Algorithms created to provide what appears to be intentional behavior.
Many games worlds contains more entities such as animals, people, monsters, or robots that have behaviors that are not decided by players. To make this possible these entities are instead have more or less complex rules, algorithms, that decide which actions they should take. The simplest only contain a couple of rules taking not consideration to what the other players or entities have done while the most complex have processes for learning under which contexts one should perform what actions.
- 1 Examples
- 2 Using the pattern
- 3 Consequences
- 4 Relations
- 4.1 Can Instantiate
- 4.1.1 with Actions Have Diegetically Social Consequences
- 4.1.2 with Companions
- 4.1.3 with Creative Control
- 4.1.4 with Creative Control and Meta Games
- 4.1.5 with Goal-Driven Personal Development
- 4.1.6 with Mutual Goals or Continuous Goals and Supporting Goals
- 4.1.7 with Own Agenda
- 4.1.8 with Preventing Goals
- 4.1.9 with Preventing Goals and Creative Control
- 4.2 Can Modulate
- 4.3 Can Be Instantiated By
- 4.4 Can Be Modulated By
- 4.5 Possible Closure Effects
- 4.6 Potentially Conflicting With
- 4.1 Can Instantiate
- 5 History
- 6 References
- 7 Acknowledgments
Already most of the earliest computer games, including Nimatron, OXO (a computerized version of Tic-Tac-Toe), Asteroids, Space Invaders, and the Bomberman series made use of algorithms to control opponents to the players. It is still used in many games, e.g. in the Assassin's Creed, God of War, Need for Speed, Doom, Quake, and Civilization series. In some of these cases (i.e. Tic-Tac-Toe, Need for Speed, Civilization, and, somewhat paradoxically, multiplayer versions of Doom, Quake), the Algorithmic Agents replaces human opponents while in other cases being part of the game world. The Left 4 Dead series uses Algorithmic Agents not only for the infected that attack the players' characters, but also to control other player characters if there are not four people available. In all these case the Algorithmic Agents are also used to provide opponents but in games such as Fable II, Fallout series, NetHack, and Torchlight the Algorithmic Agents also control animals that accompany the players' characters.
Algorithmic Agents are also used to provide a basis of behaviors to agents in a game which players can then modify, as for example in the Lemmings and Sims series, or indirectly control, of which the Black & White series is an example. Games such as RoboRally and Space Alert, where players have to choose several actions together before they are enacted, can also be seen as examples of games that use Algorithmic Agents even if players create the algorithms/instructions and the outcomes are fixed unless some external factor is changed (e.g. the position of the other robots in RoboRally. MUDs typically give experienced players privileges to modify the game environment, and for those supporting more generic coding such as LPMUDs this can be in the form of independent entities capable of initiative and adaptive responses. In contrast, programming games such as Crobots and P-Robots challenge players to create better algorithms than other players before gameplay begins.
Using the pattern
The use of Algorithmic Agents by necessity require game designer to consider what Agents should exist in the game and what algorithms control their behavior. Which algorithms to use is not only a question of creating them, but also considering who can create them. The most common case is that they are created in advance by game designers but another possibility is to allow players to create the algorithms. The simplest form of this is Action Programming (as for example in RoboRally) and can be seen more as command sets in that they do not take input from the environment while being executed. Examples of games supporting actual coding include both pure programming games, such as Crobots and P-Robots, as well as MUDs, which use it to provide user-created content.
The types of Agents available depends primarily on how they relate to players and their goals. Enemies have Preventing Goals compare to the players' goals and are typically Units or Boss Monsters but Algorithmic Agents can also be used to control Avatars or Characters, thereby creating AI Players as long as they at least seem to have their Own Agendas. The latter can also be used to create Companions so Teams can be created with players (examples include various humans characters in the Fallout series and animals in NetHack and Fable II), either through having Mutual Goals or having individual Continuous and Supporting Goals in relation to the players' goals. Enemies and Companions may both be NPCs but these may also have their Own Agendas independent of player goals. Agents may of course also change what role they have during gameplay, either due to Predetermined Story Structures which may in turn cause Surprises or as effects of players' actions through use of the pattern Actions Have Diegetically Social Consequences. One example of this can be found in the Fallout series, where players' Companions may abandon or attack players who start behaving differently than when they started to cooperate. What constitutes relevant behavior for all these types of Algorithmic Agents depend on what ambitions for Thematic Consistency exist.
