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Types of Game Worlds

It's surprising how many games rely on movement as their most fundamental concept. They range from chiefly strategic logic games such as Chess or Diplomacy, or the more entertaining alternatives such as Horses or Downfall. Let's not forget computer games of course; first-person shooters, real-time strategy, or even role-playing games would be extremely dull without motion!

Despite virtual worlds being widespread, they come in many varieties. Notably, there are worlds of different dimensions (for instance, 2D or 3D), with different sizes and precision. Some worlds are based on grids (discrete), and some are free of restrictions (continuous).

Conceptually, there is a big difference between these variations, which translates into the feel and style of the gameplay. Behind the scenes in the engine, distinct data structures and implementation tricks are used in each case, but this discussion focuses on the consequences for the AI.

On Dimensions

Some games can take place in 2D levels like top-down worlds (Warcraft II), or side views with moving platforms and smooth scrolling (Super Mario Brothers). Alternatively, the world itself can be fully 3D, with different floors in buildings (Half Life), or even tunnels (Descent 3).

It's important to note that the dimensions of the game world are independent from the rendering. For example, the original Doom is in fact a 2D world with the floors at different levels. Even recent 3D first-person shooters have a lot in common with Doom. This leads to the assumption that the underlying properties of the environment are very similar. Indeed, much of the technology is applicable in 2D and 3D.

Most academic research projects deal with top-down 2D AI movement algorithms, for example [Seymour01], which provides a nice amalgamation of past research. The same goes for the most of the figures here; they show the problem projected onto the floor. This is primarily because 2D movement is simpler than its 3D counterpart.

As a justification, one often read that "2D generalizes to 3D," so it's fine to focus on the simpler alternative. With an algorithm defined for two dimensions, the same concept is re-applied again to an additional dimension! This is mostly the case, although special care needs to be taken to devise a solution that scales up with respect to the number of dimensions.

For movement in 3D games, this simplification is also acceptable. In realistic environments, gravity plays an important role. All creatures are on the floor most of the time, which almost simplifies the problem to a 2D one. It is not quite 2D because the floor surfaces can superimposed at different heights (for instance, a spiraling staircase); this type of world is often known as 2.5D, halfway between the complexity of two and three dimensions.

In the process of applying, or generalizing, our 2D solution to 3D, watch out for a couple of pitfalls:

  • Complex environments often exhibit tricky contraptions (for example, jump pads or lifts), which are quite rare in flat worlds.

  • Instead of naively applying an original algorithm, it can be tailored to make the most of the problem at hand.

These factors influence the design the solution.

Discrete Versus Continuous

Another property of game worlds is their precision, in both time and space. There are two different approaches, discrete and continuous events (in space or time), as shown in Figure 5.1. A discrete variable can accept only a finite number of values, whereas a continuous variable theoretically takes infinite values.

Figure 5.1. Two types of game environments observed top-down. On the left, space is continuous, whereas the world on the right is based on a grid (discrete).


Conceptually speaking, there is little difference between a continuous domain and a discrete one. Indeed, when the discretization is too fine to notice, the domain appears continuous. This doesn't apply mathematically, of course, but on modern computers, there are hardware limitations. To simulate continuous media, current software must select an appropriate discrete representation.

Using examples for space and time in game worlds shows how it's not only a game design issue, but also a fundamental element for the AI. (It's an independent AI design decision.)


The game environment can be limited to cells of a grid; it's discrete if there are a finite number of grid coordinates. Continuous environments have unlimited locations because they are not restricted to grids.

To store the coordinates in the world, data types must be chosen. These are usually single precision floating-point numbers, which essentially allocate 32 bits to discretize those dimensions. Although there are benefits to using a double representation (64 bits), single representations are enough to make each object seem like it can take every possible position and orientation in space.


Actions can be discrete in time, taking place at regular intervals—like in turn-based strategy games. Alternatively, actions can be continuous, happening smoothly through time as in fast-pace action games.

Similar floating-point data types can be chosen to represent this dimension. However, this choice is slightly more complicated because the programmer can't "manipulate" time as easily. Conceptually, single processors can only execute one logical process at a time—despite being able to perform multiple atomic operations in parallel. Consequently, it's not possible for each object in the world to be simulated continuously. The closest the computer can get is an approximation; a portion of code determines what happened since the last update (for example, every 0.1 seconds). So despite being very precise, a floating-point number will not be entirely necessary to represent a value in time (see Figure 5.2).

Figure 5.2. On the top, a continuous timeline with events happening at any point in time. On the bottom, the same timeline, but discrete. The time of each event is rounded to the closest mark every two seconds.



Fundamentally, because of the limitations of computers, each of these dimensions can be considered as discrete—although it does not appear that way. Regardless, these data types can be converted to one another by simple mapping; this involves scaling, converting to an integer by rounding, and rescaling back. Arbitrary discretizations can thereby be obtained.

In practice, it's possible for the world to have one type of discretization and use a different discretization for the AI. For example, mapping continuous onto discrete domains enables us to exploit similarities in the AI design, and reuse existing AI routines (for instance, standard grid-based A* pathfinding). Of course, we may not necessarily want to perform the conversion for many reasons (for example, compromising behavior quality or sacrificing the complexity of environment). But at least AI engineers have the luxury to decide!

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