* and recursive best-first search on a set of randomly generated problems with Manhattan distance heuristic. Discuss your results.
  3. Compare the performance of A* with Manhattan distance and A* with Misplaced tiles Heuristic on randomly generated problems. Explain the differences.

If you could help in this really thankfull to u

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