:quality(85)/https://cdn.frvr.ai/6558c445ce1b5baa4d0a3cb3.png)
:quality(85)/https://cdn.frvr.ai/657b34ea862daa098b55d827.png)
#indiegame
#simulation
#artificialintelligence
#neuralnetworks
#strategy
Creation History
User prompt
Migrate to the latest version of LK
User prompt
Fix Bug: 'ReferenceError: foods is not defined' in this line: 'foods.push(food);' Line Number: 135
User prompt
Fix Bug: 'ReferenceError: foods is not defined' in this line: 'foods.push(food);' Line Number: 135
User prompt
Fix Bug: 'ReferenceError: foods is not defined' in this line: 'foods.push(food);' Line Number: 136
User prompt
Fix Bug: 'ReferenceError: foods is not defined' in this line: 'for (var i = 0; i < foods.length; i++) {' Line Number: 57
User prompt
Fix Bug: 'ReferenceError: NeuralNetwork is not defined' in this line: 'self.neuralNetwork = new NeuralNetwork();' Line Number: 5
User prompt
Fix Bug: 'TypeError: Game.ais is undefined' in this line: 'var index = Game.ais.indexOf(this);' Line Number: 37
User prompt
Fix Bug: 'TypeError: self.parent is undefined' in this line: 'var index = self.parent.ais.indexOf(this);' Line Number: 37
User prompt
Fix Bug: 'ReferenceError: ais is not defined' in this line: 'var index = ais.indexOf(this);' Line Number: 37
User prompt
End the game once all ais died
User prompt
Make the movement system more efficient once the ai detects food.
User prompt
Make it so the ai only needs 2 food to be able to breed.
User prompt
Make it so the food requiered to breed is 2
User prompt
Remove food needed to breed
User prompt
Make it so the ai is more effective at grabing food and at moving towards it's familly members to share when needed.
User prompt
Make the ai change its direction to grab food once it sees it.
User prompt
Enhance the ai movement system so it goes for the food as soon as it enters its field of view.
User prompt
Make it so ais have more traits such as speed. Add at least 3 new traits. Make them inheritable and mutatable.
User prompt
Make it so each family has another color.
User prompt
Make it so ai takes in consideration the food left, and if there are more ai's in its familly, that don't have enough food to survive it avoids taking food anymore, or if it has a lot of food it shares with the others from it's familly.
User prompt
Fix Bug: 'TypeError: self.generation is undefined' in this line: 'self.generationText = new Text2(self.generation.toString(), {' Line Number: 28
User prompt
Make it so each ai displays the generation it belongs to.
User prompt
Add some more traits to each ai. Such as speed, food needed to reproduce (which should be between 2 and 3), and other traits. Don't forget to make them mutable/inheritable.
User prompt
Make it so in one individual generation, each ai can only have one child
User prompt
Make so each ai can only spawn one child at a time.
/****
* Classes
****/
var AI = Container.expand(function () {
var self = Container.call(this);
self.inheritTraits = function (parent) {
this.food = parent.food * 0.5;
this.speed = parent.speed;
this.agility = parent.agility;
this.strength = parent.strength;
this.intelligence = parent.intelligence;
};
var ais = [];
self.foods = [];
self.aiGraphics = self.attachAsset('ai', {
anchorX: 0.5,
anchorY: 0.5
});
self.food = 10;
self.generation = 1;
self.family = [];
self.generationText = new Text2(self.generation.toString(), {
size: 50,
fill: '#ffffff'
});
self.generationText.anchor.set(.5, 0);
self.addChild(self.generationText);
self.eat = function (food) {
if (food.amount > 0) {
var familyNeedsFood = this.family.some(function (member) {
