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fsm

Finite State Machines in Javascript

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.

Implementation

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:

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

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

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.

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.

Example

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

Use the mouse to move the white balls:

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sheepdog_trails

Links to Understand The Boiding Algorithm

Ludum Dare 31, the last jam of the year, ended up very well for me. I created “baa ram ewe“, a game where you must herd sheeps with the mouse, moving them from a thin grass to a plentiful pasture. The game has a good – and solid, for an experimental game made in 48 hours – mechanics and a good overall aesthetics, I really liked the final result of this game. I also received a lot of positive feedbacks, mostly asking me to create a mobile version, which I decided to do.

Baa Ram Ewe uses a boiding algorithm (also known as flocking algorithm) to move the sheeps, to keep them together, and to avoid obstacles and dangerous elements. The boiding algorithm is a method to simulate collective movement of animals, such as fishes, birds, sheep, etc. For example, take a look a the following video, which shows a simulation of buffaloes running:

The algorithm is very simple. All agents in the simulation follow a set of simple rules. These rules defines how each agent will move accordingly to its neighbors flock-mates. The interaction between the agents generate an emergent behavior, as you can see in the video.

The rules used in these kind of simulation are really simple. Commonly, all boiding applications have the following ones:

  • Separation: the agent must avoid the nearest flock-mates by steering away from them;
  • Alignment: the agent try to head to the average position of the nearest flock-mates;
  • Cohesion: the agent try to move to the average position considering the nearest flock-mates;

You can see a visual example of these rules on the figure below (copied of the Craig Reynold’s site, the creator of this algorithm)

boiding_rules
(a) separation rule; (b) alignment rule; and (c) cohesion rule. From (http://www.red3d.com/cwr/boids/)

In Baa Ram Ewe, I used the algorithms presented by Conrad Parker in his site as basis. Summarizing, I have a FLOCKING function that moves the sheeps accordingly to a set of rules:

As you can see, you can define any number of rules, but be careful with that! More rules mean more complexity, you probably won’t be able to generate the behavior you want. Check it out the Conrad Parker site to a nice description of the basic rules.

See