Game Programming (Game AI Technologies)

Game ProgrammingGame Programming(Game AI Technologies)(Game AI Technologies)

Game ProgrammingGame Programming(Game AI Technologies)(Game AI Technologies)

2011. Spring

What is AI?

■ Artificial Intelligence (AI) The computer simulation of intelligent behavior What is Intelligence?

• Behavior that exhibits great ability to adapt and solve complex problems

Produce results that are completely unrealistic

• Behavior that is close to that of human Humans are not always brilliant

Game AIs are not just “problem solving robots” but lifelike entities

Roles of AI in Games

■ Opponents■ Teammates■ Strategic Opponents■ Support Characters■ Autonomous Characters■ Commentators■ Camera Control■ Plot and Story Guides/Directors

AI Entity

■ AI entity Agent

• A virtual character in the game world• Ex) enemies, NPCs, sidekicks, an animated cow in a field• These are structured in a way similar to our brain

Abstract controller• A structure quite similar to the agent, but each subsystem

works on a higher level than an individual• Ex) strategy game The entity that acts like the master

controller of the CPU side of the battle

Goals of AI action game opponent

■ Provide a challenging opponent Not always as challenging as a human -- Quake monsters. What ways should it be subhuman?

■ Not too challenging. Should not be superhuman in accuracy, precision, sensing, ...

■ Should not be too predictable. Through randomness. Through multiple, fine-grained responses. Through adaptation and learning.

Structure of an Intelligent Agent

■ Sensing: perceive features of the environment.■ Thinking: decide what action to take to achieve its

goals, given the current situation and its knowledge.■ Acting: doing things in the world.

Thinking has to make up for limitations in sensing and acting.

The more accurate the models of sensing and acting, the more realistic the behavior.

Simple Behavior

■ Random motion Just roll the dice to pick when and which direction to move

■ Simple pattern Follow invisible tracks: Galaxians

■ Tracking Pure Pursuit

• Move toward agent’s current position

• Ex) Head seeking missile Lead Pursuit

• Move to position in front of agent Collision

• Move toward where agent will be

■ Evasive – opposite of any tracking■ Delay in sensing gives different effects

Moving in the World: Path Following

■ Just try moving toward goal.

Source

Goal

Problem

Source

Goal

Create Avoidance Regions

Source

Goal

Path Finding

■ A* Algorithm find a shortest-path between two points on a map

• Starting with the base node• Expand nodes using the valid moves• Each expanded node is assigned a “score” • Iterate this process

until these paths prove invalid or one of these paths reach the target

Path Finding

■ The overall score for a state f(node) = g(node) + h(node)

• f(node): the total score (the sum of g and h)• g(node): the cost to get from the starting node to this

node( 측정치 ) A component that accounts for our past behavior

• h(node): the estimated cost to get from this node to the goal( 추정치 )

A component that estimate our future behavior The Manhattan distance (4-connected game world)

− Manhattan(p1, p2) = | p2.x – p1.x | + | p2.z – p1.z |

Ex) p1 (2,2), p2 (1,0) h = 3 Euclidean distance (8-connected)

22 ).2.1(p2.x)-(p1.x distance zpzp

Game AI

■ Game AI A* algorithm

• Quite smart, but not very realistic• Need to keep a reasonable degree of imperfection built

into the AI design A balance between generating

• behavior that is both highly evolved and sophisticated• And behavior that is more or less human

Game AIs are not just “problem solving robots” but lifelike entities

Comparison between A* and a human path finder

Execution Flow of an AI Engine

Sense

Think

Act

En

viro nm

ent

Finite-state machines

Decision trees

Neural nets

Fuzzy logic

Rule-based systems

Planning systems

Can be extremely expensive

Can be modulated by “think”

Structure of an AI System

■ Sensing the world (The slowest part of the AI) All AIs need to be aware of their surroundings

• use that information in the reason/analysis phase What is sensed and how largely depend on the type of game

• Ex) an individual enemy in Quake Where is the player and where is he looking? What is the geometry of the surroundings? Which weapons am I using and which is he using?

• Ex) master controller in a strategy game (Age of Empires) What is the balance of power in each subarea of the map? How much of each type of resource do I have? What is the breakdown of unit types: infantry, cavalry, … What is my status in terms of the technology tree? What is the geometry of the game world?


■ Memory Storing AI data is often complex

• the concepts being stored are not straightforward In an individual level AI (the less of a problem)

• Store points, orientations • and use numeric values to depict the “state” the AI is in

A master controller• These data structures are nontrivial• Ex) how do we store the balance of power, a path?

Case-by-case solutions

Can it remember prior events?For how long?How does it forget?


