AI Players

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AI Agents that are supposed to be able to function on a level comparable to players.

All games need players. However, there are not always enough people available and willing to play to fit the demands of a game's design and in these cases supplementing people with rule-based algorithms can be a solution. These AI Players provide a flexibility for when gameplay can occur and may also be able to offer pre-defined or customizable opponents to fit the level of challenge people may wish to have.

Examples

Already the first computer game, 'OXO' - a computer-based version of Tic-Tac-Toe developed by Alexander S. Douglas, allowed a player to compete against the program itself[1]. Today nearly all multiplayer computer games have support for replacing people with computer opponents (e.g. the Age of Empires series, the Battlefield series, the Command and Conquer series, the Left 4 Dead series, and the Tekken series). For some of these games it may be unpractical to find enough people to fill all the player slots available, so the norm in these cases is that some or the majority of the players are actually AI Players (examples of where this can occur include the Europa Universalis series, the Civilization series, and the Need for Speed series).

Not all AI players need to be controlled by computers. A 'robot', really a set of instructions that a human needed to follow, was introduced in the expansion 'The Gathering Storm' of the card game Race for the Galaxy. This 'robot' allows a single player to player against it as if a two-player instance of the game was being played. An even earlier example of an AI Player was 'MENACE' by Donald Michie. Although not the first AI Player for Tic-Tac-Toe it could get better after each time it played and was first implemented through the use of beans and about 300 matchboxes[2]. Another solution, that only works for a small range of games where interaction between players are very limited, is to use recordings of previous players actions. The ESP Game is an example of this.

Depending on which perspective one chooses to use, programming games such as Crobots and P-robots either only have AI Players or are zero-player games.

Using the pattern

The creation of AI Players is the design of Algorithmic Agents that can take the role of players. One aspect of this design is to consider if the choice of using AI Players can only be done at the beginning of them game or if this can change during gameplay. The first option allows Multiplayer Games can have more players than people playing it or than they can be played as if they were Single-Player Games while avoiding problems of Game Balance and Team Balance due to changes in players. The second option allows Drop-In/Drop-Out with may have problems with Game Balance and Team Balance unless they are pure Cooperation games.

One less common choice for AI Players

Mules

Agents Avatars Units Freedom of Choice Zero-Player Games Factions Teams Creative Control

If several various types of AI Players are offered to players to choose from together with explanations of their difference in gaming and in skill, this can provide both Varied Gameplay and an indirect way of having Difficulty Settings. By doing so, games can provide Smooth Learning Curves and supporting players in reaching Game Mastery. If the AI Players can change their behavior due to how well people are gaming, this is one form of Dynamic Difficulty Adjustment.

Replays

Diegetic Aspects

Interface Aspects

Narrative Aspects

Consequences

AI Players allow Multiplayer Games to be played as Single-Player Games. They also allows the creation of Zero-Player Games when all players are replaced by AI Players (although this typically opens up additional interpretations about Meta Games or questioning if any gameplay occurs or not).

Relations

Can Instantiate

Can Modulate

Can Be Instantiated By

Can Be Modulated By

Possible Closure Effects

Potentially Conflicting With

History

New pattern created in this wiki.

References

  1. Link to the EDSAC emulator website, which includes the code for 'OXO'.
  2. Michie, D. 'Trial and Error', in Penguin Science Survey 1961, Vol. 2.