private void bt_q3_Click(object sender, EventArgs e)
{
//Invoked for Q3
Constants.newHValues();
solutionPath = new Queue <int[]>();
solutionPath.Enqueue(new int[] { Constants.start.X, Constants.start.Y }); //adding the start state to the solutionPath
Constants.Corners.Add(new int[] { Constants.finish.X, Constants.finish.Y });
LRTA lrta = new LRTA();
int HVal = 0;
int[] currentPos = new int[] { Constants.start.X, Constants.start.Y }; //setting the start state as the current positiion
bool solutionFound = false;
//navigating to first corner
List <int[]> percept = lrta.getPercept(world.array, currentPos[0], currentPos[1]); //getting the percept
SimplePriorityQueue <int[]> priorityQueue = new SimplePriorityQueue <int[]>();
//getting the heuristic values for the percept
foreach (int[] x in percept)
{
HVal = lrta.calculateHeuristicValue(x[0], x[1]);
try
{
Constants.Hvalues.Add(x, HVal);
}
catch { }
//Goal Test
if (HVal == 0)
{
solutionPath.Enqueue(x);
solutionFound = true;
break;
}
priorityQueue.Enqueue(x, Constants.Hvalues[x]);
}
int[] lastPos = new int[] { Constants.start.X, Constants.start.Y };
Constants.Hvalues.Add(lastPos, lrta.calculateHeuristicValue(lastPos[0], lastPos[1]));
if (!solutionFound)
{
currentPos = priorityQueue.Dequeue();
solutionPath.Enqueue(currentPos);
}
//now we have last and current so find next and check if it is in a local minima
while (!solutionFound)
{
//finding the percept for the current state
percept = lrta.getPercept(world.array, currentPos[0], currentPos[1]);
priorityQueue = new SimplePriorityQueue <int[]>();
//finding all the heuristic values
foreach (int[] x in percept)
{
if (x != currentPos)
{
HVal = lrta.calculateHeuristicValue(x[0], x[1]);
try
{
Constants.Hvalues.Add(x, HVal);
}
catch { }
//Goal Test
if (HVal == 0)
{
solutionPath.Enqueue(x);
solutionFound = true;
break;
}
priorityQueue.Enqueue(x, Constants.Hvalues[x]);
}
}
//Implementing the 30-70% probability
if (!solutionFound)
{
Random rand = new Random();
int probability = rand.Next(10); //random number for probability
if (probability > 2)
{
//Take the correct path and implement it as in Q1
int[] nextPos = priorityQueue.Dequeue();
if (Constants.Hvalues[currentPos] <= Constants.Hvalues[nextPos] && Constants.Hvalues[currentPos] <= Constants.Hvalues[lastPos])
{
//stuck in local minima
Constants.Hvalues[currentPos] = (Constants.Hvalues[nextPos] < Constants.Hvalues[lastPos] ? Constants.Hvalues[nextPos] : Constants.Hvalues[lastPos]) + 1;
int[] tempPos = lastPos;
lastPos = currentPos;
currentPos = Constants.Hvalues[nextPos] < Constants.Hvalues[lastPos] ? nextPos : tempPos; //moving it to the next best value
}
else
{
lastPos = currentPos;
currentPos = nextPos;
}
solutionPath.Enqueue(nextPos);
}
else
{
//Robot went to a wrong position
//Implementing a Stack to backtrack to the original position
Stack <int[]> returnToOriginalPlan = new Stack <int[]>();
int[] actualNextPos = currentPos;
int[] randomNextPos = new int[] { 0, 0 };
returnToOriginalPlan.Push(currentPos); //Pushing the current position to the stack
do
{
int times = rand.Next(priorityQueue.Count);
for (int i = 0; i < times; i++) //selecting a random position to go
{
randomNextPos = priorityQueue.Dequeue();
}
percept = lrta.getPercept(world.array, randomNextPos[0], randomNextPos[1]); //getting percept of that position to try to get back
priorityQueue.Clear();
