001 package aima.search.demos;
002
003 import java.util.HashSet;
004 import java.util.Iterator;
005 import java.util.List;
006 import java.util.Properties;
007 import java.util.Set;
008
009 import aima.search.framework.GraphSearch;
010 import aima.search.framework.Problem;
011 import aima.search.framework.Search;
012 import aima.search.framework.SearchAgent;
013 import aima.search.framework.TreeSearch;
014 import aima.search.informed.HillClimbingSearch;
015 import aima.search.informed.SimulatedAnnealingSearch;
016 import aima.search.informed.ga.GeneticAlgorithm;
017 import aima.search.nqueens.NQueensBoard;
018 import aima.search.nqueens.NQueensFitnessFunction;
019 import aima.search.nqueens.NQueensGoalTest;
020 import aima.search.nqueens.NQueensSuccessorFunction;
021 import aima.search.nqueens.QueensToBePlacedHeuristic;
022 import aima.search.uninformed.BreadthFirstSearch;
023 import aima.search.uninformed.DepthFirstSearch;
024 import aima.search.uninformed.DepthLimitedSearch;
025 import aima.search.uninformed.IterativeDeepeningSearch;
026
027 /**
028 * @author Ravi Mohan
029 *
030 */
031
032 public class NQueensDemo {
033
034 public static void main(String[] args) {
035
036 newNQueensDemo();
037 }
038
039 private static void newNQueensDemo() {
040
041 nQueensWithDepthFirstSearch();
042 nQueensWithBreadthFirstSearch();
043 nQueensWithRecursiveDLS();
044 nQueensWithIterativeDeepeningSearch();
045 nQueensSimulatedAnnealingSearch();
046 nQueensHillClimbingSearch();
047 nQueensGeneticAlgorithmSearch();
048 }
049
050 private static void nQueensWithRecursiveDLS() {
051 System.out.println("\nNQueensDemo recursive DLS -->");
052 try {
053 Problem problem = new Problem(new NQueensBoard(8),
054 new NQueensSuccessorFunction(), new NQueensGoalTest());
055 Search search = new DepthLimitedSearch(8);
056 SearchAgent agent = new SearchAgent(problem, search);
057 printActions(agent.getActions());
058 printInstrumentation(agent.getInstrumentation());
059 } catch (Exception e) {
060 e.printStackTrace();
061 }
062
063 }
064
065 private static void nQueensWithBreadthFirstSearch() {
066 try {
067 System.out.println("\nNQueensDemo BFS -->");
068 Problem problem = new Problem(new NQueensBoard(8),
069 new NQueensSuccessorFunction(), new NQueensGoalTest());
070 Search search = new BreadthFirstSearch(new TreeSearch());
071 SearchAgent agent2 = new SearchAgent(problem, search);
072 printActions(agent2.getActions());
073 printInstrumentation(agent2.getInstrumentation());
074 } catch (Exception e1) {
075
076 e1.printStackTrace();
077 }
078 }
079
080 private static void nQueensWithDepthFirstSearch() {
081 System.out.println("\nNQueensDemo DFS -->");
082 try {
083 Problem problem = new Problem(new NQueensBoard(8),
084 new NQueensSuccessorFunction(), new NQueensGoalTest());
085 Search search = new DepthFirstSearch(new GraphSearch());
086 SearchAgent agent = new SearchAgent(problem, search);
087 printActions(agent.getActions());
088 printInstrumentation(agent.getInstrumentation());
089 } catch (Exception e) {
090 e.printStackTrace();
091 }
092 }
093
094 private static void nQueensWithIterativeDeepeningSearch() {
095 System.out.println("\nNQueensDemo Iterative DS -->");
096 try {
097 Problem problem = new Problem(new NQueensBoard(8),
098 new NQueensSuccessorFunction(), new NQueensGoalTest());
099 Search search = new IterativeDeepeningSearch();
100 SearchAgent agent = new SearchAgent(problem, search);
101
102 System.out.println();
103 printActions(agent.getActions());
104 printInstrumentation(agent.getInstrumentation());
105 } catch (Exception e) {
106 e.printStackTrace();
