Chapter 5.3 Artificial Intelligence: Agents, Architecture, and Techniques.
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Transcript of Chapter 5.3 Artificial Intelligence: Agents, Architecture, and Techniques.
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Chapter 5.3Artificial Intelligence:Agents, Architecture, and Techniques
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Artificial Intelligence
Intelligence embodied in a man-made device
Human level AI still unobtainable
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Game Artificial Intelligence:What is considered Game AI?
Is it any NPC behavior? A single “if” statement? Scripted behavior?
Pathfinding? Animation selection? Automatically generated
environment? Best shot at a definition of game AI?
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Possible Game AIDefinition
Inclusive view of game AI:
“Game AI is anything that contributes to the perceived intelligence of an entity, regardless of what’s under the hood.”
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Goals of anAI Game Programmer
Different than academic or defense industry
1. AI must be intelligent, yet purposely flawed2. AI must have no unintended weaknesses3. AI must perform within the constraints4. AI must be configurable by game designers
or players5. AI must not keep the game from shipping
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Specialization ofGame AI Developer No one-size fits all solution to game AI
Results in dramatic specialization Strategy Games
Battlefield analysis Long term planning and strategy
First-Person Shooter Games One-on-one tactical analysis Intelligent movement at footstep level
Real-Time Strategy games the most demanding, with as many as three full-time AI game programmers
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Game Agents May act as an
Opponent Ally Neutral character
Continually loops through the Sense-Think-Act cycle Optional learning or remembering step
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Sense-Think-Act Cycle:Sensing Agent can have access to perfect
information of the game world May be expensive/difficult to tease out
useful info Game World Information
Complete terrain layout Location and state of every game object Location and state of player
But isn’t this cheating???
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Sensing:Enforcing Limitations Human limitations? Limitations such as
Not knowing about unexplored areas Not seeing through walls Not knowing location or state of player
Can only know about things seen, heard, or told about
Must create a sensing model
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Sensing:Human Vision Model for Agents
Get a list of all objects or agents; for each:1. Is it within the viewing distance of the agent?
How far can the agent see? What does the code look like?
2. Is it within the viewing angle of the agent? What is the agent’s viewing angle? What does the code look like?
3. Is it unobscured by the environment? Most expensive test, so it is purposely last What does the code look like?
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Sensing:Vision Model
Isn’t vision more than just detecting the existence of objects?
What about recognizing interesting terrain features? What would be interesting to an
agent?
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Sensing:Human Hearing Model Humans can hear sounds
Can recognize sounds Knows what emits each sound
Can sense volume Indicates distance of sound
Can sense pitch Sounds muffled through walls have more
bass Can sense location
Where sound is coming from
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Sensing:Modeling Hearing
How do you model hearing efficiently? Do you model how sounds reflect off
every surface? How should an agent know about
sounds?
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Sensing:Modeling Hearing Efficiently
Event-based approach When sound is emitted, it alerts
interested agents Use distance and zones to
determine how far sound can travel
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Sensing:Communication
Agents might talk amongst themselves! Guards might alert other guards Agents witness player location and
spread the word Model sensed knowledge through
communication Event-driven when agents within vicinity
of each other
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Sensing:Reaction Times
Agents shouldn’t see, hear, communicate instantaneously
Players notice! Build in artificial reaction times
Vision: ¼ to ½ second Hearing: ¼ to ½ second Communication: > 2 seconds
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Sense-Think-Act Cycle: Thinking
Sensed information gathered Must process sensed information Two primary methods
Process using pre-coded expert knowledge
Use search to find an optimal solution
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Thinking:Expert Knowledge Many different systems
Finite-state machines Production systems Decision trees Logical inference
Encoding expert knowledge is appealing because it’s relatively easy Can ask just the right questions As simple as if-then statements
Problems with expert knowledge Not very scalable
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Thinking:Search
Employs search algorithm to find an optimal or near-optimal solution
A* pathfinding common use of search
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Thinking:Machine Learning If imparting expert knowledge and
search are both not reasonable/possible, then machine learning might work
Examples: Reinforcement learning Neural networks Decision tree learning
Not often used by game developers Why?
