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Agent3.java
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Agent3.java
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import java.io.FileWriter;
import java.io.IOException;
import java.util.*;
public class Main {
public static void main(String[] args) throws NullPointerException {
List<Double> pData = new ArrayList<>();
List<Integer> noOfBumps = new ArrayList<>();
List<Integer> cellsProcessed = new ArrayList<>();
List<Integer> finalLength = new ArrayList<>();
List<Boolean> solvabilty = new ArrayList<>();
List<Integer> trajectoryLen = new ArrayList<>();
List<Float> trajectoryDivideLength = new ArrayList<>();
for (int l = 0; l < 1; l++) {
int dim = 40;
int values[][] = new int[dim][dim];
int dummyValues[][] = new int[dim][dim];
double beliefState[][] = new double[dim][dim];
// double p = Math.random();
// while(p>0.33){
// p = Math.random();
// }
double p = 0.3;
double t=0.23;
//creating a dummy grid with no blocked cells
for (int rn = 0; rn < dummyValues.length; rn++) {
for (int cl = 0; cl < dummyValues[rn].length; cl++) {
dummyValues[rn][cl] = 0;
System.out.print(dummyValues[rn][cl]);
}
System.out.println();
}
System.out.println("Done printing the dummy grid");
for (int i = 0; i < values.length; i++) {
// do the for in the row according to the column size
for (int j = 0; j < values[i].length; j++) {
if (i == 0 && j == 0 || i == dim - 1 && j == dim - 1) {
values[i][j] = 0;
} else {
if (Math.random() >= p) {
values[i][j] = 0;
} else {
values[i][j] = 1;
}
}
System.out.print(values[i][j]);
}
// add a new line
System.out.println();
}
System.out.println("Done Printing the actual grid");
//terrain grid
/*for (int i = 0; i < values.length; i++) {
// do the for in the row according to the column size
for (int j = 0; j < values[i].length; j++) {
double random = Math.random();
//System.out.println(random);
if (random >= p) {
if(random>=0.3 && random<=0.54){
//flat terrain
values[i][j] = 2;
}else if(random>=0.54 && random<=0.77){
//hilly terrain
values[i][j] = 3;
}else if(random>=0.77 && random<=1.0){
//forest terain
values[i][j] = 4;
}
} else {
values[i][j] = 1;
}
System.out.print(values[i][j]);
}
// add a new line
System.out.println();
}
*/ //Assign agent and target to cells
/*
int min=1;
int max = dim;
int x1=-1;
int y1=-1;
int x2=-1;
int y2=-1;
for(int i=0;i<2;i++){
if(i==0){
do{
x1 = min + (int)(Math.random() * ((max - min) + 1));
y1 = min + (int)(Math.random() * ((max - min) + 1));
}while(values[x1][y1]==1);
}
if(i==1){
do{
x2 = min + (int)(Math.random() * ((max - min) + 1));
y2 = min + (int)(Math.random() * ((max - min) + 1));
}while(values[x2][y2]==1);
}
}
*/
Solution1 sol = new Solution1();
//grid is the actual maze
int[][] grid = values;
//int[][] grid ={{0,0,1,0},{0,0,0,0},{1,1,0,1},{0,0,0,0}};
//int[][] grid ={{0,0,1,1,1},{1,0,1,1,1},{0,0,1,1,1},{0,1,1,1,1},{0,0,0,0,0}};
int x1=0; //Assuming left corner as start, right corner bottom as end
int y1=0;
int x2=grid.length-1;
int y2=grid.length-1;
//newGrid is the maze with no blockage
int[][] newGrid = dummyValues;
int length = sol.shortestPathBinaryMatrix(grid,x1,y1,x2,y2);
System.out.println(length);
System.out.println();
if (length != -1) {
// float ans = sol.RFAstarTest(newGrid,0,0,1,grid);
int ans = sol.AgentThree(newGrid, 0, 0, 1, grid);
System.out.println(ans);
pData.add(p);
