Difference between revisions of "Algorithmic Agents"

Revision as of 14:01, 8 September 2010

Agents that are described and enacted through algorithms.

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 controlled, 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 the outcomes are fixed if no outside influences interfere with them. In contrast, programming games such as Crobots and P-Robots challenge players to create better algorithms than other players.

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.

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, thereby creating AI Players.Companions can create in Teams with players, 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 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^[1] 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. Initiativeand 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 how Algorithmic Agents can

Open Destiny Ambiguous Responses Unpredictable Behavior

No Player Influence Action Programming

Mules

Surprises

Programming games such as Crobots and P-Robots let players have indirect Conflict in the sense that they try to create Algorithmic Agents that are in Conflict with each other due to Elimination goals.

Algorithmic Agents can be used to create AI Players, so that Multiplayer Games can be played with only one (or in some cases zero) players. While this make is possible to make Multiplayer Games into Single-Player Games or

Using Algorthimic Agents in conjunction with the Avatars or Units players control offers distinctly different design opportunities. If they have already been developed to be able to take the role of players when there are not enough humans, as for example in the Left 4 Dead Series, it is easy to

They can be used to support

Eladhari, M. & Lindley, C. A. Player Character Design Facilitating Emotional Depth in MMORPGs

Stimulated Planning

Diegetic Aspects

Interface Aspects

Narrative Aspects

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.

Enforced Agent Behavior

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

Potentially Conflicting With

History

New pattern created in this wiki.

References

1. ↑ Lankoski, P. & Björk, S. (2007). Gameplay Design Patterns for Believable Non-Player Characters. DiGRA 2007 Conference.

Acknowledgments

Karl-Petter Åkesson