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Thinking:Flip-Flopping Decisions
Must prevent flip-flopping of decisions
Reaction times might help keep it from happening every frame
Must make a decision and stick with it Until situation changes enough Until enough time has passed
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Sense-Think-Act Cycle:Acting Sensing and thinking steps invisible to
player Acting is how player witnesses intelligence Numerous agent actions, for example:
Change locations Pick up object Play animation Play sound effect Converse with player Fire weapon
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Acting:Showing Intelligence Adeptness and subtlety of actions
impact perceived level of intelligence Enormous burden on asset generation Agent can only express intelligence in
terms of vocabulary of actions Current games have huge sets of
animations/assets Must use scalable solutions to make
selections
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Extra Step in Cycle:Learning and Remembering
Optional 4th step Not necessary in many games
Agents don’t live long enough Game design might not desire it
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Learning
Remembering outcomes and generalizing to future situations
Simplest approach: gather statistics If 80% of time player attacks from left Then expect this likely event
Adapts to player behavior
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Remembering Remember hard facts
Observed states, objects, or players For example
Where was the player last seen? What weapon did the player have? Where did I last see a health pack?
Memories should fade Helps keep memory requirements lower Simulates poor, imprecise, selective human
memory
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Rememberingwithin the World
All memory doesn’t need to be stored in the agent – can be stored in the world
For example: Agents get slaughtered in a certain area Area might begin to “smell of death” Agent’s path planning will avoid the area Simulates group memory
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Making Agents Stupid Sometimes very easy to trounce player
Make agents faster, stronger, more accurate Sometimes necessary to dumb down
agents, for example: Make shooting less accurate Make longer reaction times Engage player only one at a time Change locations to make self more
vulnerable
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Agent Cheating Players don’t like agent cheating
When agent given unfair advantage in speed, strength, or knowledge
Sometimes necessary For highest difficultly levels For CPU computation reasons For development time reasons
Don’t let the player catch you cheating! Consider letting the player know upfront
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Finite-State Machine (FSM)
Abstract model of computation Formally:
Set of states A starting state An input vocabulary A transition function that maps inputs
and the current state to a next state
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Finite-State Machine:In Game Development
Deviate from formal definition1. States define behaviors (containing code)
Wander, Attack, Flee
2. Transition function divided among states Keeps relation clear
3. Blur between Moore and Mealy machines Moore (within state), Mealy (transitions)
4. Leverage randomness5. Extra state information
For example, health
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Most common game AI software pattern Natural correspondence between states and
behaviors Easy to diagram Easy to program Easy to debug Completely general to any problem
Problems Explosion of states Often created with ad hoc structure
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Finite-State Machine:UML Diagram
W an d er Attac k
F lee
S ee E n em y
L ow HealthN o E n em
y
N o E n em y
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Finite-State Machine:Approaches
Three approaches Hardcoded (switch statement) Scripted Hybrid Approach
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Finite-State Machine: Hardcoded FSMvoid RunLogic( int * state ) { switch( state ) { case 0: //Wander Wander(); if( SeeEnemy() ) { *state = 1; } break; case 1: //Attack Attack(); if( LowOnHealth() ) { *state = 2; } if( NoEnemy() ) { *state = 0; } break;
case 2: //Flee Flee(); if( NoEnemy() ) { *state = 0; } break; }}
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Finite-State Machine: Problems with switch FSM
1. Code is ad hoc Language doesn’t enforce structure
2. Transitions result from polling Inefficient – event-driven sometimes
better
3. Can’t determine 1st time state is entered
4. Can’t be edited or specified by game designers or players
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Finite-State Machine:Scripted with alternative languageAgentFSM{ State( STATE_Wander ) OnUpdate Execute( Wander ) if( SeeEnemy ) SetState( STATE_Attack ) OnEvent( AttackedByEnemy ) SetState( Attack ) State( STATE_Attack ) OnEnter Execute( PrepareWeapon ) OnUpdate Execute( Attack ) if( LowOnHealth ) SetState( STATE_Flee ) if( NoEnemy ) SetState( STATE_Wander ) OnExit Execute( StoreWeapon ) State( STATE_Flee ) OnUpdate Execute( Flee ) if( NoEnemy ) SetState( STATE_Wander )}
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Finite-State Machine:Scripting Advantages
1. Structure enforced2. Events can be handed as well as
polling3. OnEnter and OnExit concept exists4. Can be authored by game designers
Easier learning curve than straight C/C++
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Finite-State Machine:Scripting Disadvantages Not trivial to implement Several months of development
Custom compiler With good compile-time error feedback
Bytecode interpreter With good debugging hooks and support
Scripting languages often disliked by users Can never approach polish and robustness of
commercial compilers/debuggers
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Finite-State Machine:Hybrid Approach Use a class and C-style macros to approximate
a scripting language Allows FSM to be written completely in C++
leveraging existing compiler/debugger Capture important features/extensions
OnEnter, OnExit Timers Handle events Consistent regulated structure Ability to log history Modular, flexible, stack-based Multiple FSMs, Concurrent FSMs
Can’t be edited by designers or players
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Finite-State Machine:Extensions
Many possible extensions to basic FSM OnEnter, OnExit Timers Global state, substates Stack-Based (states or entire FSMs) Multiple concurrent FSMs Messaging
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Common Game AI Techniques
Whirlwind tour of common techniques
