creating emergent gameplay with autonomous agents

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Creating Emergent Gameplay with Autonomous Agents Borut Pfeifer

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Creating Emergent Gameplay with Autonomous Agents. Borut Pfeifer. 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). - PowerPoint PPT Presentation

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Page 1: Creating Emergent Gameplay with Autonomous Agents

Creating Emergent Gameplay with Autonomous Agents

Borut Pfeifer

Page 2: Creating Emergent Gameplay with Autonomous Agents

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)

Page 3: Creating Emergent Gameplay with Autonomous Agents

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...

Page 4: Creating Emergent Gameplay with Autonomous Agents

Chess

6 pieces, almost infinite gameplay scenarios.

Page 5: Creating Emergent Gameplay with Autonomous Agents

Grand Theft Auto

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

GTA:Vice CityRockstar NorthRockstar Games

Page 6: Creating Emergent Gameplay with Autonomous Agents

HALO: Combat Evolved

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

HALOBungieMicrosoft

Page 7: Creating Emergent Gameplay with Autonomous Agents

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?

Page 8: Creating Emergent Gameplay with Autonomous Agents

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.

Page 9: Creating Emergent Gameplay with Autonomous Agents

Perspectives/Schema of Agent Behavior:

Agent Behavior as Game Rule

Game goals achieved by strategic application of rules.

Not because they’re “stupid” (limited senses, lack of context sensitive behavior).

– HALO: Elites are tough in melee combat, 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.

Page 10: Creating Emergent Gameplay with Autonomous Agents

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.

Page 11: Creating Emergent Gameplay with Autonomous Agents

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.

Page 12: Creating Emergent Gameplay with Autonomous Agents

Sense – Sensory Modeling

Modeling an agent’s senses -> 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).

Page 13: Creating Emergent Gameplay with Autonomous Agents

Think – Decision making

The Usual Suspects

State Machines– Easy to embody game rules as states & transitions.

– Causes state errors – enemy doesn’t know how to react to a stimulus b/c someone forgot to make it a trigger in the state he’s 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

Page 14: Creating Emergent Gameplay with Autonomous Agents

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 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.

Page 15: Creating Emergent Gameplay with Autonomous Agents

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 we’ve communicated to the player to help track what they’ve learned.

Page 16: Creating Emergent Gameplay with Autonomous Agents

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.

Page 17: Creating Emergent Gameplay with Autonomous Agents

Non-Deterministic Decision Making

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?

(or, You're all going to hate me for saying this...)

Page 18: Creating Emergent Gameplay with Autonomous Agents

Non-Deterministic Decision Making

Random decision making occasionally has uses:

• Novelty– Humor

– Variety

• Appearance of Depth – Exploit player’s 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.

Page 19: Creating Emergent Gameplay with Autonomous Agents

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.

Page 20: Creating Emergent Gameplay with Autonomous Agents

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 - [email protected]