return member.food < 1;
});
if (!familyNeedsFood) {
this.food += food.consume(1);
}
}
};
self.share = function (otherAI) {
var decision = this.neuralNetwork.feedForward([this.food, otherAI.food, this.fieldOfView]);
if (decision > 0) {
if (this.food > 10) {
var shareAmount = this.food * 0.1;
this.food -= shareAmount;
this.family.forEach(function (member) {
member.food += shareAmount / this.family.length;
});
}
}
};
self.survive = function () {
this.food -= 0.01;
if (this.food < 0) {
this.destroy();
var index = ais.indexOf(this);
if (index > -1) {
ais.splice(index, 1);
}
}
};
self.neuralNetwork = new NeuralNetwork();
self.neuralNetwork.weights = [Math.random(), Math.random()];
self.neuralNetwork.bias = Math.random();
self.direction = Math.random() * 2 * Math.PI;
self.fieldOfView = Math.random() * Math.PI;
self.viewDistance = Math.random() * 500;
self.speed = Math.random() * 5;
self.reproductionThreshold = 2;
self.generation = 1;
self.agility = Math.random() * 5;
self.strength = Math.random() * 5;
self.intelligence = Math.random() * 5;
self._move_migrated = function () {
var nearestFood = null;
var nearestFoodDistance = Infinity;
for (var i = 0; i < this.foods.length; i++) {
var dx = this.foods[i].x - this.x;
var dy = this.foods[i].y - this.y;
var distance = Math.sqrt(dx * dx + dy * dy);
var angle = Math.atan2(dy, dx);
var deltaAngle = Math.abs(this.direction - angle);
if (distance < this.viewDistance && deltaAngle < this.fieldOfView / 2 && distance < nearestFoodDistance) {
nearestFood = this.foods[i];
nearestFoodDistance = distance;
}
}
if (nearestFood) {
var dx = nearestFood.x - this.x;
var dy = nearestFood.y - this.y;
self.direction = Math.atan2(dy, dx);
} else {
if (Math.random() < 0.05) {
self.direction += Math.random() * 0.2 - 0.1;
}
}
var speed = 5;
var newX = self.x + Math.cos(self.direction) * speed;
var newY = self.y + Math.sin(self.direction) * speed;
if (newX < 0) {
newX = 0;
self.direction = Math.PI - self.direction;
} else if (newX > 2048) {
newX = 2048;
self.direction = Math.PI - self.direction;
}
if (newY < 0) {
newY = 0;
self.direction = -self.direction;
} else if (newY > 2732) {
newY = 2732;
self.direction = -self.direction;
}
self.x = newX;
self.y = newY;
};
self.reproduce = function () {
if (this.food > 10) {
var child = new AI();
child.neuralNetwork = this.neuralNetwork.clone();
for (var i = 0; i < child.neuralNetwork.weights.length; i++) {
if (Math.random() < 0.1) {
child.neuralNetwork.weights[i] += Math.random() * 0.2 - 0.1;
}
}
if (Math.random() < 0.1) {
child.neuralNetwork.bias += Math.random() * 0.2 - 0.1;
}
if (Math.random() < 0.1) {
child.fieldOfView += Math.random() * 0.2 - 0.1;
}
if (Math.random() < 0.1) {
child.viewDistance += Math.random() * 100 - 50;
}
if (Math.random() < 0.1) {
child.speed += Math.random() * 0.2 - 0.1;
}
if (Math.random() < 0.1) {
child.reproductionThreshold += Math.random() * 0.2 - 0.1;
}
if (Math.random() < 0.1) {
child.agility += Math.random() * 0.2 - 0.1;
}
if (Math.random() < 0.1) {
child.strength += Math.random() * 0.2 - 0.1;
}
if (Math.random() < 0.1) {
child.intelligence += Math.random() * 0.2 - 0.1;
}
child.inheritTraits(this);
this.food -= 10;
ais.push(child);
this.family.push(child);
child.direction = Math.random() * 2 * Math.PI;
child.generation = this.generation + 1;
return child;
}
return null;
};
});
var Food = Container.expand(function () {
var self = Container.call(this);
var foodGraphics = self.attachAsset('food', {
anchorX: 0.5,
anchorY: 0.5
});
self.amount = 10;
self.consume = function (amount) {
