Creating Emergent Gameplay with Autonomous Agents

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Creating Emergent Gameplay with Autonomous Agents
Borut Pfeifer
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Disclaimers and other Miscellaneous Warnings

• Not an AI programming talk...
• Not entirely a game design talk, either.
• Me
• Radical, 2003
• White Knuckle Games 2001-3
• 2 articles in Game Programming Gems 4 (one on DDA)

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What the hell is Emergent Gameplay?

• Not emergent behavior or AI.
• A large amount of gameplay experiences from a
  much smaller set of interconnected game rules.
• Examples...

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Chess

• 6 pieces, almost infinite gameplay scenarios.

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Grand Theft Auto

• Rules allow the player to explore the world and
  accomplish set gameplay goals in many ways.

GTAVice City Rockstar North Rockstar Games
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HALO Combat Evolved

• Linear experience, but emergent gameplay allows
  for dynamic situations and replayability.

HALO Bungie Microsoft
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So what?

• Emergent gameplay has advantages (but not the
  only style of gameplay)
• Less scripted play, more replayability.
• More reuse of resources (no such thing as mission
  specific gameplay or assets).
• How can we build AI to enhance this style of
  gameplay?
• What are the design issues involved?

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Perspectives/Schema of Agent Behavior

• Agent Behavior as Opponent
• AI needs to be as smart as possible to beat the
  player.
• AI's main purpose is to provide challenge
  player wins the game by beating the AI.
• Hardcore FPS AI
• Chess programs (agent behavior isn't what
  encourages emergent gameplay).
• Not as valid for emergent gameplay player wins
  the game or has fun by other means.

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Perspectives/Schema of Agent Behavior

• Agent Behavior as Game Rule
• Game goals achieved by strategic application of
  rules.
• Not because theyre stupid (limited senses,
  lack of context sensitive behavior).
• HALO Elites are tough in melee, sneak around
  them.
• GTA Avoid cops as you try to achieve ancillary
  goal
• Emergent gameplay once player learns one rule,
  he/she can apply the same rule to new contexts.
• Agent behavior needs to be learned by the player

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Perspectives/Schema of Agent Behavior

• Agent Behavior as Interface
• Player manipulates AI to achieve game goals
• (like the controller interface, but at a higher
  level).
• Understanding user's conceptual model
• visibility, mappings, feedback
• affordances - tough guys look tough
• constraints/forcing functions ex. force player
  to move by getting more accurate the longer they
  stand still.

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Designing Agents For Emergent Gameplay

• Orthogonal behavior
• Combined behaviors expands gameplay possibility
  space (chain reactions).
• Allows for a variety of player strategies.
• Teaching Behavior
• Player must understand agent behavior to learn
  gameplay rules to apply them to new situations.
• Agent's Sense Think Do cycle relates to
  player's own Sense - Think Do cycle.
• Player needs know the causes of behaviors
  (reactive).
• Behavior Archetypes - Groups of agents that
  share, and appear to share, behavior - helps
  player learn.

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Sense Sensory Modeling

• Modeling an agents senses -gt game rules
• Problems with conflicts (different sense having
  different priorities under different
  circumstances).
• Often sacrifice realism for gameplay (stealth
  games).
• Biggest area to develop for emergent gameplay AI
  - agents need to be able to handle more context
  (react to more things, and remember more things).

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Think Decision making

• The Usual Suspects
• State Machines
• Easy to embody game rules as states
  transitions.
• Causes state errors enemy doesnt know how to
  react to a stimulus b/c someone forgot to make it
  a trigger in the state hes in. Can be decreased
  with augmentations (hierarchical, parallel, stack
  based).
• Behavior/Task arbitration
• Harder to embody game rules, deals with
  conflicting contexts much better.
• Problems with priorities tasks flipping back
  and forth, hard to debug

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Think Decision making

• Phoebe Sengers - Schizophrenia and Narrative in
  Artificial Agents, Narrative Intelligence
• Agent behavior can be disjoint with no focus on
  what it is communicating.
• Yet another layer in our agent?
• For example
• Rules based system acts as a perceptual filter,
  relating sensory stimuli to responses focuses
  on what needs to be communicated to the player
• Traditional method - handles behind the scenes
  decision making, resolving conflicts between what
  the agent is doing and what it needs to be
  doing/communicating.

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Do Communicating Intent

• Must communicate behavior through
• Action (what they do)
• Movement (where they move, how they move,
  speed)
• Animation
• Sound (effects and dialog)
• Modeling/texturing
• What does the player think the agent is thinking?
• How does the player link their actions to agent
  behavior?
• Can also record what weve communicated to the
  player to help track what theyve learned.

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The Player's Mental Model

• Dealing with Causality
• Mind tends to say link event A caused event B if
  A happens just before B.
• Need restrict agent behavior to being affected
  only by direct stimulus/action (preferably the
  player's).
• What sort of strategies is the behavior enabling?
  
• What actions does it reward or what is the best
  action to deal with it?
• Increase opportunities for emergence by making
  strategies conflict or interact.
• Example from HALO Elites require stealth, Grunts
  are easier to just overpower mix them in the
  same combat and player must make decision.

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Non-Deterministic Decision Making
(or, You're all going to hate me for saying
this...)

• Bad - breaks consistency required for the player
  to learn game rules for emergence.
• For one set of input stimuli, the game needs to
  react the same way.
• How can the player learn the game if something
  different happens each time they do one
  particular thing?

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Non-Deterministic Decision Making

• Random decision making occasionally has uses
• Novelty
• Humor
• Variety
• Appearance of Depth
• Exploit players inability to reason about random
  events.
• Fakes complexity (pedestrians randomly waving to
  each other makes a city seem more involved).
• Shouldn't affect core game rules of agent
  behavior.

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Summary

• Easy to expand a gameplay space with reactive
  agent behavior. (Car swerves to avoid player,
  hits obstacle, explodes, bystanders die, cops
  come).
• To handle reactivity, more complexity is needed
  in the decision making layer, focusing on what we
  need to communicate.
• The player has to be able to learn the behavior
  it's motivations in order to use it as a rule or
  in a strategy.

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References

• Katie Salen and Eric Zimmerman - Rules of Play
  Game Design Fundamentals.
• Phoebe Sengers - "Schizophrenia and Narrative in
  Artificial Agents", Narrative Intelligence.
• Harvey Smith Randy Smith "Will the Real
  Emergent Gameplay Please Stand Up?", GDC 2004.
• Harvey Smith - "Orthogonal Unit Design", GDC
  2003.
• Harvey Smith - "Systemic Level Design", GDC
  Europe 2002.
• Chris Butcher and Jaime Griesemer - HALO AI
  Level Design. GDC 2002
• Email me - borut_p_at_yahoo.com