//getting heuristic values
foreach (int[] x in percept)
{
if (x != randomNextPos)
{
int Hval = calcHeurToGetBackToOriginalPlan(x, actualNextPos);
priorityQueue.Enqueue(x, Hval);
}
}
probability = rand.Next(10); //again checking the probability for going back
if (probability > 2)
{
//correct path pop from stack and update the percept
int[] a = returnToOriginalPlan.Pop();
solutionPath.Enqueue(a);
//priority queue update
percept = lrta.getPercept(world.array, a[0], a[1]); //getting percept of that position to try to get back
priorityQueue.Clear();
foreach (int[] x in percept)
{
if (x != a)
{
int Hval = calcHeurToGetBackToOriginalPlan(x, actualNextPos);
priorityQueue.Enqueue(x, Hval);
}
}
}
else
{
//wrong path go to the new wrong positino and pust the current position onto the stack
solutionPath.Enqueue(randomNextPos); //going to that position
returnToOriginalPlan.Push(randomNextPos);
actualNextPos = randomNextPos;
}
} while (returnToOriginalPlan.Count != 0);
}
}
}
lbl_perf.Text = "Performance : " + (1001 - solutionPath.Count);
pbCamvas.Invalidate();
}
private void bt_q4_2_Click(object sender, EventArgs e)
{
//Implements Q4
Constants.newHValues();
solutionPath = new Queue <int[]>();
solutionPath.Enqueue(new int[] { Constants.start.X, Constants.start.Y });
Constants.Corners.Add(new int[] { Constants.finish.X, Constants.finish.Y });
LRTA lrta = new LRTA();
int HVal = 0;
int[] currentPos = new int[] { Constants.start.X, Constants.start.Y };
bool solutionFound = false;
//navigating to first corner
List <int[]> percept = lrta.getPercept(world.array, currentPos[0], currentPos[1]);
//getting the percept and calculating the heuristic values for all tha points in the percept
SimplePriorityQueue <int[]> priorityQueue = new SimplePriorityQueue <int[]>();
foreach (int[] x in percept)
{
HVal = lrta.calculateHeuristicValue(x[0], x[1]);
try
{
Constants.Hvalues.Add(x, HVal);
}
catch { }
//goal test
if (HVal == 0)
{
solutionPath.Enqueue(x);
solutionFound = true;
break;
}
priorityQueue.Enqueue(x, Constants.Hvalues[x]); //pushing the heuristic values in the queue
}
int[] lastPos = new int[] { Constants.start.X, Constants.start.Y };
Constants.Hvalues.Add(lastPos, lrta.calculateHeuristicValue(lastPos[0], lastPos[1]));
if (!solutionFound)
{
//adding the current position in the output
currentPos = priorityQueue.Dequeue();
solutionPath.Enqueue(currentPos);
}
//now we have last and current so find next and check if it is in a ditch
while (!solutionFound)
{
//getting the percept
percept = lrta.getPercept(world.array, currentPos[0], currentPos[1]);
priorityQueue.Clear();
//calculating heuristic values
foreach (int[] x in percept)
{
if (x != currentPos)
{
HVal = lrta.calculateHeuristicValue(x[0], x[1]);
try
{
Constants.Hvalues.Add(x, HVal);
}
catch { }
//goal test
if (HVal == 0)
{
solutionPath.Enqueue(x);
solutionFound = true;
break;
}
priorityQueue.Enqueue(x, Constants.Hvalues[x]);
}
}
if (!solutionFound)
{
//implementing the probability 30-70%
Random rand = new Random();
int probability = rand.Next(10);
if (probability > 2)
{
//Take the correct path as in Q1
int[] nextPos = priorityQueue.Dequeue();
if (Constants.Hvalues[currentPos] <= Constants.Hvalues[nextPos] && Constants.Hvalues[currentPos] <= Constants.Hvalues[lastPos])
{
//stuck in local minima
Constants.Hvalues[currentPos] = (Constants.Hvalues[nextPos] < Constants.Hvalues[lastPos] ? Constants.Hvalues[nextPos] : Constants.Hvalues[lastPos]) + 1;