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Common AI Techniques:A* Pathfinding
Directed search algorithm used for finding an optimal path through the game world
A* is regarded as the best Guaranteed to find a path if one
exists Will find the optimal path Very efficient and fast
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Common AI Techniques:Command Hierarchy
Strategy for dealing with decisions at different levels From the general down to the foot
soldier Modeled after military hierarchies
General directs high-level strategy Foot soldier concentrates on combat
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Common AI Techniques:Dead Reckoning Method for predicting object’s
future position based on current position, velocity and acceleration
Works well since movement is generally close to a straight line over short time periods
Can also give guidance to how far object could have moved
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Common AI Techniques:Emergent Behavior
Behavior that wasn’t explicitly programmed
Emerges from the interaction of simpler behaviors or rules
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Common AI Techniques:Flocking Example of emergent behavior
Simulates flocking birds, schooling fish Developed by Craig Reynolds
1987 SIGGRAPH paper Three classic rules
1. Separation – avoid local flockmates2. Alignment – steer toward average
heading3. Cohesion – steer toward average position
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Common AI Techniques:Formations
Group movement technique Mimics military formations
Similar to flocking, but actually distinct
Each unit guided toward formation position Flocking doesn’t dictate goal positions
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Common AI Techniques:Influence Mapping Method for viewing/abstracting
distribution of power within game world Typically 2D grid superimposed on land Unit influence is summed into each grid
cell Unit influences neighboring cells with falloff
Facilitates decisions Can identify the “front” of the battle Can identify unguarded areas
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Common AI Techniques:Level-of-Detail AI Optimization technique like graphical
LOD Only perform AI computations if player
will notice For example
Only compute detailed paths for visible agents
Off-screen agents don’t think as often
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Common AI Techniques:Manager Task Assignment
Manager organizes cooperation between agents Manager may be invisible in game Avoids complicated negotiation and
communication between agents Manager identifies important tasks
and assigns them to agents
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Common AI Techniques:Obstacle Avoidance
Paths generated from pathfinding algorithm consider only static terrain, not moving obstacles
Given a path, agent must still avoid moving obstacles Requires trajectory prediction Requires various steering behaviors
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Common AI Techniques:Scripting Scripting specifies game data or logic
outside of the game’s source language Scripting influence spectrum
Level 0: Everything hardcodedLevel 1: Data in files specify stats/locationsLevel 2: Scripted cut-scenes (non-interactive)Level 3: Lightweight logic, like trigger systemLevel 4: Heavy logic in scriptsLevel 5: Everything coded in scripts
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Common AI Techniques:Scripting Pros and Cons Pros
Scripts changed without recompiling game
Designers empowered Players can tinker with scripts
Cons More difficult to debug Nonprogrammers required to program Time commitment for tools
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Common AI Techniques:State Machine Most common game AI software pattern Set of states and transitions, with only
one state active at a time Easy to program, debug, understand
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Common AI Techniques:Stack-Based State Machine
Also referred to as push-down automata
Remembers past states Allows for diversions, later
returning to previous behaviors
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Common AI Techniques:Subsumption Architecture Popularized by the work of Rodney Brooks Separates behaviors into concurrently
running finite-state machines Lower layers
Rudimentary behaviors (like obstacle avoidance)
Higher layers Goal determination and goal seeking
Lower layers have priority System stays robust
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Common AI Techniques:Terrain Analysis Analyzes world terrain to identify
strategic locations Identify
Resources Choke points Ambush points Sniper points Cover points
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Common AI Techniques:Trigger System Highly specialized scripting system Uses if/then rules
If condition, then response Simple for designers/players to
understand and create More robust than general scripting Tool development simpler than
general scripting
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Promising AI Techniques
Show potential for future Generally not used for games
May not be well known May be hard to understand May have limited use May require too much development
time May require too many resources
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Promising AI Techniques:Bayesian Networks Performs humanlike reasoning
when faced with uncertainty Potential for modeling what an AI
should know about the player Alternative to cheating
RTS Example AI can infer existence or nonexistence
of player build units
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Promising AI Techniques:Blackboard Architecture
Complex problem is posted on a shared communication space Agents propose solutions Solutions scored and selected Continues until problem is solved
Alternatively, use concept to facilitate communication and cooperation
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Promising AI Techniques:Decision Tree Learning
Constructs a decision tree based on observed measurements from game world
Best known game use: Black & White Creature would learn and form “opinions” Learned what to eat in the world based on
feedback from the player and world
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Promising AI Techniques:Filtered Randomness Filters randomness so that it appears
random to players over short term Removes undesirable events
Like coin coming up heads 8 times in a row Statistical randomness is largely preserved
without gross peculiarities Example:
In an FPS, opponents should randomly spawn from different locations (and never spawn from the same location more than 2 times in a row).
