/// <summary>
/// <para>
/// Dynamic-based Prediction Algorithm is the last high abstraction prediction algorithm for this model.
/// Unlike the previous prediction algorithms, this method focuses more on details of the combat. Instead
/// of looking at the initial constant damage, or a noised damage, it randomizes between whether the unit
/// will attack or use its skill. Because of this, it is now able to consider that damage is not constant,
/// damage and skill are not executed at once, some skills have cooldown and duration, and some skills does not
/// deal damage, but something that affects other properties of unit. While it is still bounded by time
/// like <see cref="StaticBasedPrediction(TargetPolicy)"/>, it does not need to stop when the time ran out, but
/// it stops when there are no more units that can attack each other. Lastly, like the other mentioned algorithms,
/// this considers that some units cannot target other units, and that the dealt damage is reduced by the armor.
/// </para>
/// <para>
/// This method returns a string of survived units from the winning army, but the cost worth of doing battle
/// is always relative to the player. As such, if the opposing army won, the cost worth returned is negative since
/// it represents as a loss.
/// </para>
/// </summary>
/// <remarks>
/// <para>
/// Unlike other algorithms, it does not look at the army as a whole, but on a per unit basis.
/// The probable running time is around O(n^3)
/// </para>
/// <para>
/// In summary, it only take part the time-related aspect of Static-based prediction, and proceeds
/// to let the army fight each other in a detailed manner. By detailed manner, a unit can attack
/// normally its target, or use a skill if it is researched during that time. It considers and
/// does not consider the following:
/// Considers:
/// <list type="bullet">
/// <item>Current/Decreasing Health and Energy</item>
/// <item>True Current Damage (Damage is applied with Armor)</item>
/// <item>Restrictions in targeting unit</item>
/// <item>Skills that deals damage, boost damage, and other that affects Health and Energy</item>
/// <item>Time-related properties to battle</item>
/// <item>Skills with cooldown and duration</item>
/// </list>
/// Does not Consider:
/// <list type="bullet">
/// <item>AoE Damage / Chaining Damage</item>
/// <item>Transforming Units</item>
/// <item>Damage with bonus to target type</item>
/// </list>
/// </para>
/// </remarks>
/// <param name="target_policy"></param>
/// <returns></returns>
public Tuple <string, CostWorth> DynamicBasedPrediction(TargetPolicy target_policy)
{
Tuple <string, CostWorth> battle_result = null;
var random = Services.ModelRepositoryService.ModelService.GetModelService().RandomEngine;
try
{
//Create a copy of units
var owned_units = _owned_units.GetDeepCopy();
var enemy_units = _enemy_units.GetDeepCopy();
//Set the targets for each army
//This will get the true damage of unit, and a target to be attacked
switch (target_policy)
{
case TargetPolicy.Random:
RandomBasedTargetPolicy(ref owned_units, enemy_units);
RandomBasedTargetPolicy(ref enemy_units, owned_units);
break;
case TargetPolicy.Priority:
PriorityBasedTargetPolicy(ref owned_units, enemy_units);
PriorityBasedTargetPolicy(ref enemy_units, owned_units);
break;
case TargetPolicy.Resource:
ResourceBasedTargetPolicy(ref owned_units, enemy_units);
ResourceBasedTargetPolicy(ref enemy_units, owned_units);
break;
}
//Compute the battle output, then let both armies attack each other
var combat_result = new DynamicCombatResult(owned_units, enemy_units);