//float result = (float)ans/(float)length;
// System.out.println(ans);
// trajectoryLen.add(ans);
int cells = sol.noOfCellsProcessed;
//noOfBumps.add(ans);
finalLength.add(ans);
//trajectoryLen.add(ans);
cellsProcessed.add(cells);
// trajectoryDivideLength.add(ans);
}
//System.out.println(cells);
//No.of.cells processed in RFA
//int cellsRFA = sol.cellsProcessedRFA()-1;
//pData.add(p);
//lengthData.add(length);
//cellsProcessedRFA.add(cellsRFA);
}
FileWriter locFile1 = null;
FileWriter locFile2 = null;
// FileWriter locFile3 = null;
// FileWriter locFile4 = null;
// FileWriter locFile5 = null;
// FileWriter locFile6 = null;
try{
locFile1 = new FileWriter("finalLen.txt");
// locFile2 = new FileWriter("lengthData.txt");
locFile2 = new FileWriter("cellsProcessed.txt");
// locFile4 = new FileWriter("solvability.txt");
// locFile5 = new FileWriter("cellProcessedRFA.txt");
// locFile6 = new FileWriter("trajectoryDivideLen.txt");
for(int i = 0;i<pData.size();i++){
locFile1.write(finalLength.get(i)+"\n");
locFile2.write(cellsProcessed.get(i)+"\n");
// locFile3.write(cellsProcessed.get(i)+"\n");
// locFile4.write(solvabilty.get(i)+"\n");
// locFile5.write(cellsProcessedRFA.get(i)+"\n");
// locFile6.write(trajectoryDivideLength.get(i)+"\n");
}
// locFile6.close();
// locFile5.close();
// locFile4.close();
// locFile3.close();
locFile2.close();
locFile1.close();
}catch (IOException e){
System.out.println("In catch block");
e.printStackTrace();
}
}
// public static int[][] newMaze(){
// Scanner scanner = new Scanner(System.in);
// int[][] maze = new int[4][4];
// for(int i =0;i<4;i++){
// for (int j =0;j<4;j++){
// maze[i][j] = scanner.nextInt();
// }
// }
// for(int i =0;i<4;i++){
// for (int j =0;j<4;j++){
// System.out.println(maze[i][j]);
// }
// System.out.println();
// }
// return maze;
//
// }
}
class Solution1 {
double flatFNR = 0.2;
double hillyFNR = 0.5;
double forestFNR = 0.8;
Map<List<Integer>, List<Integer>> pathMapRFA = new HashMap<List<Integer>, List<Integer>>();
List<Candidate> track = new ArrayList<Candidate>();
//Map<Integer,List<Candidate>> inferenceKnowledge = new HashMap<Integer,List<Candidate>();
List<Equation> inferenceKnowledge = new ArrayList<Equation>();
int noOfCellsProcessed = 0;
boolean[][] visitedRFA = new boolean[8][8];
//Queue<Candidate> pqRFA = new PriorityQueue<>((a, b) -> a.totalEstimate - b.totalEstimate);
// Candidate represents a possible option for travelling to the cell
// at (row, col).
class Candidate {
public int row;
public int col;
public int distanceSoFar;
public int totalEstimate;
public Candidate(int row, int col, int distanceSoFar, int totalEstimate) {
this.row = row;
this.col = col;
this.distanceSoFar = distanceSoFar;
this.totalEstimate = totalEstimate;
}
}
class Equation {
public List<Candidate> candidateList;
public int c;
public Equation(List<Candidate> candidateList , int c) {
this.candidateList = candidateList;
this.c = c;
//inferenceKnowledge.put(c,candidateList);
}
}
private static final int[][] directions =
new int[][]{{-1, 0}, {0, -1}, {0, 1}, {1, 0}};
//Covering diagonal neighbors -Agent-3
private static final int[][] directionsAgent3 =
new int[][]{{-1, -1}, {-1, 0}, {-1, 1}, {0, -1}, {0, 1}, {1, -1}, {1, 0}, {1, 1}};
// public int shortestPathBinaryMatrix(int[][] grid) {
//
// // Firstly, we need to check that the start and target cells are open.