However, players may not perceived Enemies, Companions, or NPCs as Agents if their behavior is too predictable even if the entities diegetically convey that relation and behavior. Instead, players may reduce them to Converters, Containers, Obstacles, Self-Service Kiosks, or Traps. Although this may be impossible to hinder if players achieve Gameplay Mastery or simply play long enough, there are many ways to make this take longer time. Initiative, Context Dependent Reactions, and Contextualized Conversational Responses can all make the Algorithmic Agents function more in relation to the current context in their Game Worlds, while Awareness of Surroundings and Actions Have Diegetically Social Consequences forces players to consider what the Algorithmic Agents can sense. Memory of Important Events, Own Agenda, Sense of Self, and Goal-Driven Personal Development all show ways of how to prolong the time until the Algorithmic Agents are no longer seen as Agents (often this include an application of Enforced Agent Behavior in comparison to other similar Agents). Since it is algorithms that create these behaviors, Algorithmic Agents both instantiate these patterns and are affected by them.
For games aiming at Replayability, Open Destiny can also be required since otherwise overarching patterns for the Algorithmic Agents may be found and their apparent agency might lessen. Both these may be created through the use of giving the Algorithmic Agents the ability to have Goal-Driven Personal Development. Many of these solutions require the development of AI systems and require extensive testing to ensure their functionality is what is intended. A complementary approach to this creating Unpredictable Behavior through the use of Randomness or significantly complex rules controlling the behavior. Ambiguous Responses can also be used to make the illusion of intentionality last longer since players have more possibilities to read intentionality into the actions (an approach made famously by the computer program ELIZA).
A less common use of Algorithmic Agents is to interweave it with a player's control of an Avatar or Character. Some games, e.g. the Super Mario series and Fable II, use them when players' have not provided input for extended periods of time to indicate diegetically that the Avatar or Character is becoming bored but return control to the players as soon as input is provided. Other games, e.g. the Lego Star Wars series and the Left 4 Dead series, have them as backup AI Players that can step in when Game Pauses are desired, and by doing so enable Drop-In/Drop-Out gameplay. Concurrent shared control between human players and Algorithmic Agents has been discussed in research for affecting the behavior Characters in general, and exists in FPS games through aimbots
Algorithmic Agents do not themselves exist in a Game World but since they are typically tied closely to specific Avatars, Companions, or Enemies, it is necessary to consider both how the Algorithmic Agents should be able to observe what happens in the Game World and how they can act in it.
In Meta Games focused upon creating Algorithmic Agents it is necessary to include interfaces for this, often including functionality to ensure that the algorithms are properly expressed and the possibility to test them. In games with Companions controlled by Algorithmic Agents it may be necessary to provide specific interface components so that players can modifying their behavior, something which exist for example in the Fallout series.
Algorithmic Agents are necessary if players are to be able to interact with NPCs that exist in Narration Structures, and in this case it is important that they maintain the game's Diegetic Consistency if one aims at evoking an Emotional Engrossment to the unfolding storyline. Providing Actions Have Diegetically Social Consequences to Algorithmic Agents allow them to show Emotional Attachment to what happens in the game world.
Since they do need to be designed, Algorithmic Agents are Predetermined Story Structures if any type of narration is taking place in a game.
Algorithmic Agents are powerful tools that can make sure a game includes Conflicts, since game designer can create them with Preventing Goals to the players' goals and be sure that these will be acted upon (which is not always the case when given to players). However, Algorithmic Agents can just as well used to support Cooperation through Companions. Regardless, the Algorithmic Agents can provide both Complex and Challenging Gameplay and this may be incorporated into Handicap Systems and Difficulty Levels. A side effect of this is that the Algorithmic Agents can be used to modulate how much Tactical Planning a game requires, and how difficult this is. Regardless, since most Agents in games interact with the game through actions in a Game World, Algorithmic Agents is a very common component when designing Game Worlds.
