The 32th ludum dare happened this weekend (April 16, 2015), with the theme “unconventional weapon“.


I have have to say that I hate this theme. Really, there were so many good themes there, why this one could win? Damn you!!!! The theme isn’t an excuse to not do a game, but I spent so much time thinking of what I could do with this… A game not based on fights? Weapon is love? The weapon must be abstract! This is clichĂȘ? Oh damn… I finally came up with an idea, after 14 hours (counting sleep time)!

My game entry is about “democracy”, with quotes. The democracy and progress are the weapon of the government in the game, they want you to attack other species in order to “spread democracy and progress through the galaxy”. The player controls the invasions to planets inhabited by bugs with the goal to kill the natives and collect the planet resources.

The game is a very humble tribute to starship troopers (novel by Robert Heinlein and film by Paul Verhoven and Edward Neumeier). I wanted to create, somehow, the sense of order and progress presented by movie.


I always start by the core mechanics because it is the main component of the game – if I can’t find time to make graphics, at least I have a block-based game.

I also always wanted to create a game in a circular world, that was my excuse. I had no idea how to do that, but at is was pretty simple. You have a world centered at (0, 0) with radius (r), than you can set the origin of all objects in the world to (0, r) and only manipulate the rotation to place than in the world.

After the basic world structure, I could drop structures, and these structures could create units. To control the units, I created a behavior tree to control each unit – the first time I really use behavior3js – and a behavior tree to control the bugs strategy. Figure below show these behaviors trees:

Behavior tree for player and enemy units.

Behavior tree for player and enemy units – Click to see in full size.

Behavior tree for the nests, its used to create orders to enemy units

Behavior tree for the nests, its used to create orders to enemy units – Click to see in full size.

Then it came to particles and easing! Man, I love these two. The animation below shows the game at this point:

Sample of democracy

After 28 hours I started to create the sprites and visual things in the world. I was stupid enough to lost time adding some details to sprites that don’t even will see because the sprites are very small (about 20 pixels). I also spend a good time trying to create a futurist screen aspect, which ended up very cool.

I added some scenes, UI and some other effects to the graphics:


When the clock marked 5 hours to end the competition I started create sounds effects and music. I was desperate because of the time, I still had to adjust the levels of my game and I didn’t know what to do with sounds. At the end it was pretty simple and I didn’t do much, actually recorded some sounds with Audacity, generated most of them in BFXR and generated the music in SoundHelix.

I finished the game in the packing hour, the extra hour after 48, the time to deploy and write about your game.


Good stuff:

  • Game finished is always good!
  • I’m using BTs successfully, even to control hundreds of creatures.
  • A circular world (always wanted to try).
  • Visually smooth and lot of cool PARTICLES!!!!!
  • Generative music and sounds worked well.

Bad stuff:

  • Balancing is hard as hell, I couldn’t do that properly.
  • Some details missing, especially the visual feedback when you win the game and the bug when restarting a level before a tween is complete.
  • Isn’t much fun due to balance, few types of units, and few levels.

I’m very critic about my games, I really fell for to not balance properly and create more content to the game, but well, it was made in 48 hours and this time the good stuff was really good! Moreover, this was the most complex and complete game I ever made for LD, it was really hard to do, thus of course some things would be missing.



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I’ve been talking a lot about Behavior Trees (BTs) lately, partially because I’m using them for my PhD. But although, BTs provide a powerful and flexible tool to model game agents, this method still have problems.

Suppose you want to model a bunch of sheeps (just like my last Ludum Dare game “Baa Ram Ewe”), these sheep have simple behaviors: “run from cursor”, “stay near to neighbor sheeps”, “don’t collide with neighbor sheeps” and “follow velocity and direction of neighbors”. A sheep can also have 4 states: “idle” when it is just eating grass, “obey” when it is being herded by the player (using the mouse), “stopping” between obey and idle, and “fear” when a predator is near. The behaviors are always executing, but they may have different weight for different states of the sheep. For example, when a sheep is “obey”-ing, it try to be near other sheeps more than when it is eating grass or running scared.

Modeling this as a Behavior Tree is hard because:

  1. BTs don’t really model states well. There is no default mechanism to define or consult which state an agent is; and
  2. All behaviors are executed each tick, thus this agent wouldn’t exploit the BT advantages of constrained executions.

Notice that, you still can model these sheeps with BTs, but the final model would be a lot more complex than it would be using other simple methods.

In previous posts, I also talked about how Behavior Trees have several advantages over Finite State Machines (FSMs). But, in cases like this a FSM is a lot useful and considerably easier to use than BTs.


Like my Behavior Tree implementation, I want to use a single instance of a FSM to control multiple agents, so if a game has 100 of creatures using the same behaviors, only a single FSM instance is needed, saving a lot of memory. To do this, each agent must have its own memory, which is used by the FSM and the states to store and retrieve internal information. This memory is also useful to store sensorial information, such as the distance to nearest obstacles, last enemy position, etc.