while (DynamicCombatResult.IsCombatContinuable(owned_units, enemy_units))
{
var combat_time = DynamicCombatResult.GetCombatTime(combat_result);
for (int time_to_kill = 0; time_to_kill < combat_time; time_to_kill++)
{
var ability_probability = random.NextDouble();
owned_units.DealDamageToTarget(ability_probability);
enemy_units.DealDamageToTarget(ability_probability);
}
//Get the surviving units of both army
owned_units = owned_units.Where(unit => !unit.IsDefeated).ToArmy();
enemy_units = enemy_units.Where(unit => !unit.IsDefeated).ToArmy();
//Check if the battle can be continued
if (!DynamicCombatResult.IsCombatContinuable(owned_units, enemy_units) || (combat_time == 0))
{
break;
}
//Re-set the targets for each army to prevent
//a dead-lock and null target, and to get the true damage a target to attack
switch (target_policy)
{
case TargetPolicy.Random:
RandomBasedTargetPolicy(ref owned_units, enemy_units);
RandomBasedTargetPolicy(ref enemy_units, owned_units);
break;
case TargetPolicy.Priority:
PriorityBasedTargetPolicy(ref owned_units, enemy_units);
PriorityBasedTargetPolicy(ref enemy_units, owned_units);
break;
case TargetPolicy.Resource:
ResourceBasedTargetPolicy(ref owned_units, enemy_units);
ResourceBasedTargetPolicy(ref enemy_units, owned_units);
break;
}
//Recompute the battle output, then let them fight again
combat_result = new DynamicCombatResult(owned_units, enemy_units);
}
//Get the surviving units of the winner
var survived_owned_units = owned_units.Where(unit => !unit.IsDefeated).ToArmy();
var survived_enemy_units = enemy_units.Where(unit => !unit.IsDefeated).ToArmy();
//Owned Army Loss
if (survived_owned_units.Count() < survived_enemy_units.Count())
{
battle_result = new Tuple <string, CostWorth>(survived_enemy_units.ToString(), !survived_enemy_units.GetValueOfArmy());
}
else if (survived_owned_units.Count() > survived_enemy_units.Count())
{
battle_result = new Tuple <string, CostWorth>(survived_owned_units.ToString(), survived_owned_units.GetValueOfArmy());
}
else
{
battle_result = new Tuple <string, CostWorth>(@"""""", default(CostWorth));
}
}
catch (ArgumentNullException ex)
{
Console.WriteLine($@"DynamicBasedPrediction() [{target_policy.ToString()}] -> {ex.Message}");
throw new Exception("");
}
catch (Exception ex)
{
Console.WriteLine($@"DynamicBasedPrediction() -> {ex.Message}");
battle_result = null;
}
return(battle_result);
}
/// <summary>
/// <para>
/// Lanchester-based Prediction Algorithm is the highest form of abstraction. It uses the
/// Lanchester formula to compute the combat winner, including the survivor in the winning army.
/// As a highest-abstraction prediction algorithm, it exchanges fine detail for faster computing. It
/// does not consider the case that Health and Energy decreases over time, and that some skills
/// have cooldown and duration. However, it considers that some units cannot target some
/// other units such as air units. It also consider skills that deals damage/boost damage but not on the finest
/// detail. Lastly, it also consider that the damage is reduced by the armor.
/// </para>
/// <para>
/// This method returns a string of survived units from the winning army, but the cost worth
/// of doing battle is always relative to the player. As such, if the opposing army won, the cost worth
/// returned is negative since it represents as a loss.
/// </para>
/// </summary>
/// <remarks>
/// <para>
/// While it is said to be the fastest out of the three algorithm, it is still dependent on the target policy.
/// The probably running time is O(4n^2 + 2n).