// if (grid[0][0] != 0 || grid[grid.length - 1][grid[0].length - 1] != 0) {
// return -1;
// }
//
// // Set up the A* search.
// Queue<Candidate> pq = new PriorityQueue<>((a, b) -> a.totalEstimate - b.totalEstimate);
// pq.add(new Candidate(0, 0, 1, estimate(0, 0, grid)));
//
// boolean[][] visited = new boolean[grid.length][grid[0].length];
// Map<List<Integer>, List<Integer>> pathMap = new HashMap<List<Integer>, List<Integer>>();
//
//
// // Carry out the A* search.
// while (!pq.isEmpty()) {
//
// Candidate best = pq.remove();
// // int counter = cellsProcessed();
//
//
// // Is this for the target cell?
// if (best.row == grid.length - 1 && best.col == grid[0].length - 1) {
//
// List<Integer> goalnode = new ArrayList<>();
// goalnode.add(best.row);
// goalnode.add(best.col);
// System.out.print(goalnode);
// System.out.print("->");
// List<Integer> result = pathMap.get(goalnode);
// for (int i = 0; i < best.distanceSoFar; i++) {
// System.out.print(result);
// System.out.print("->");
// result = pathMap.get(result);
//
// }
//
// return best.distanceSoFar;
// }
//
// // Have we already found the best path to this cell?
// if (visited[best.row][best.col]) {
// continue;
// }
//
// visited[best.row][best.col] = true;
// // pathQueue.add({new int[]{best.row,best.col},{});
//
// for (int[] neighbour : getNeighbours(best.row, best.col, grid)) {
// int neighbourRow = neighbour[0];
// int neighbourCol = neighbour[1];
//
// // This check isn't necessary for correctness, but it greatly
// // improves performance.
// if (visited[neighbourRow][neighbourCol]) {
// continue;
// }
//
// // Otherwise, we need to create a Candidate object for the option
// // of going to neighbor through the current cell.
// int newDistance = best.distanceSoFar + 1;
// int totalEstimate = newDistance + estimate(neighbourRow, neighbourCol, grid);
// Candidate candidate =
// new Candidate(neighbourRow, neighbourCol, newDistance, totalEstimate);
// pq.add(candidate);
// List<Integer> neigh = new ArrayList<>();
// neigh.add(neighbourRow);
// neigh.add(neighbourCol);
// List<Integer> parent = new ArrayList<>();
// parent.add(best.row);
// parent.add(best.col);
// if (pathMap.containsKey(neigh)) {
// continue;
// } else {
// pathMap.put(neigh, parent);
// }
//
//
// }
// }
// // The target was unreachable.
// return -1;
// }
public int shortestPathBinaryMatrix(int[][] grid,int x1,int y1,int x2,int y2) {
// Firstly, we need to check that the start and target cells are open.
if (grid[x1][y1] == 1 || grid[x2][y2] == 1) {
return -1;
}
// Set up the A* search.
Queue<Candidate> pq = new PriorityQueue<>((a, b) -> a.totalEstimate - b.totalEstimate);
pq.add(new Candidate(x1, y1, 1, estimate(x1, y1, grid)));
boolean[][] visited = new boolean[grid.length][grid[0].length];
Map<List<Integer>, List<Integer>> pathMap = new HashMap<List<Integer>, List<Integer>>();
// Carry out the A* search.
while (!pq.isEmpty()) {
Candidate best = pq.remove();
// int counter = cellsProcessed();
// Is this for the target cell?
if (best.row == x2 && best.col == y2) {
List<Integer> goalnode = new ArrayList<>();
goalnode.add(best.row);
goalnode.add(best.col);
System.out.print(goalnode);
System.out.print("->");
List<Integer> result = pathMap.get(goalnode);
for (int i = 0; i < best.distanceSoFar; i++) {
System.out.print(result);
System.out.print("->");
result = pathMap.get(result);
}
return best.distanceSoFar;
}
// Have we already found the best path to this cell?