int[] tempPos = lastPos;
lastPos = currentPos;
currentPos = Constants.Hvalues[nextPos] < Constants.Hvalues[lastPos] ? nextPos : tempPos; //moving it to the next best value
}
else
{
//not in local minima
lastPos = currentPos;
currentPos = nextPos;
}
solutionPath.Enqueue(nextPos);
}
else
{
//wrong path- find a new path to the goal from this new position
lastPos = currentPos;
int i = rand.Next(priorityQueue.Count);
for (int k = 0; k < i; k++)
{
priorityQueue.Dequeue();
}
currentPos = priorityQueue.Dequeue();
solutionPath.Enqueue(currentPos);
}
}
}
lbl_perf.Text = "Performance : " + (1001 - solutionPath.Count);
pbCamvas.Invalidate();
}
private void LRTAFindPath()
{
//defining the variables
Constants.newHValues();
solutionPath = new Queue <int[]>();
solutionPath.Enqueue(new int[] { Constants.start.X, Constants.start.Y }); //adding start to solution path
Constants.Corners.Add(new int[] { Constants.finish.X, Constants.finish.Y }); //adding finish state as one of the corners
LRTA lrta = new LRTA();
int HVal = 0;
int[] currentPos = new int[] { Constants.start.X, Constants.start.Y };
bool solutionFound = false;
//navigating to first corner
List <int[]> percept = lrta.getPercept(world.array, currentPos[0], currentPos[1]);
SimplePriorityQueue <int[]> priorityQueue = new SimplePriorityQueue <int[]>();
//finding the Heuristic value for all the corners
foreach (int[] x in percept)
{
HVal = lrta.calculateHeuristicValue(x[0], x[1]);
try
{
Constants.Hvalues.Add(x, HVal);
}
catch { }
//Goal Test
if (HVal == 0)
{
solutionPath.Enqueue(x);
solutionFound = true;
break;
}
priorityQueue.Enqueue(x, Constants.Hvalues[x]);
}
int[] lastPos = new int[] { Constants.start.X, Constants.start.Y };
Constants.Hvalues.Add(lastPos, lrta.calculateHeuristicValue(lastPos[0], lastPos[1]));
//Appending the best node to the priority Queue
if (!solutionFound)
{
currentPos = priorityQueue.Dequeue();
solutionPath.Enqueue(currentPos);
}
//now we have last and current so find next and check if it is in a local minima
while (!solutionFound)
{
//Getting the percept for current pos
percept = lrta.getPercept(world.array, currentPos[0], currentPos[1]);
priorityQueue = new SimplePriorityQueue <int[]>();
foreach (int[] x in percept)
{
//finding Heuristic values for the current percept
if (x != currentPos)
{
HVal = lrta.calculateHeuristicValue(x[0], x[1]);
try
{
Constants.Hvalues.Add(x, HVal);
}
catch { }
//Goal Test
if (HVal == 0)
{
solutionPath.Enqueue(x);
solutionFound = true;
break;
}
priorityQueue.Enqueue(x, Constants.Hvalues[x]);
}
}
if (!solutionFound)
{
int[] nextPos = priorityQueue.Dequeue();
if (Constants.Hvalues[currentPos] <= Constants.Hvalues[nextPos] && Constants.Hvalues[currentPos] <= Constants.Hvalues[lastPos])
{
//stuck in local minima hence update the herustic value of that position
Constants.Hvalues[currentPos] = (Constants.Hvalues[nextPos] < Constants.Hvalues[lastPos] ?
Constants.Hvalues[nextPos] : Constants.Hvalues[lastPos]) + 1;
int[] tempPos = lastPos;
lastPos = currentPos;
//moving it to the next best value
currentPos = Constants.Hvalues[nextPos] < Constants.Hvalues[lastPos] ? nextPos : tempPos;
}
else
{
//best position found which is not a local minima
lastPos = currentPos;
currentPos = nextPos;
}
//adding the path to the solution
solutionPath.Enqueue(nextPos);
}
}
}