/// </para>
/// <para>
/// In summary, the Lanchester-based prediction considers and does not consider the following:
/// Considers:
/// <list type="bullet">
/// <item>True Current Damage (Current Damage applied with opposing Armor)</item>
/// <item>Restrictions in targeting unit (Some unit cannot target air unit, and vice versa)</item>
/// <item>Current Health</item>
/// <item>Skills that deals damage and gives boost to the Current damage</item>
/// </list>
/// Does not Consider:
/// <list type="bullet">
/// <item>Decreasing Health</item>
/// <item>Decreasing Energy</item>
/// <item>Skills with Cooldown and Duration</item>
/// <item>Skills that affects Health/Energy</item>
/// <item>AoE Damage / Chaining Damage</item>
/// <item>Transforming Units</item>
/// <item>Time and its related properties to battle</item>
/// <item>Damage with bonus to target type</item>
/// </list>
/// </para>
/// </remarks>
/// <param name="target_policy"></param>
/// <returns></returns>
public Tuple <string, CostWorth> LanchesterBasedPrediction(TargetPolicy target_policy)
{
Tuple <string, CostWorth> battle_result = null;
try
{
//Create a copy of the units
var owned_units = _owned_units.GetDeepCopy();
var enemy_units = _enemy_units.GetDeepCopy();
//Set the targets for each army
//This will get the true damage of unit, since Damage with Armor is applied
switch (target_policy)
{
case TargetPolicy.Random:
RandomBasedTargetPolicy(ref owned_units, enemy_units);
RandomBasedTargetPolicy(ref enemy_units, owned_units);
break;
case TargetPolicy.Priority:
PriorityBasedTargetPolicy(ref owned_units, enemy_units);
PriorityBasedTargetPolicy(ref enemy_units, owned_units);
break;
case TargetPolicy.Resource:
ResourceBasedTargetPolicy(ref owned_units, enemy_units);
ResourceBasedTargetPolicy(ref enemy_units, owned_units);
break;
}
//Compute the battle output
var combat_result = new LanchesterCombatResult(owned_units, enemy_units);
var combat_winner = LanchesterCombatResult.GetCombatWinner(combat_result);
var combat_survivor = LanchesterCombatResult.GetSurvivingUnits(combat_result, combat_winner);
//Get the surviving units of the winner
Army survived_units = default(Army);
switch (combat_winner)
{
case LanchesterCombatResult.CombatWinner.Owned_Army:
//Based on policy, pick which units will survive
switch (target_policy)
{
case TargetPolicy.Random:
survived_units = owned_units.RandomlyTake(combat_survivor);
break;
case TargetPolicy.Priority:
survived_units = owned_units.PriorityTake(combat_survivor);
break;
case TargetPolicy.Resource:
survived_units = owned_units.ResourceTake(combat_survivor);
break;
}
battle_result = new Tuple <string, CostWorth>(survived_units.ToString(), survived_units.GetValueOfArmy());
break;
case LanchesterCombatResult.CombatWinner.Draw:
battle_result = new Tuple <string, CostWorth>(@"""""", default(CostWorth));
break;
case LanchesterCombatResult.CombatWinner.Enemy_Army:
//Based on policy, pick which units will survive
switch (target_policy)
{
case TargetPolicy.Random:
survived_units = enemy_units.RandomlyTake(combat_survivor);
break;
case TargetPolicy.Priority:
survived_units = enemy_units.PriorityTake(combat_survivor);
break;
case TargetPolicy.Resource:
survived_units = enemy_units.ResourceTake(combat_survivor);
break;
}
battle_result = new Tuple <string, CostWorth>(survived_units.ToString(), !survived_units.GetValueOfArmy());
break;
}
}
catch (ArgumentNullException ex)
{
Console.WriteLine($@"LanchesterBasedPrediction() [{target_policy.ToString()}] -> {ex.Message}");
throw new Exception("");
}
catch (Exception ex)
{
Console.WriteLine($@"LanchesterBasedPrediction() -> {ex.Message}");
battle_result = null;
}
return(battle_result);
}
/// <summary>
/// <para>
/// Static-based Prediction Algorithm is an another high abstraction prediction algorithm.
/// Unlike the <see cref="LanchesterBasedPrediction(TargetPolicy)"/>, the computation focuses more
/// on time-related properties of combat. However, like the mentioned prediction algorithm, it still
/// exchanges fine detail for faster computing. Like the <see cref="LanchesterBasedPrediction(TargetPolicy)"/>,
/// It does not consider that the Energy decreases over time, and that some skills have cooldown and duration.
/// However, it does consider that the damage is not constant by adding noises to the damage such as
/// there are times it is the current damage, and sometimes it is more than the current damage. It also considers
/// that some units cannot target other units, and considers skills that deals damage/boost damage but not on the
/// finest detail. Lastly, it also consider that the dealt damage is reduced by the armor.