if (visited[best.row][best.col]) {
continue;
}
visited[best.row][best.col] = true;
// pathQueue.add({new int[]{best.row,best.col},{});
for (int[] neighbour : getNeighbours(best.row, best.col, grid)) {
int neighbourRow = neighbour[0];
int neighbourCol = neighbour[1];
// This check isn't necessary for correctness, but it greatly
// improves performance.
if (visited[neighbourRow][neighbourCol]) {
continue;
}
// Otherwise, we need to create a Candidate object for the option
// of going to neighbor through the current cell.
int newDistance = best.distanceSoFar + 1;
int totalEstimate = newDistance + estimate(neighbourRow, neighbourCol, grid);
Candidate candidate =
new Candidate(neighbourRow, neighbourCol, newDistance, totalEstimate);
pq.add(candidate);
List<Integer> neigh = new ArrayList<>();
neigh.add(neighbourRow);
neigh.add(neighbourCol);
List<Integer> parent = new ArrayList<>();
parent.add(best.row);
parent.add(best.col);
if (pathMap.containsKey(neigh)) {
continue;
} else {
pathMap.put(neigh, parent);
}
}
}
// The target was unreachable.
return -1;
}
private List<int[]> getNeighbours(int row, int col, int[][] grid) {
List<int[]> neighbours = new ArrayList<>();
for (int i = 0; i < directions.length; i++) {
int newRow = row + directions[i][0];
int newCol = col + directions[i][1];
if (newRow < 0 || newCol < 0 || newRow >= grid.length
|| newCol >= grid[0].length
|| grid[newRow][newCol] != 0) {
continue;
}
neighbours.add(new int[]{newRow, newCol});
}
return neighbours;
}
//GetNeighbors -Agent3
private List<int[]> getNeighboursAgent3(int row, int col, int[][] grid) {
List<int[]> neighbours = new ArrayList<>();
for (int i = 0; i < directionsAgent3.length; i++) {
int newRow = row + directionsAgent3[i][0];
int newCol = col + directionsAgent3[i][1];
if (newRow < 0 || newCol < 0 || newRow >= grid.length
|| newCol >= grid[0].length) {
continue;
}
neighbours.add(new int[]{newRow, newCol});
}
return neighbours;
}
// Get the best case estimate of how much further it is to the bottom-right cell.
private int estimate(int row, int col, int[][] grid) {
//Manhattan distance
int heuristic = Math.abs(row - (grid.length - 1)) + Math.abs(col - (grid[0].length - 1));
return heuristic;
}
private void intialBielfStateSetting(double[][] beliefState,int dim){
for(int i=0;i<beliefState.length;i++){
for(int j=0;j<beliefState[i].length;j++){
beliefState[i][j] = 1/(dim*dim);
}
}
}
//examine function
private int examine(int x1,int y1,int x2,int y2,double[][] beliefState,
int[][] trueGrid,int[][] dummyGrid,int currRow,
int currCol){
//At time t + 1 you attempt to enter (x, y) and find it is blocked?
// Px,y (t+1) = 0
// Pi,j(t+1)=Pi,j(t)/ i=0 to dim & j=0 to dim Pi,j(t) (Summation)
if(trueGrid[currRow][currCol]==1){
dummyGrid[currRow][currCol]=1;
beliefState[currRow][currCol]=0;
divide(beliefState,currRow,currCol);
return 1;
}
if(currRow!=x2 && currCol!=y2) {
//flat terrain
//At time t + 1 you examine cell (x, y) of terrain type flat, and fail to find the target?
//Px,y(t+1) = Px,y(t)* FN(Tr)
//Pi,j(t+1) = Pi,j(t)/i=0 to dim & j=0 to dim Pi,j(t) (Summation)
if (trueGrid[currRow][currCol] == 2) {
examineTerrainType(flatFNR, beliefState, currRow, currCol);
divide(beliefState, currRow, currCol);
return 0;
}
//hilly terrain
//At time t + 1 you examine cell (x, y) of terrain type hilly, and fail to find the target?