107 }
108 }
109
110 private static void nQueensSimulatedAnnealingSearch() {
111 System.out.println("\nNQueensDemo Simulated Annealing -->");
112 try {
113 Problem problem = new Problem(new NQueensBoard(8),
114 new NQueensSuccessorFunction(), new NQueensGoalTest(),
115 new QueensToBePlacedHeuristic());
116 SimulatedAnnealingSearch search = new SimulatedAnnealingSearch();
117 SearchAgent agent = new SearchAgent(problem, search);
118
119 System.out.println();
120 printActions(agent.getActions());
121 System.out.println("Search Outcome=" + search.getOutcome());
122 System.out.println("Final State=\n" + search.getLastSearchState());
123 printInstrumentation(agent.getInstrumentation());
124 } catch (Exception e) {
125 e.printStackTrace();
126 }
127 }
128
129 private static void nQueensHillClimbingSearch() {
130 System.out.println("\nNQueensDemo HillClimbing -->");
131 try {
132 Problem problem = new Problem(new NQueensBoard(8),
133 new NQueensSuccessorFunction(), new NQueensGoalTest(),
134 new QueensToBePlacedHeuristic());
135 HillClimbingSearch search = new HillClimbingSearch();
136 SearchAgent agent = new SearchAgent(problem, search);
137
138 System.out.println();
139 printActions(agent.getActions());
140 System.out.println("Search Outcome=" + search.getOutcome());
141 System.out.println("Final State=\n" + search.getLastSearchState());
142 printInstrumentation(agent.getInstrumentation());
143 } catch (Exception e) {
144 e.printStackTrace();
145 }
146 }
147
148 public static void nQueensGeneticAlgorithmSearch() {
149 System.out.println("\nNQueensDemo GeneticAlgorithm -->");
150 try {
151 int boardSize = 8;
152 NQueensFitnessFunction fitnessFunction = new NQueensFitnessFunction();
153 // Generate an initial population
154 Set<String> population = new HashSet<String>();
155 for (int i = 0; i < 20; i++) {
156 population.add(fitnessFunction
157 .generateRandomIndividual(boardSize));
158 }
159
160 GeneticAlgorithm ga = new GeneticAlgorithm(boardSize,
161 fitnessFunction.getFiniteAlphabetForBoardOfSize(boardSize),
162 0.15);
163
164 // Run for a set number of iterations
165 String bestIndividual = ga.geneticAlgorithm(population,
166 fitnessFunction, 100);
167
168 System.out.println("Iterations Best Individual=\n"
169 + fitnessFunction.getBoardForIndividual(bestIndividual));
170 System.out.println("Fitness="
171 + fitnessFunction.getValue(bestIndividual));
172 System.out.println("Is Goal="
173 + fitnessFunction.isGoalState(bestIndividual));
174 System.out.println("Itertions=" + ga.getIterations());
175
176 // Run till goal is achieved
177 bestIndividual = ga.geneticAlgorithm(population, fitnessFunction,
178 fitnessFunction);
179
180 System.out.println("Goal Test Best Individual=\n"
181 + fitnessFunction.getBoardForIndividual(bestIndividual));
182 System.out.println("Fitness="
183 + fitnessFunction.getValue(bestIndividual));
184 System.out.println("Is Goal="
185 + fitnessFunction.isGoalState(bestIndividual));
186 System.out.println("Itertions=" + ga.getIterations());
187
188 } catch (Exception e) {
189 e.printStackTrace();
190 }
191 }
192
193 private static void printInstrumentation(Properties properties) {
194 Iterator keys = properties.keySet().iterator();
195 while (keys.hasNext()) {
196 String key = (String) keys.next();
197 String property = properties.getProperty(key);
198 System.out.println(key + " : " + property);
199 }
200
201 }
202
203 private static void printActions(List actions) {
204 for (int i = 0; i < actions.size(); i++) {
205 String action = (String) actions.get(i);
206 System.out.println(action);
207 }
208 }
209
210 }