/// </para>
/// <para>
/// This method returns a string of survived units from the winning army, but the cost worth of doing
/// battle is always relative to the player. As such, if the opposing army won, the cost worth returned
/// is negative since it represents as a loss.
/// </para>
/// </summary>
/// <remarks>
/// <para>
/// While it still looks the army as a whole, it computes more parameters.
/// The probable running time is O(4n^2 + 32n)
/// </para>
/// <para>
/// In summary, it is almost like the Lanchester-based prediction where it looks at the army as
/// a whole, but also considers the time-related property of combat. Lastly, it also adds noise
/// to the damage being dealt to the target. It considers and does not consider the following:
/// Considers:
/// <list type="bullet">
/// <item>Current and Decreasing Health</item>
/// <item>True Current Damage (Current Damage applied with opposing Armor)</item>
/// <item>Restrictions in targeting unit</item>
/// <item>Skills that deals damage and gives boost to the damage of unit</item>
/// <item>Time-related properties to battle</item>
/// </list>
/// Does not Consider:
/// <list type="bullet">
/// <item>Decreasing Energy</item>
/// <item>Skills with Cooldown and Duration</item>
/// <item>Skills that affects Health/Energy</item>
/// <item>AoE Damage / Chaining Damage</item>
/// <item>Transforming Units</item>
/// <item>Damage with bonus to target type</item>
/// </list>
/// </para>
/// </remarks>
/// <param name="target_policy"></param>
/// <returns></returns>
public Tuple <string, CostWorth> StaticBasedPrediction(TargetPolicy target_policy)
{
Tuple <string, CostWorth> battle_result = null;
try
{
//Create a copy of the units
var owned_units = _owned_units.GetDeepCopy();
var enemy_units = _enemy_units.GetDeepCopy();
//Set the targets for each army
//This will get the true damage of unit, and a target to be attacked
switch (target_policy)
{
case TargetPolicy.Random:
RandomBasedTargetPolicy(ref owned_units, enemy_units);
RandomBasedTargetPolicy(ref enemy_units, owned_units);
break;
case TargetPolicy.Priority:
PriorityBasedTargetPolicy(ref owned_units, enemy_units);
PriorityBasedTargetPolicy(ref enemy_units, owned_units);
break;
case TargetPolicy.Resource:
ResourceBasedTargetPolicy(ref owned_units, enemy_units);
ResourceBasedTargetPolicy(ref enemy_units, owned_units);
break;
}
//Compute the battle output
var combat_result = new StaticCombatResult(owned_units, enemy_units);
var combat_time = StaticCombatResult.GetCombatTime(combat_result);
//Let both armies attack each other
owned_units.DealDamageToTarget(combat_time);
enemy_units.DealDamageToTarget(combat_time);
//Get the surviving units of the winner
var survived_owned_units = owned_units.Where(unit => !unit.IsDefeated).ToArmy();
var survived_enemy_units = enemy_units.Where(unit => !unit.IsDefeated).ToArmy();
//Owned Army Loss
if (survived_owned_units.Count() < survived_enemy_units.Count())
{
battle_result = new Tuple <string, CostWorth>(survived_enemy_units.ToString(), !survived_enemy_units.GetValueOfArmy());
}
//Owned Army Won
else if (survived_owned_units.Count() > survived_enemy_units.Count())
{
battle_result = new Tuple <string, CostWorth>(survived_owned_units.ToString(), survived_owned_units.GetValueOfArmy());
}
//Draw
else
{
battle_result = new Tuple <string, CostWorth>(@"""""", default(CostWorth));
}
}
catch (ArgumentNullException ex)
{
Console.WriteLine($@"StaticBasedPrediction() [{target_policy.ToString()}] -> {ex.Message}");
throw new Exception("");
}
catch (Exception ex)
{
Console.WriteLine($@"StaticBasedPrediction() -> {ex.Message}");
battle_result = null;
}
return(battle_result);
}