//Px,y(t+1) = Px,y(t)* FN(Tr)
//Pi,j(t+1) = Pi,j(t)/i=0 to dim & j=0 to dim Pi,j(t) (Summation)
if (trueGrid[currRow][currCol] == 3) {
examineTerrainType(hillyFNR, beliefState, currRow, currCol);
divide(beliefState, currRow, currCol);
return 0;
}
//forest Terrain
//At time t + 1 you examine cell (x, y) of terrain type forest, and fail to find the target?
//Px,y(t+1) = Px,y(t)* FN(Tr) /i=0 dimj=0 dimPi,j(t)
//Pi,j(t+1) = Pi,j(t)/i=0 to dim & j=0 to dim Pi,j(t) (Summation)
if (trueGrid[currRow][currCol] == 4) {
examineTerrainType(forestFNR, beliefState, currRow, currCol);
divide(beliefState, currRow, currCol);
return 0;
}
}else{
//target is in cell
beliefState[currRow][currCol] = 1;
return -1;
}
return 0;
}
//Px,y(t+1) = Px,y(t)* FN(Tr)
private void examineTerrainType(double FNR,double[][] beliefState,int currRow,int currCol){
double variable;
variable = (beliefState[currRow][currCol]*FNR);
beliefState[currRow][currCol]=variable;
}
//P(i,j)T/Summation
private void divide(double[][] beliefState,int currRow,int currCol){
double variable;
double summation = summation(beliefState,currRow,currCol);
for (int rn = 0; rn < beliefState.length; rn++) {
for (int cl = 0; cl < beliefState[rn].length; cl++) {
variable = beliefState[rn][cl]/summation;
beliefState[rn][cl]=variable;
}
}
}
//summation
private double summation(double[][] beliefState,int currRow,int currCol){
double sum =0;
for (int rn = 0; rn < beliefState.length; rn++) {
for (int cl = 0; cl < beliefState[rn].length; cl++) {
sum += beliefState[rn][cl];
}
}
return sum;
}
//update inference knowledge based on any change in KB
private void updateInferenceKB(Candidate candidate,int value){
for(int i=0;i< inferenceKnowledge.size();i++){
Equation eq = inferenceKnowledge.get(i);
for(int j=0;j<eq.candidateList.size();j++){
Candidate candidate1 = eq.candidateList.get(j);
if(candidate1.row== candidate.row && candidate.col== candidate1.col){
if(value == 0){
eq.candidateList.remove(j);
if(eq.candidateList.size()==0){
inferenceKnowledge.remove(eq);
}
}else{
eq.candidateList.remove(j);
eq.c = eq.c-1;
if(eq.candidateList.size()==0){
inferenceKnowledge.remove(eq);
}
}
}
}
}
}
//infer values from updateInferenceKB
private int inferFromInferenceKB(int[][] newGrid,boolean[][] visited){
int counter = 0;
for(int i=0;i<inferenceKnowledge.size();i++){
Equation eq = inferenceKnowledge.get(i);
// if((eq.c==2 && eq.candidateList.size()==2) ||(eq.c==1 && eq.candidateList.size()==1) ){
if(eq.c==eq.candidateList.size()){
for(int j=0;j<eq.candidateList.size();j++){
Candidate candidate = eq.candidateList.get(j);
newGrid[candidate.row][candidate.col] = 1;
visited[candidate.row][candidate.col]=true;
}
inferenceKnowledge.remove(eq);
counter++;
}
if(eq.c==0){
for(int j=0;j<eq.candidateList.size();j++){
Candidate candidate = eq.candidateList.get(j);
newGrid[candidate.row][candidate.col] = 0;
visited[candidate.row][candidate.col]=true;
}
inferenceKnowledge.remove(eq);
}
}
return counter;
}
private boolean trackPathHasAnyBlock(int[][] newGrid,int i,boolean[][] visited) {
boolean hasBlock = false;
for (int j = 0; j < track.size(); j++) {
Candidate can = track.get(j);
if (newGrid[can.row][can.col] == 1) {
hasBlock = true;
Candidate newSource;
Candidate can1 = track.get(j-1);
if(visited[can1.row][can1.col]==true){
newSource = track.get(j-1);
}else{
newSource = track.get(i);
}
int ans1 = RFAstar(newGrid, newSource.row, newSource.col, 0);
Collections.reverse(track);
//counter = ans1+counter;
// i=-1;
}
}
return hasBlock;
}
public static int cellsProcessed = 0;
// public int cellsProcessed() {
// return cellsProcessed++;
// }
public static int cellsProcessedRFA = 0;
// public int cellsProcessedRFA() {
// return cellsProcessedRFA++;
// }
public int RFAstar(int[][] newGrid, int row, int col, int distanceSoFar) {
int counter = 0;
// Firstly, we need to check that the start and target cells are open.
