Upit | Learn about creating the game Machine learning example with gen AI

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;
	};
};
var AI = Container.expand(function () {
	var self = Container.call(this);
	self.inheritTraits = function (parent) {
		this.food = parent.food * 0.5;
	};
	var ais = [];
	self.aiGraphics = self.createAsset('ai', 'AI character', .5, .5);
	self.food = 10;
	self.eat = function (food) {
		if (food.amount > 0) {
			this.food += food.consume(1);
		}
	};
	self.share = function (otherAI) {
		var decision = this.neuralNetwork.feedForward([this.food, otherAI.food]);
		if (decision > 0) {
			var shareAmount = this.food * 0.1;
			this.food -= shareAmount;
			otherAI.food += shareAmount;
		}
	};
	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.move = function () {
		var nearestFood = null;
		var nearestFoodDistance = Infinity;
		for (var i = 0; i < this.foods.length; i++) {
			var distance = Math.sqrt(Math.pow(this.x - this.foods[i].x, 2) + Math.pow(this.y - this.foods[i].y, 2));
			if (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 {
			var dx = nearestFood.x - this.x;
			var dy = nearestFood.y - this.y;
			self.direction = Math.atan2(dy, dx);
		}
		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;
			}
			child.inheritTraits(this);
			this.food -= 10;
			ais.push(child);
			child.direction = Math.random() * 2 * Math.PI;
			return child;
		}
		return null;
	};
});
var Food = Container.expand(function () {
	var self = Container.call(this);
	var foodGraphics = self.createAsset('food', 'Food', .5, .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 = [];
	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);
		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--;
					}
				}
			}
			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 (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 % 60 == 0) {
			var food = new Food();
			food.x = Math.random() * 2048;
			food.y = Math.random() * 2732;
			foods.push(food);
			self.addChild(food);
		}
	});
});
Creation History