Since Algorithmic Agents can be used to create AI Players, it makes it possible to make Multiplayer Games into Single-Player Games and thereby providing players with a way to have a Smooth Learning Curve at the expense of Social Interaction. Another effect of this is to allow Game Pauses and Drop-In/Drop-Out gameplay styles in Multiplayer Games. Of course, Multiplayer Games can be turned into Zero-Player Games by the same approach, although this is typically only done for supporting No Direct Player Influence through letting players of a Meta Game create the Algorithmic Agents. When the use of Algorithmic Agents are temporary, i.e. they replace players for limited periods of time, this provides support for Interruptibility.
When made by the game designers or developers, Algorithmic Agents provides Enforced Agent Behavior and can offer ways of achieving Predictable Consequences on more general levels of a game. Their behaviors may however take time to understand and until this is achieved they may instead give Limited Planning Ability. In contrast, player-generated algorithms offers players some Creative Control and may be used to support Mules. This also typically encourages Stimulated Planning and Strategic Planning since creating the algorithms requires an understanding of the game system regardless of a specific game state. Even if the Algorithmic Agents typically are abstract construction, they might be considered in this case be viewed as Player Created Game Elements, especially when the creation is linked to Characters or Avatars that would otherwise not be present in the game. When the creation of the algorithms are part of the set-up phase, Algorithmic Agents can create Meta Games where the inner game has No Direct Player Influence and might work against players having an Exaggerated Perception of Influence. This allows programming games such as Crobots and P-Robots to let players have Indirect Conflicts with each other in the sense that they try to create Algorithmic Agents that are in Conflicts with each other (due to Preventing Goals such as Eliminate) but that these players themselves do not perform confrontational actions. When possible during gameplay, the creation of Algorithmic Agents by players is an example of Player Created Game Elements and player-controlled Game Element Insertion.
The combination of players and Algorithmic Agents influencing the actions of the same Characters or Avatars can lead to a conflict over the amount of agency players have, and for this case the pattern can work against Empowerment and Exaggerated Perception of Influence.
While Algorithmic Agents often are realized through code running on computers and thereby set a bound on Ubiquitous Gameplay to require computers, this is not always the case. Race for the Galaxy and Power Grid have expansion with Algorithmic Agents contained on Tiles.
Actions Have Diegetically Social Consequences, Agents, Challenging Gameplay, Complex Gameplay, Creative Control, Enemies, Enforced Agent Behavior, Game Element Insertion, Interruptibility, Limited Planning Ability, Memory of Important Events, Meta Games, Mules, Narration Structures, No Direct Player Influence, Own Agenda, Player Created Game Elements, Predetermined Story Structures, Predictable Consequences, Sense of Self, Tactical Planning
with Creative Control
with Creative Control and Meta Games
with Mutual Goals or Continuous Goals and Supporting Goals
with Own Agenda
with Preventing Goals
with Preventing Goals and Creative Control
Can Be Instantiated By
Action Programming, Rules
Can Be Modulated By
Actions Have Diegetically Social Consequences, Ambiguous Responses, Awareness of Surroundings, Contextualized Conversational Responses, Context Dependent Reactions, Difficulty Levels, Enforced Agent Behavior, Goal-Driven Personal Development, Handicap Systems, Initiative, Memory of Important Events, Open Destiny, Own Agenda, Randomness, Sense of Self, Thematic Consistency, Unpredictable Behavior
Possible Closure Effects
Potentially Conflicting With
with Multiplayer Games
with Gameplay Mastery or without Diegetic Consistency
without Open Destiny
New pattern created for this wiki by Staffan Björk.
- Wikipedia entry for MUDs.
- Wikipedia entry to LPMUDs.
- Lankoski, P. & Björk, S. (2007). Gameplay Design Patterns for Believable Non-Player Characters. DiGRA 2007 Conference.
- Wikipedia entry for the ELIZA program.
- Eladhari, M. & Lindley, C. A. Player Character Design Facilitating Emotional Depth in MMORPGs. Level Up Conference Proceedings. Utrecht: University of Utrecht, November, 2003.
- Wikipedia entry for Aimbots.