if (newGrid[0][0] != 0 || newGrid[newGrid.length - 1][newGrid[0].length - 1] != 0) {
return -1;
}
// Set up the A* search.
Queue<Candidate> pq = new PriorityQueue<>((a, b) -> a.totalEstimate - b.totalEstimate);
pq.add(new Candidate(row, col, distanceSoFar, estimate(0, 0, newGrid)));
boolean[][] visited = new boolean[newGrid.length][newGrid[0].length];
Map<Candidate, Candidate> pathMap = new HashMap<Candidate, Candidate>();
// Carry out the A* search.
while (!pq.isEmpty()) {
Candidate best = pq.remove();
noOfCellsProcessed++;
counter++;
// Is this for the target cell?
if (best.row == newGrid.length - 1 && best.col == newGrid[0].length - 1) {
if (track.isEmpty()) {
Candidate result = pathMap.get(best);
System.out.print(best.row+","+best.col);
System.out.print("->");
track.add(best);
Candidate source = new Candidate(row,col,distanceSoFar,estimate(row,col,newGrid));
// for (int i = 0; i < best.distanceSoFar - 1; i++) {
while (result.row!=source.row || result.col!=source.col){
System.out.print(result.row + "," + result.col);
track.add(result);
System.out.print("->");
result = pathMap.get(result);
}
System.out.println(source.row+","+ source.col);
track.add(source);
} else {
track.clear();
Candidate result = pathMap.get(best);
System.out.print(best.row+","+ best.col);
System.out.print("->");
track.add(best);
Candidate source = new Candidate(row,col,distanceSoFar,estimate(row,col,newGrid));
// for (int i = 0; i <7; i++) {
while(result.row!=source.row || result.col!=source.col){
System.out.print(result.row + "," + result.col);
track.add(result);
System.out.print("->");
result = pathMap.get(result);
}
track.add(source);
//System.out.println(source.row+","+ source.col);
System.out.println();
}
return best.distanceSoFar;
//return counter;
}
// Have we already found the best path to this cell?
if (visited[best.row][best.col]) {
continue;
}
visited[best.row][best.col] = true;
// pathQueue.add({new int[]{best.row,best.col},{});
for (int[] neighbour : getNeighbours(best.row, best.col, newGrid)) {
int neighbourRow = neighbour[0];
int neighbourCol = neighbour[1];
// This check isn't necessary for correctness, but it greatly
// improves performance.
if (visited[neighbourRow][neighbourCol]) {
continue;
}
// Otherwise, we need to create a Candidate object for the option
// of going to neighbor through the current cell.
int newDistance = best.distanceSoFar + 1;
int totalEstimate = newDistance + estimate(neighbourRow, neighbourCol, newGrid);
Candidate candidate =
new Candidate(neighbourRow, neighbourCol, newDistance, totalEstimate);
pq.add(candidate);
if (!pathMap.containsKey(candidate)) {
pathMap.put(candidate, best);
}
// pathMap.put(candidate, best);
}
}
// The target was unreachable.
