robin hunicke northwestern university 7.26.04aaai workshop challenges in game ai ai for dynamic...

7.26.04 AAAI WorkshopChallenges in Game AI

Robin HunickeNorthwestern University

AI for Dynamic Difficulty in Games



Early work

Interactive installations Stories that users participate in Interacting as expression



Fall 1999

AAAI Narrative Intelligence Symposium Students from Parsons/NYU RE:Play



The Wheel

Set in a prison Rigid structure? Narrative – monolog & notes Factional

Actions result in global change Persistence = consequence Consequence = drama!



Too Big

Lots of great ideas Erving Goffman Character and Personality Disorders NPR for emotional impact

Needed to graduate someday



2000

GDC Flexbot Build a mod… “Fix” multiplayer experience



Ghostbot

Gladiator fiction Patrons donate items to favorites As long as you have one… …you can live!



Sucked!

Bots: “Like White On Rice” Novice players

Live longer … seemingly forever



2001

Game Design & Tuning Workshop MDA of Multiplayer

Challenge Sensation Drama



Oops!

Knee-capped multiplayer Removed challenge Rewarded failures Flattened the tension arc

…probably easy for experts to “game”



Design Tools at Work



Multiplayer Revisited

Fun because it’s people Training up = part of the challenge Frustration is still a factor Why do we keep playing?



Challenge

Too Difficult

Too Easy

FlowChannel

LowSkill

HighSkill

increasing skill

incr

easi

ng

ch

alle

ng

e

M. Csikszentmihalyi



Training

New tasks

Simple Goals

FlowChannel

Beginner skills

Skill increase

increasing skill

incr

easi

ng

ch

alle

ng

e



Tennis

Backhand?

Beat Your Boss?

FlowChannel

Hit the ball?

Volley for 3 hours in hot sun?

increasing skill

incr

easi

ng

ch

alle

ng

e



Engagement = Cyclical

Each challenge = new flow channel trip Understand and control those cycles?



Opponent Process Theory

Comfort (steady state) is discouraged Sitting cross-legged “Pins and Needles”

Applied to vision, emotion, body chemistry



Solomon & Corbit



Failure

Success

10sec.

10min.

1hr.

10hrs.time



Flux vs. Flow

Regulating this feedback Engineering chaos, then calm Keeps the player “engaged”



Game Difficulty

Typically static at run-time Flow happens – but only for a niche



Experiencing Flow

How can we expand the flow channel? Include “just folks” and “gamers” alike? How can AI help?



Adjusting Game Difficulty



FPS as a Base Case

Obstacle

Enemy

Search

Object

Spawn

Win (or retreat)

Die

Fight

FindFind

Solve (or not)

Get (or not)



Goal

Avoid “flailing”:

flailing = repeatedly edging towards a state where current means do not support necessary and immediate ends.



Inventory Theory

Flow In Flow OutInventory Level



Health



Basic Steps

Notice when a player is flailing Determine which changes can be made Execute changes as seamlessly as possible



In A Nutshell

Look at Player Inventory Current obstacles Performance over time

Generate projected probability of failure Adjust accordingly



AI View of this Process

Game Play Session

User Model

Action Planner

observations

actions

conditions



Dynamic Systems View

Game Play Session

State Estimator

Control Policy

observations

actions

states



Estimation

Estimate probability of getting hit Average measurements over time Add a fudge factor to value new estimates

over old ones



Control

Regulator keeps system within a specified range low varience

Tracking System keeps the system within a range that changes

over time high varience



And so…



Hamlet

Half Life/Case Closed mod Monitor game statistics Predict failure/death Define adjustment actions and policies Execute those actions and policies



Game Chart

Some C# and C++ widgets Display data Play session traces “Knobs” to “Frob”



Aesthetic Goals

Ramping challenge Responsive reward Sense of mastery



Existing Dynamics

Enemies increasing number increasing strength increasing accuracy increasingly variable behavior increasingly “intelligent” or tactical behavior Increased frequency of surprise attacks



Tweakable Mechanics

Available health Available ammunition Enemy Type/HP/Accuracy Weapon Type/Strength/Accuracy # of Targets Distance



Other Considerations

Terrain/Lighting/Cover Group Movement Player’s Accuracy/Progress Player’s internal state



Two Simple Policies

Novice case Expert case Scale between them?



Novice Policy

“Comfort Zone” Regulator Babysitter Trial and error

Steady supply, consistent demand



Expert Policy

“Discomfort Zone” Tracking System Boxing coach or Drill Sergeant Ramping challenge

Sporadic supply, high but erratic demand



Could you do better?

Best policies? More robust policies? Other factors?



“Even a little better is better”

10% less work

10% more fun

... or both!



Thanks

Ian & NWU crew Michael, Phoebe and NI/CTP crew Looking Glass Mafia



Questions?

robin hunicke northwestern university 7.26.04aaai workshop challenges in game ai ai for dynamic...

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