public List <string> Aalgorithm(State state)
{
path.Clear();
came_from.Clear();
cost_so_far.Clear();
steps_so_far.Clear();
came_from_name.Clear();
initial_state = new State(state.position);
came_from[initial_state] = null;
came_from_name[initial_state] = "";
cost_so_far[initial_state] = 0.0f;
steps_so_far[initial_state] = 0;
path.Add("");
Info initial = new Info("", initial_state);
frontier.Enqueue(initial, 0);
count = 0;
while (frontier.Count != 0)
{
count++;
Info current_info = frontier.Dequeue();
state_name = current_info.action_name;
current_state = current_info.info_state;
current_pos = current_state.position;
GameObject[] bulletArray = GameObject.FindGameObjectsWithTag("EnemyBullet");
//Debug.Log ("Prior: " + bulletArray.Length);
List <GameObject> nearbyThreats = keepThreatsIntoList(current_pos, bulletArray);
//Debug.Log (eneList.Count);
//find nearest enemy within X location amongst bulletArray
GameObject[] enemyArray = GameObject.FindGameObjectsWithTag("Enemy");
if (enemyArray.Length == 0 && bulletArray.Length == 0)
{
path.Add("doNothing");
return(path);
}
float nearestEnemyX = findNearestEnemyX(enemyArray, current_pos);
//Debug.Log ("nearest: " + nearestEnemyX);
// 2 exit conditions; one checking for an enemy, another for no enemy in which case, remain in place dodging
//Vector2 ene_pos = Enemy.transform.position;
Vector2 dodge_pos = transform.position;
if (steps_so_far[current_state] == 3)
{
path = creatingPath(current_state, state_name);
return(path);
}
//print(current_state)
//Debug.Log("good_moves.Count: "+good_moves.Count);
for (int i = 0; i < good_moves.Count; i++)
{
//NEXT = Name, State effected by action, and Time cost
action_name = good_moves[i];
//Debug.Log (action_name);
State newState = new State(current_state.position);
AIBehavior.apply_move(action_name, newState);
// Simulate state here
//Debug.Log (newState.position);
new_cost = cost_so_far[current_state] + 0.1f;
float testValue;
if ((cost_so_far.TryGetValue(newState, out testValue) == false) || new_cost < cost_so_far [newState])
{
cost_so_far[newState] = new_cost;
priority = new_cost + heuristic(current_state, newState, nearestEnemyX, nearbyThreats, steps_so_far[current_state], action_name);
Info newer_info = new Info(action_name, newState);
steps_so_far[newState] = steps_so_far[current_state] + 1;
came_from[newState] = current_state;
came_from_name[newState] = action_name;
frontier.Enqueue(newer_info, priority);
}
}
}
frontier.Clear();
return(path);
}