this.amount -= amount;
return amount;
};
});
/****
* Initialize Game
****/
var game = new LK.Game({
backgroundColor: 0x000000
});
/****
* Game Code
****/
var NeuralNetwork = function NeuralNetwork() {
var self = this;
self.weights = [];
self.bias = 0;
self.clone = function () {
var clone = new NeuralNetwork();
clone.weights = this.weights.slice();
clone.bias = this.bias;
return clone;
};
self.feedForward = function (inputs) {
var sum = 0;
for (var i = 0; i < inputs.length; i++) {
sum += inputs[i] * this.weights[i];
}
return sum + this.bias;
};
};
var ais = [];
var foods = [];
for (var i = 0; i < 3; i++) {
var ai = new AI();
ai.x = Math.random() * 2048;
ai.y = Math.random() * 2732;
ais.push(ai);
game.addChild(ai);
var food = new Food();
food.x = Math.random() * 2048;
food.y = Math.random() * 2732;
foods.push(food);
game.addChild(food);
}
LK.on('tick', function () {
for (var i = 0; i < ais.length; i++) {
var ai = ais[i];
ai._move_migrated();
ai.survive();
ai.generationText.setText(ai.generation.toString());
for (var j = 0; j < foods.length; j++) {
var food = foods[j];
if (ai.intersects(food)) {
ai.eat(food);
food.consume(1);
if (food.amount <= 0) {
food.destroy();
foods.splice(j, 1);
j--;
}
}
}
for (var j = i + 1; j < ais.length; j++) {
var otherAI = ais[j];
if (ai.intersects(otherAI)) {
var decision = ai.neuralNetwork.feedForward([ai.food, otherAI.food]);
if (decision > 0) {
ai.share(otherAI);
}
}
}
if (ai.food <= 0) {
var index = ais.indexOf(ai);
if (index > -1) {
ais.splice(index, 1);
}
ai.destroy();
i--;
if (ais.length === 0) {
LK.showGameOver();
}
}
}
if (LK.ticks % 600 == 0) {
for (var i = 0; i < ais.length; i++) {
var ai = ais[i];
var child = ai.reproduce();
if (child) {
child.x = ai.x;
child.y = ai.y;
game.addChild(child);
ais.push(child);
}
}
}
if (LK.ticks % 120 == 0) {
var food = new Food();
food.x = Math.random() * 2048;
food.y = Math.random() * 2732;
foods.push(food);
game.addChild(food);
}
}); ===================================================================
--- original.js
+++ change.js
@@ -1,41 +1,51 @@
-var foods = [];
-var NeuralNetwork = function () {
- var self = this;
- self.weights = [];
- self.bias = 0;
- self.clone = function () {
- var clone = new NeuralNetwork();
- clone.weights = this.weights.slice();
- clone.bias = this.bias;
- return clone;
- };
- self.feedForward = function (inputs) {
- var sum = 0;
- for (var i = 0; i < inputs.length; i++) {
- sum += inputs[i] * this.weights[i];
- }
- return sum + this.bias;
- };
-};
+/****
+* Classes
+****/
var AI = Container.expand(function () {
var self = Container.call(this);
self.inheritTraits = function (parent) {
this.food = parent.food * 0.5;
+ this.speed = parent.speed;
+ this.agility = parent.agility;
+ this.strength = parent.strength;
+ this.intelligence = parent.intelligence;
};
- self.aiGraphics = self.createAsset('ai', 'AI character', .5, .5);
+ var ais = [];
+ self.foods = [];
+ self.aiGraphics = self.attachAsset('ai', {
+ anchorX: 0.5,
+ anchorY: 0.5
+ });
self.food = 10;
+ self.generation = 1;
+ self.family = [];
+ self.generationText = new Text2(self.generation.toString(), {
+ size: 50,
+ fill: '#ffffff'
+ });
+ self.generationText.anchor.set(.5, 0);
+ self.addChild(self.generationText);
self.eat = function (food) {
if (food.amount > 0) {
- this.food += food.consume(1);
+ var familyNeedsFood = this.family.some(function (member) {
+ return member.food < 1;
+ });
+ if (!familyNeedsFood) {
+ this.food += food.consume(1);
+ }
}
};
self.share = function (otherAI) {