return -1;
}
//AgentTwo - blindfolded
public int AgentTwo(int[][] newGrid, int row, int col, int distanceSoFar, int[][] grid) {
int noOfBumps = 0;
int totalCounter=0;
int trajectoryLen = 0;
boolean[][] visited = new boolean[newGrid.length][newGrid[0].length];
int ans = RFAstar(newGrid, row, col, distanceSoFar);
totalCounter = ans+totalCounter;
Collections.reverse(track);
for (int i = 0; i < track.size(); i++) {
Candidate canValue = track.get(i);
if (newGrid[canValue.row][canValue.col] != grid[canValue.row][canValue.col]) {
newGrid[canValue.row][canValue.col] = grid[canValue.row][canValue.col];
visited[canValue.row][canValue.col] = true;
trajectoryLen++;
trajectoryLen++;
Candidate newSource = track.get(i - 1);
noOfBumps++;
int ans1 = RFAstar(newGrid, newSource.row, newSource.col, 0);
totalCounter = ans1+totalCounter;
Collections.reverse(track);
i = -1;
}else{
trajectoryLen++;
visited[canValue.row][canValue.col]=true;
}
}
// int ansLength = RFAstar(newGrid, 0, 0, 1);
// return trajectoryLen;
//float result = (float) trajectoryLen/(float)ansLength;
//return ansLength;
//return totalCounter;
// return -1;
for (int i = 0; i<newGrid.length; i++) {
for (int j = 0; j < newGrid[i].length; j++) {
if(visited[i][j]!=true){
newGrid[i][j]=1;
}
}
}
RFAstar(newGrid, 0, 0, 1);
int ansLen = track.size();
return ansLen;
}
//AgentOne - can see its four neighbors
public int AgentOne(int[][] newGrid, int row, int col, int distanceSoFar, int[][] grid) {
int noOfBumps = 0;
int totalCounter = 0;
int trajectoryLen = 0;
int ans = RFAstar(newGrid, row, col, distanceSoFar);
totalCounter +=ans;
Collections.reverse(track);
boolean[][] visited = new boolean[newGrid.length][newGrid[0].length];
boolean[][] visited1 = new boolean[newGrid.length][newGrid[0].length];
//int ans=0;
for (int i = 0; i < track.size(); i++) {
Candidate canValue = track.get(i);
for (int[] neighbour : getNeighbours(canValue.row, canValue.col, newGrid)){
int neighbourRow = neighbour[0];
int neighbourCol = neighbour[1];
if(newGrid[neighbourRow][neighbourCol]!=grid[neighbourRow][neighbourCol]){
newGrid[neighbourRow][neighbourCol]=grid[neighbourRow][neighbourCol];
visited[neighbourRow][neighbourCol]=true;
visited1[neighbourRow][neighbourCol]=true;
}
}
if (newGrid[canValue.row][canValue.col] != grid[canValue.row][canValue.col] || visited[canValue.row][canValue.col]==true) {
// newGrid[canValue.row][canValue.col] = grid[canValue.row][canValue.col];
visited1[canValue.row][canValue.col]=true;
Candidate newSource = track.get(i - 1);
trajectoryLen++;
trajectoryLen++;
noOfBumps++;
int ans1 = RFAstar(newGrid, newSource.row, newSource.col, 1);
totalCounter+=ans1;
Collections.reverse(track);
i = -1;
}else{
trajectoryLen++;
visited1[canValue.row][canValue.col]=true;
}
}
// int ansLength = RFAstar(newGrid, 0, 0, 1);
// System.out.println("TracjectorLen="+trajectoryLen);
// System.out.println("SPL"+ansLength);
// float result = (float) trajectoryLen/(float)ansLength;
// return ansLength;
// return trajectoryLen;
//return result;
// return -1;
for (int i = 0; i<newGrid.length; i++) {
