Difference between revisions of "Algorithmic Agents"

Revision as of 09:04, 18 November 2010

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.

Examples

Already most of the earliest computer games, including 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 thees 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^[1] such as 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, e.g. through Action Programming or actual coding.

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. 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 Narration Structures which may in turn cause Surprises or as effects of players' actions through use of the pattern Actions Have 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.

However, players may not perceived Enemies, Companions, or NPCs as Agents if their behavior is too predictable^[2] even if the entities diegetically convey that relation and behavior. Instead, players may reduce them to Converters, Containers, Obstacles, or Traps. Although this may be impossible to hinder if players achieve Game Mastery or simply play long enough, there are many ways to make this take longer time. Initiative 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. Emotional Attachment, 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. 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. 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 Ambiguous Responses (the latter famously used for the same reason in the computer program ELIZA^[3]).

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^[4] for affecting the behavior Characters in general, and exists in FPS games through aimbots^[5]

Diegetic Aspects

Algorithmic Agents do not themselves exist in a Game World but since they are typically tied closely to specific Agents, 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.

Interface Aspects

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.

Narrative Aspects

Algorithmic Agents are necessary if NPCs are to be used in a Narration Structure, and in this case it is important that they maintain the game's Diegetic Consistency if one aims at evoking an Emotional Attachment to the unfolding storyline.

Consequences

Algorithmic Agents are powerful tools that can make sure a game includes Conflict, 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. 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. Of course, Multiplayer Games can be turned into Zero-Player Games by the same approach, although this is typically only done when players of a Meta Game can create the Algorithmic Agents. 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.

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. 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. 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 Player Influence. This allows programming games such as Crobots and P-Robots to let players have indirect Conflict with each other in the sense that they try to create Algorithmic Agents that are in Conflict with each other (due to Elimination goals) but that these players themselves do not perform confrontational actions.

Drop-In/Drop-Out

Relations

Can Instantiate

Agents, Mules Conflict Cooperation AI Players NPCs Companions Enemies Converters, Containers, Obstacles, or Traps

Can Modulate

Units Avatars

Can Be Instantiated By

Can Be Modulated By

Initiative Awareness of Surroundings Contextualized Conversational Responses Emotional Attachment Own Agenda Sense of Self Goal-Driven Personal Development Open Destiny Ambiguous Responses Unpredictable Behavior

Possible Closure Effects

Stopping to consider Algorithmic Agents as Agents may cause the removal of Emotional Attachment

Potentially Conflicting With

History

New pattern created in this wiki.

References

1. ↑ Wikipedia entry for MUDs.
2. ↑ Lankoski, P. & Björk, S. (2007). Gameplay Design Patterns for Believable Non-Player Characters. DiGRA 2007 Conference.
3. ↑ Wikipedia entry for the ELIZA program.
4. ↑ Eladhari, M. & Lindley, C. A. Player Character Design Facilitating Emotional Depth in MMORPGs. Level Up Conference Proceedings. Utrecht: University of Utrecht, November, 2003.
5. ↑ Wikipedia entry for Aimbots.

Acknowledgments

Karl-Petter Åkesson