- var decision = this.neuralNetwork.feedForward([this.food, otherAI.food]);
+ var decision = this.neuralNetwork.feedForward([this.food, otherAI.food, this.fieldOfView]);
if (decision > 0) {
- var shareAmount = this.food * 0.1;
- this.food -= shareAmount;
- otherAI.food += shareAmount;
+ if (this.food > 10) {
+ var shareAmount = this.food * 0.1;
+ this.food -= shareAmount;
+ this.family.forEach(function (member) {
+ member.food += shareAmount / this.family.length;
+ });
+ }
}
};
self.survive = function () {
this.food -= 0.01;
@@ -50,27 +60,38 @@
self.neuralNetwork = new NeuralNetwork();
self.neuralNetwork.weights = [Math.random(), Math.random()];
self.neuralNetwork.bias = Math.random();
self.direction = Math.random() * 2 * Math.PI;
- self.move = function () {
+ self.fieldOfView = Math.random() * Math.PI;
+ self.viewDistance = Math.random() * 500;
+ self.speed = Math.random() * 5;
+ self.reproductionThreshold = 2;
+ self.generation = 1;
+ self.agility = Math.random() * 5;
+ self.strength = Math.random() * 5;
+ self.intelligence = Math.random() * 5;
+ self._move_migrated = function () {
var nearestFood = null;
var nearestFoodDistance = Infinity;
- for (var i = 0; i < foods.length; i++) {
- var distance = Math.sqrt(Math.pow(this.x - foods[i].x, 2) + Math.pow(this.y - foods[i].y, 2));
- if (distance < nearestFoodDistance) {
- nearestFood = foods[i];
+ for (var i = 0; i < this.foods.length; i++) {
+ var dx = this.foods[i].x - this.x;
+ var dy = this.foods[i].y - this.y;
+ var distance = Math.sqrt(dx * dx + dy * dy);
+ var angle = Math.atan2(dy, dx);
+ var deltaAngle = Math.abs(this.direction - angle);
+ if (distance < this.viewDistance && deltaAngle < this.fieldOfView / 2 && distance < nearestFoodDistance) {
+ nearestFood = this.foods[i];
nearestFoodDistance = distance;
}
}
- var decision = this.neuralNetwork.feedForward([this.food, nearestFood.amount, nearestFoodDistance]);
- if (decision > 0) {
- if (Math.random() < 0.05) {
- self.direction += Math.random() * 0.2 - 0.1;
- }
- } else {
+ if (nearestFood) {
var dx = nearestFood.x - this.x;
var dy = nearestFood.y - this.y;
self.direction = Math.atan2(dy, dx);
+ } else {
+ if (Math.random() < 0.05) {
+ self.direction += Math.random() * 0.2 - 0.1;
+ }
}
var speed = 5;
var newX = self.x + Math.cos(self.direction) * speed;
var newY = self.y + Math.sin(self.direction) * speed;
@@ -102,93 +123,150 @@
}
if (Math.random() < 0.1) {
child.neuralNetwork.bias += Math.random() * 0.2 - 0.1;
}
+ if (Math.random() < 0.1) {
+ child.fieldOfView += Math.random() * 0.2 - 0.1;
+ }
+ if (Math.random() < 0.1) {
+ child.viewDistance += Math.random() * 100 - 50;
+ }
+ if (Math.random() < 0.1) {
+ child.speed += Math.random() * 0.2 - 0.1;
+ }
+ if (Math.random() < 0.1) {
+ child.reproductionThreshold += Math.random() * 0.2 - 0.1;
+ }
+ if (Math.random() < 0.1) {
+ child.agility += Math.random() * 0.2 - 0.1;
+ }
+ if (Math.random() < 0.1) {
+ child.strength += Math.random() * 0.2 - 0.1;
+ }
+ if (Math.random() < 0.1) {
+ child.intelligence += Math.random() * 0.2 - 0.1;
+ }
child.inheritTraits(this);
this.food -= 10;
ais.push(child);
+ this.family.push(child);
child.direction = Math.random() * 2 * Math.PI;
+ child.generation = this.generation + 1;
return child;
}
return null;
};
});
var Food = Container.expand(function () {
var self = Container.call(this);
- var foodGraphics = self.createAsset('food', 'Food', .5, .5);
+ var foodGraphics = self.attachAsset('food', {
+ anchorX: 0.5,
+ anchorY: 0.5
+ });
self.amount = 10;
self.consume = function (amount) {
this.amount -= amount;
return amount;
};