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Promising AI Techniques:Fuzzy Logic
Extension of classical logic In classical crisp set theory, an
object either does or doesn’t belong to a set
In fuzzy set theory, an object can have continuous varying degrees of membership in fuzzy sets
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Promising AI Techniques:Genetic Algorithms Technique for search and optimization
that uses evolutionary principles Good at finding a solution in complex or
poorly understood search spaces Typically done offline before game ships Example:
Game may have many settings for the AI, but interaction between settings makes it hard to find an optimal combination
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Promising AI Techniques:N-Gram Statistical Prediction
Technique to predict next value in a sequence
In the sequence 18181810181, it would predict 8 as being the next value
Example In street fighting game, player just did
Low Kick followed by Low Punch Predict their next move and expect it
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Promising AI Techniques:Neural Networks Complex non-linear functions that relate
one or more inputs to an output Must be trained with numerous
examples Training is computationally expensive
making them unsuited for in-game learning Training can take place before game ships
Once fixed, extremely cheap to compute
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Promising AI Techniques:Perceptrons
Single layer neural network Simpler and easier to work with
than multi-layer neural network Perceptrons get “stimulated”
enough to either fire or not fire Simple yes/no output
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Promising AI Techniques:Perceptrons (2)
Game example: Black & White Creature used perceptron for hunger Three inputs: low energy, tasty food,
and unhappiness If creature ate and received positive
or negative reinforcement, then perceptron weights were modified
Results in learning
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Promising AI Techniques:Planning Planning is a search to find a series of
actions that change the current world state into a desired world state
Increasingly desirable as game worlds become more rich and complex
Requires Good planning algorithm Good world representation Appropriate set of actions
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Promising AI Techniques:Player Modeling Build a profile of the player’s behavior
Continuously refine during gameplay Accumulate statistics and events
Player model then used to adapt the AI Make the game easier Make the game harder
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Promising AI Techniques:Production Systems Formal rule-based system
Database of rules Database of facts Inference engine to decide which rules
trigger – resolves conflicts between rules Example
Soar used experiment with Quake 2 bots Upwards of 800 rules for competent
opponent
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Promising AI Techniques:Reinforcement Learning
Machine learning technique Discovers solutions through trial and
error Must reward and punish at appropriate
times Can solve difficult or complex problems
like physical control problems Useful when AI’s effects are
uncertain or delayed
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Promising AI Techniques:Reputation System Models player’s reputation within the
game world Agents learn new facts by watching
player or from gossip from other agents Based on what an agent knows
Might be friendly toward player Might be hostile toward player
Affords new gameplay opportunities “Play nice OR make sure there are no
witnesses”
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Promising AI Techniques:Smart Terrain Put intelligence into inanimate objects Agent asks object how to use it Agents can use objects for which they
weren’t originally programmed for Allows for expansion packs or user created
objects, like in The Sims Enlightened by Affordance Theory
Objects by their very design afford a very specific type of interaction
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Promising AI Techniques:Speech Recognition
Players can speak into microphone to control some aspect of gameplay
Limited recognition means only simple commands possible
Problems with different accents, different genders, different ages (child vs adult)
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Promising AI Techniques:Text-to-Speech Turns ordinary text into synthesized
speech Cheaper than hiring voice actors Quality of speech is still a problem
Not particularly natural sounding Intonation problems Algorithms not good at “voice acting”
Large disc capacities make recording human voices not that big a problem No need to resort to worse sounding solution
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Promising AI Techniques:Weakness Modification Learning
General strategy to keep the AI from losing to the player in the same way every time
Two main steps1. Record a key gameplay state that precedes a
failure2. Recognize that state in the future and change
something about the AI behavior AI might not win more often or act more intelligently,
but won’t lose in the same way every time Keeps “history from repeating itself”