Algorithmic Agents - Revision history

Staffan Björk: /* Narrative Aspects */

2016-08-12T12:27:00Z

‎Narrative Aspects

Staffan Björk at 14:01, 26 October 2015

2015-10-26T14:01:48Z

Staffan Björk at 10:15, 11 August 2015

2015-08-11T10:15:08Z

Staffan Björk at 07:52, 8 August 2015

2015-08-08T07:52:31Z

Staffan Björk at 16:17, 25 July 2015

2015-07-25T16:17:21Z

Staffan Björk: /* History */

2014-09-21T07:30:41Z

‎History

Staffan Björk at 13:01, 3 September 2014

2014-09-03T13:01:37Z

Staffan Björk at 13:00, 3 September 2014

2014-09-03T13:00:55Z

Staffan Björk at 19:46, 14 August 2014

2014-08-14T19:46:14Z

Staffan Björk: /* Using the pattern */

2014-07-31T18:31:58Z

‎Using the pattern

@@ Line 27: / Line 27: @@
 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 ===
+=== Narration Aspects ===
 [[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.

@@ Line 17: / Line 17: @@
 However, players may not perceived [[Enemies]], [[Companions]], or [[NPCs]] as [[Agents]] if their behavior is too predictable<ref name="Lankoski2007"/> 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. 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]]<ref name="ELIZA"/>).
+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]]<ref name="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<ref name="Eladhari"/> for affecting the behavior [[Characters]] in general, and exists in FPS games through ''aimbots''<ref name="Aimbot"/>
@@ Line 70: / Line 70: @@
 ==== with [[Creative Control]] and [[Meta Games]] ====
 [[No Direct Player Influence]]
 ==== with [[Mutual Goals]] or [[Continuous Goals]] and [[Supporting Goals]] ====

@@ Line 37: / Line 37: @@
 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 Curves|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 Games|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 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]].
+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]].
@@ Line 51: / Line 51: @@
 [[Meta Games]], [[Mules]],
 [[Narration Structures]],
-[[No Player Influence]],
+[[No Direct Player Influence]],
 [[Own Agenda]],
 [[Player Created Game Elements]],

@@ Line 15: / Line 15: @@
 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 Agenda|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 Goals| Continuous]] and [[Supporting Goals]] in relation to the players' goals. [[Enemies]] and [[Companions]] may both be [[NPCs]] but these may also have their [[Own Agenda|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<ref name="Lankoski2007"/> 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 [[Game 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.
+However, players may not perceived [[Enemies]], [[Companions]], or [[NPCs]] as [[Agents]] if their behavior is too predictable<ref name="Lankoski2007"/> 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. 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]]<ref name="ELIZA"/>).
@@ Line 114: / Line 114: @@
 [[Social Interaction]]
-==== with [[Game Mastery]] or without [[Diegetic Consistency]] ====
+==== with [[Gameplay Mastery]] or without [[Diegetic Consistency]] ====
 [[Emotional Engrossment]]

@@ Line 37: / Line 37: @@
 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 Curves|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 Games|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 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 [[Elimination]]) 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]].
+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 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]].

@@ Line 121: / Line 121: @@
 == History ==
-New pattern created in this wiki.
+New pattern created for this wiki by [[User:Staffan Björk|Staffan Björk]].
 == References ==

@@ Line 91: / Line 91: @@
 === Can Be Modulated By ===
-[[Actions Have Diegetically Social Consequences]], [[Ambiguous Responses]], [[Awareness of Surroundings]], [[Contextualized Conversational Responses]],
+[[Actions Have Diegetically Social Consequences]], [[Ambiguous Responses]], [[Awareness of Surroundings]],
 [[Difficulty Levels]],
 [[Enforced Agent Behavior]], [[Goal-Driven Personal Development]],

@@ Line 15: / Line 15: @@
 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 Agenda|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 Goals| Continuous]] and [[Supporting Goals]] in relation to the players' goals. [[Enemies]] and [[Companions]] may both be [[NPCs]] but these may also have their [[Own Agenda|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<ref name="Lankoski2007"/> 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 [[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. [[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]]).
+However, players may not perceived [[Enemies]], [[Companions]], or [[NPCs]] as [[Agents]] if their behavior is too predictable<ref name="Lankoski2007"/> 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 [[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. [[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. 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]]<ref name="ELIZA"/>).
@@ Line 47: / Line 47: @@
 [[Actions Have Diegetically Social Consequences]], [[Agents]], [[Challenging Gameplay]], [[Complex Gameplay]], [[Creative Control]], [[Enemies]], [[Enforced Agent Behavior]], [[Game Element Insertion]],
 [[Interruptibility]],
-[[Limited Planning Ability]], [[Meta Games]], [[Mules]],
+[[Limited Planning Ability]],
 [[Narration Structures]],
 [[No Player Influence]],
 [[Player Created Game Elements]],
 [[Predetermined Story Structures]],
 [[Predictable Consequences]],
 [[Tactical Planning]]
@@ Line 91: / Line 95: @@
 [[Enforced Agent Behavior]], [[Goal-Driven Personal Development]],
 [[Handicap Systems]],
-[[Initiative]], [[Open Destiny]], [[Own Agenda]], [[Randomness]], [[Sense of Self]],
+[[Initiative]],
 [[Thematic Consistency]],
 [[Unpredictable Behavior]]

@@ Line 13: / Line 13: @@
 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|Own Agenda]]. 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 Goals| Continuous]] and [[Supporting Goals]] in relation to the players' goals. [[Enemies]] and [[Companions]] may both be [[NPCs]] but these may also have their [[Own Agenda|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.
+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 Agenda|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 Goals| Continuous]] and [[Supporting Goals]] in relation to the players' goals. [[Enemies]] and [[Companions]] may both be [[NPCs]] but these may also have their [[Own Agenda|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<ref name="Lankoski2007"/> 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 [[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. [[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]]).