First, consider that all states and machines have a different id, created using the following function:

function createUUID() {
  var s = [];
  var hexDigits = "0123456789abcdef";
  for (var i = 0; i < 36; i++) {
    s[i] = hexDigits.substr(Math.floor(Math.random() * 0x10), 1);
  // bits 12-15 of the time_hi_and_version field to 0010
  s[14] = "4";

  // bits 6-7 of the clock_seq_hi_and_reserved to 01
  s[19] = hexDigits.substr((s[19] & 0x3) | 0x8, 1);

  s[8] = s[13] = s[18] = s[23] = "-";

  var uuid = s.join("");
  return uuid;

and to simply inheritance, we will use the Class function:

function Class(baseClass) {
  // create a new class
  var cls = function(params) {
  // if base class is provided, inherit
  if (baseClass) {
    cls.prototype = Object.create(baseClass.prototype);
    cls.prototype.constructor = cls;
  // create initialize if does not exist on baseClass
  if(!cls.prototype.initialize) {
    cls.prototype.initialize = function() {};

  return cls;

We will use a Blackboard as memory for our agents. Notice that, this is the same blackboard used in my behavior trees.

var Blackboard = Class();
var p = Blackboard.prototype;

p.initialize = function() {
  this._baseMemory = {};
  this._machineMemory = {};

p._getTreeMemory = function(machineScope) {
  if (!this._machineMemory[machineScope]) {
    this._machineMemory[machineScope] = {
      'nodeMemory'     : {},
      'openNodes'      : [],
      'traversalDepth' : 0,
      'traversalCycle' : 0,
  return this._machineMemory[machineScope];

p._getNodeMemory = function(machineMemory, nodeScope) {
  var memory = machineMemory['nodeMemory'];
  if (!memory[nodeScope]) {
    memory[nodeScope] = {};

  return memory[nodeScope];

p._getMemory = function(machineScope, nodeScope) {
  var memory = this._baseMemory;

  if (machineScope) {
    memory = this._getTreeMemory(machineScope);

    if (nodeScope) {
      memory = this._getNodeMemory(memory, nodeScope);

  return memory;

p.set = function(key, value, machineScope, nodeScope) {
  var memory = this._getMemory(machineScope, nodeScope);
  memory[key] = value;

p.get = function(key, machineScope, nodeScope) {
  var memory = this._getMemory(machineScope, nodeScope);
  return memory[key];

We will also use a state object that implements the following methods:

  • enter“: called by the FSM when a transition occurs and this state is now the current;
  • exit“, called by the FSM when a transition occurs and this state is not the current one anymore; and
  • tick“, called by the FSM every tick in the machine. This method contains the actual behavior code for each state.
var State = statejs.Class();
var p = State.prototype;

p.initialize = function() { = statejs.createUUID();
  this.machine = null;

p.enter = function(target, memory) {}

p.tick = function(target, memory) {}

p.exit = function(target, memory) {}

Our FSM will have the following methods:

  • add(name, state)“: adds a new state to the FSM, this state is identified by a unique name.
  • get(name)“: returns the state instance registered in the FSM, given a name.
  • list()“: returns the list of state names in the FSM.
  • name(memory)“: return the name of the current state. It can be null if there is no current state.
  • to(name, target, memory)“: perform a transition from the current state to the provided state name.
  • tick(target, memory)“: tick the FSM, which propagates to the current state.

Notice that, some methods must receive the blackboard and the target object as parameters, which can be a little annoying – this is the downside of using a single FSM to control multiple agents – but the cost is small compared to the gain in memory.

The target parameter is usually the agent being controlled, but in practice it can be any kind of object such as DOM elements, function or variables.

var FSM = statejs.Class();
var p = FSM.prototype;

p.initialize = function() { = statejs.createUUID();
  this._states = {};

p.add = function(name, state) {
  if (typeof this._states[name] !== 'undefined') {
    throw new Error('State "'+name+'" already on the FSM.');

  this._states[name] = state;
  state.machine = this;

  return this;

p.get = function(name) {
  return this._states[name];

p.list = function() {
  var result = [];
  for (var name in this._states) {

  return result;
} = function(memory) {
  return memory.get('name',;
} = function(name, target, memory) {
  if (typeof this._states[name] === 'undefined') {
    throw new Error('State "'+name+'" does not exist.');

  // exit current state
  var fromStateName = memory.get('name',;
  var fromState = this.get(fromStateName);
  if (fromState) {
    fromState.exit(target, memory);

  // change to the next state
  var state = this._states[name];
  memory.set('name', name,;
  state.enter(target, memory);

  return this;

p.tick = function(target, memory) {
  var stateName = memory.get('name',;
  var state = this.get(stateName);
  if (state) {
    state.tick(target, memory);


Using a simple Boiding algorithm, we have 3 states: “idle”, “obey” and “stopping”.

var StoppingState = Class(State);

StoppingState.prototype.enter = function(target, memory) {
  // when this state is initiated, it resets the timer
  memory.set('starttime', new Date().getTime(),,;

StoppingState.prototype.tick = function(target, memory) {
  var mx = game.stage.mouseX;
  var my = game.stage.mouseY;

  // transition to obey
  if (euclidDistance(target.x, target.y, mx, my) < SHEEP_OBEY_DISTANCE) {'obey', target, memory);

  // transition to idle
  var starttime = memory.get('starttime',,;
  var curtime = new Date().getTime();
  if (curtime - starttime > 3000) {'idle', target, memory);

  // call the boid algorithm with specific weights
  flock(target, memory.get('neighbors'), [0.0, 0.1, 1.0, 0.4])

Use the mouse to move the white balls:

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