});
-var Game = Container.expand(function () {
- var self = Container.call(this);
- var ais = [];
- for (var i = 0; i < 3; i++) {
- var ai = new AI();
- ai.x = Math.random() * 2048;
- ai.y = Math.random() * 2732;
- ais.push(ai);
- self.addChild(ai);
- var food = new Food();
- food.x = Math.random() * 2048;
- food.y = Math.random() * 2732;
- foods.push(food);
- self.addChild(food);
- }
- LK.on('tick', function () {
- for (var i = 0; i < ais.length; i++) {
- var ai = ais[i];
- ai.move();
- ai.survive();
- for (var j = 0; j < foods.length; j++) {
- var food = foods[j];
- if (ai.intersects(food)) {
- ai.eat(food);
- food.consume(1);
- if (food.amount <= 0) {
- food.destroy();
- foods.splice(j, 1);
- j--;
- }
+
+/****
+* Initialize Game
+****/
+var game = new LK.Game({
+ backgroundColor: 0x000000
+});
+
+/****
+* Game Code
+****/
+var NeuralNetwork = function NeuralNetwork() {
+ var self = this;
+ self.weights = [];
+ self.bias = 0;
+ self.clone = function () {
+ var clone = new NeuralNetwork();
+ clone.weights = this.weights.slice();
+ clone.bias = this.bias;
+ return clone;
+ };
+ self.feedForward = function (inputs) {
+ var sum = 0;
+ for (var i = 0; i < inputs.length; i++) {
+ sum += inputs[i] * this.weights[i];
+ }
+ return sum + this.bias;
+ };
+};
+var ais = [];
+var foods = [];
+for (var i = 0; i < 3; i++) {
+ var ai = new AI();
+ ai.x = Math.random() * 2048;
+ ai.y = Math.random() * 2732;
+ ais.push(ai);
+ game.addChild(ai);
+ var food = new Food();
+ food.x = Math.random() * 2048;
+ food.y = Math.random() * 2732;
+ foods.push(food);
+ game.addChild(food);
+}
+LK.on('tick', function () {
+ for (var i = 0; i < ais.length; i++) {
+ var ai = ais[i];
+ ai._move_migrated();
+ ai.survive();
+ ai.generationText.setText(ai.generation.toString());
+ for (var j = 0; j < foods.length; j++) {
+ var food = foods[j];
+ if (ai.intersects(food)) {
+ ai.eat(food);
+ food.consume(1);
+ if (food.amount <= 0) {
+ food.destroy();
+ foods.splice(j, 1);
+ j--;
}
}
- for (var j = i + 1; j < ais.length; j++) {
- var otherAI = ais[j];
- if (ai.intersects(otherAI)) {
- var decision = ai.neuralNetwork.feedForward([ai.food, otherAI.food]);
- if (decision > 0) {
- ai.share(otherAI);
- }
+ }
+ for (var j = i + 1; j < ais.length; j++) {
+ var otherAI = ais[j];
+ if (ai.intersects(otherAI)) {
+ var decision = ai.neuralNetwork.feedForward([ai.food, otherAI.food]);
+ if (decision > 0) {
+ ai.share(otherAI);
}
}
- if (ai.food <= 0) {
- var index = ais.indexOf(ai);
- if (index > -1) {
- ais.splice(index, 1);
- }
- ai.destroy();
- i--;
+ }
+ if (ai.food <= 0) {
+ var index = ais.indexOf(ai);
+ if (index > -1) {
+ ais.splice(index, 1);
}
+ ai.destroy();
+ i--;
+ if (ais.length === 0) {
+ LK.showGameOver();
+ }
}
- if (LK.ticks % 600 == 0) {
- for (var i = 0; i < ais.length; i++) {
- var ai = ais[i];
- var child = ai.reproduce();
- if (child) {
- child.x = ai.x;
- child.y = ai.y;
- self.addChild(child);
- ais.push(child);
- }
+ }
+ if (LK.ticks % 600 == 0) {
+ for (var i = 0; i < ais.length; i++) {
+ var ai = ais[i];
+ var child = ai.reproduce();
+ if (child) {
+ child.x = ai.x;
+ child.y = ai.y;
+ game.addChild(child);
+ ais.push(child);
}
}
- if (LK.ticks % 60 == 0) {
- var food = new Food();
- food.x = Math.random() * 2048;
- food.y = Math.random() * 2732;
- foods.push(food);
- self.addChild(food);
- }
- });
-});
+ }
+ if (LK.ticks % 120 == 0) {
+ var food = new Food();
+ food.x = Math.random() * 2048;
+ food.y = Math.random() * 2732;
+ foods.push(food);
+ game.addChild(food);
+ }
+});
\ No newline at end of file