for (int j = 0; j < newGrid[i].length; j++) {
if(visited1[i][j]!=true){
newGrid[i][j]=1;
}
}
}
RFAstar(newGrid, 0, 0, 1);
int ansLen = track.size();
return ansLen;
}
//AgentThree - Eg inference Agent
public int AgentThree(int[][] newGrid, int row, int col, int distanceSoFar, int[][] grid){
int trajectoryLen = 0;
int noOfBumps=0;
int counter = 0;
int ans = RFAstar(newGrid, row, col, distanceSoFar);
counter = ans+counter;
int c;
int b;
int n;
int e;
int h;
Collections.reverse(track);
boolean[][] visited = new boolean[newGrid.length][newGrid[0].length];
for (int i = 0; i < track.size(); i++) {
c=0;
b=0;
e=0;
h=0;
Candidate canValue = track.get(i);
//visited[canValue.row][canValue.col]=true;
if (newGrid[canValue.row][canValue.col] != grid[canValue.row][canValue.col] && i!=-1) {
newGrid[canValue.row][canValue.col] = grid[canValue.row][canValue.col];
trajectoryLen++;
trajectoryLen++;
visited[canValue.row][canValue.col]=true;
Candidate newSource = track.get(i - 1);
noOfBumps++;
int ans1 = RFAstar(newGrid, newSource.row, newSource.col, 0);
counter = ans1+counter;
Collections.reverse(track);
i = -1;
}else if(i!=-1) {
trajectoryLen++;
if(i<track.size()-1)
{
Solution1.Candidate nextVal=track.get(i + 1);
appendDirection(canValue.row,canValue.col,nextVal.row,nextVal.col);
}
appendLocAgent(canValue.row, canValue.col);
appendFile(newGrid); //ML
visited[canValue.row][canValue.col] = true;
n = getNeighboursAgent3(canValue.row, canValue.col, newGrid).size();
h = n;
for (int[] neighbour : getNeighboursAgent3(canValue.row, canValue.col, newGrid)) {
int neighbourRow = neighbour[0];
int neighbourCol = neighbour[1];
if (newGrid[neighbourRow][neighbourCol] != grid[neighbourRow][neighbourCol] || newGrid[neighbourRow][neighbourCol] == 1) {
c++;
}
if (newGrid[neighbourRow][neighbourCol] == 1) {
b++;
h--;
}
if (newGrid[neighbourRow][neighbourCol] == 0 && visited[neighbourRow][neighbourCol] == true) {
e++;
h--;
}
}
//Cx=Bx: all remaining hidden neighbors of x are empty.
if (c == b) {
e = h;
h = 0;
for (int[] neighbour : getNeighboursAgent3(canValue.row, canValue.col, newGrid)) {
int neighbourRow = neighbour[0];
int neighbourCol = neighbour[1];
if (newGrid[neighbourRow][neighbourCol] == 0 && visited[neighbourRow][neighbourCol] == false) {
visited[neighbourRow][neighbourCol] = true;
}
}
}
//Nx−Cx=Ex: all remaining hidden neighbors of x are blocked.
if (n - c == e && h != 0) {
b = h;
h = 0;
for (int[] neighbour : getNeighboursAgent3(canValue.row, canValue.col, newGrid)) {
int neighbourRow = neighbour[0];
int neighbourCol = neighbour[1];
if (newGrid[neighbourRow][neighbourCol] == 0 && visited[neighbourRow][neighbourCol] == false) {
visited[neighbourRow][neighbourCol] = true;
newGrid[neighbourRow][neighbourCol] = 1;
}
}
for (int j = 0; j < track.size(); j++) {
Candidate can = track.get(j);
if (newGrid[can.row][can.col] == 1) {
Candidate newSource = track.get(i);
//Call here to RFA
int ans1 = RFAstar(newGrid, newSource.row, newSource.col, 0);
Collections.reverse(track);
counter = ans1 + counter;
i = -1;
}
}
}
//Hx= 0: nothing remains to be inferred about cellx.
}
}