■ Analysis/Reasoning Core (= Think) Uses the sensory data and the memory

• to analyze the current configuration Make a decision

• Can be slow or fast depending on the number of alternatives and the sensory data to evaluate

Slow process Chess playing Fast process moving a character in Quake


■ Action/Output System Permeate actions and responses Many games exaggerate the action system

• Personality and perceive intelligence was enhanced The character’s intensions are obvious Personality is conveyed

• Ex) Super Mario Bros (creatures similar AIs)

Game ProgrammingGame Programming(Action Oriented AI)(Action Oriented AI)

Game ProgrammingGame Programming(Action Oriented AI)(Action Oriented AI)

2011. Spring

Action Oriented AI

■ Action Oriented AI Overview of AI methods used in fast-paced action games. Action: intelligent activity that involves changes of behavior

at a fast speed.• Locomotion behaviors

Ex) a character runs in Mario

• Simple aggression or defense Ex) enemies shooting or ducking in Quake

Object Tracking

■ Object Tracking Eye contact (aim at a target: 조준하기 )

• Given an orientation, a point in space Computing the best rotation to align the orientation with the

point

• Solution 2D Hemiplane Test 3D Version: Semispaces

Eye Contact: 2D Hemiplane Test

■ 2D Hemiplane Test Top-down view, overseeing the X,Z plane

point mypos; // position of the AIfloat myyaw; // yaw angle in radians. I assume top-down viewpoint hispos; // position of the moving target

The line formed by mypos and myyawX = mypos.x + cos(myyaw) * tZ = mypos.z + sin(myyaw) * t

Solving for t

(X – mypos.x)/cos(myyaw) - (Z – mypos.z)/sin(myyaw) = 0 Which side ?

F(X,Z)= (X – mypos.x)/cos(myyaw) - (Z – mypos.z)/sin(myyaw) F(X,Z) > 0 (it lies to one side of the line)F(X,Z) = 0 (it lies exactly on the line)F(X,Z) < 0 (it lies to the opposite side)

3 sub, 2 div, 1 comparison

3D version: Semispaces

■ 3D version: Semispaces Need to work with the pitch and yaw angles The equation of a unit sphere

x = cos(pitch) cos(yaw)

y = sin(pitch)

z = cos(pitch) sin(yaw)

Use two plane to detect both left-right and the above-below test

Which side?if (vertplane.eval(target)>0) yaw-=0.01;

else yaw+=0.01;

if (horzplane.eval(target)>0) pitch-=0.01;

else pitch+=0.01;

Chasing

■ Chasing Moving forward while keeping eye contact with the target

■ Chase 2D: Constant speed Depends on the relationship between

• our speed, the target’s speed, and our turning ability

void chase(point mypos, float myyaw, point hispos)

{

reaim(mypos, myyaw, hispos);

mypos.x = mypos.x + cos(myyaw) * speed;

mypos.z = mypos.z + sin(myyaw) * speed;

}

aiming and advancing

Chasing

■ Predictive chasing Not aim at the target directly but try to anticipate his movement and guess his intensions Tree step approach (instead of aiming and advancing)

1. Calculate a projected position2. Aim at that position3. Advance

void chase(point mypos, float myyaw, point hispos,point prevpos){

point vec=hispos-prevpos; // vec is the 1-frame position differencevec=vec*N; // we project N frames into the futurepoint futurepos=hispos+vec; // and build the future projection

reaim(mypos,myyaw,futurepos);mypos.x = mypos.x + cos(myyaw) * speed;mypos.z = mypos.z + sin(myyaw) * speed;

}

Evasion

■ Evasion The opposite of chasing In stead of trying to decrease the distance to the target

• Try to maximize it

void evade(point mypos, float myyaw, point hispos)

{reaim(mypos, myyaw, hispos); negated

mypos.x = mypos.x + cos(myyaw) * speed;

mypos.z = mypos.z + sin(myyaw) * speed;

}

Patrolling

■ Patrolling Store a set of waypoints that will determine the path followed by the

AI Two configurations (waypoints)

• Cyclic: W1 W2 W3 W4 W5 W1 W2 W3 W4 W5 …• Ping-pong: W1 W2 W3 W4 W5 W4 W3 W2 W1 W2 …

■ Implementation Two state finite machine

• Advance toward the next waypoint• Update next waypoint

■ Adding a third state Chase behavior

• Triggered by using a view cone for the AI• Ex) Commandos

Hiding and Taking Cover

■ Hiding and Taking Cover AIs to run for cover and remain unnoticed by the player Taking cover

• Find a good hiding spot • Move there quickly (= chase routine)

Three data items• Position and orientation of the player• Our position and orientation• The geometry of the level

Hiding and Taking Cover

Actual algorithm• Finding the closest object to the AI’s location

Using the scene graph

• Computing a hiding position for that object Shoot one ray from the player position to the barycenter of

object Compute a point along the ray that’s actually behind the

object

• Ex) Medal of Honor

Shooting

■ Shooting Infinite-Speed Targeting

• Very high speed compared to the speed of the target (virtually zero)

• Aligned with the target at the moment of shooting• Abuse infinite-speed weapon (Ex: laser gun)

Unbalance your game Solution

− The firing rate: very low− The ammunition: limited− The weapon: hard to get

Shooting

■ Real-World Targeting Shoots projectiles at a limited speed Shooting fast moving target is harder than shooting one that stand

still Finite-speed devices (sniper-type AI)

• The Still Shooter Only shoots whenever the enemy is standing still for a certain period

of time Disadvantage : Shooter will have very few opportunities to actually

shoot

• The Tracker Shoot moving target

− Compute the distance from the sniper to the target− Use projectile velocity− Predict where the target will be

Single-shot firing devices

Shooting

■ Machine Guns Fast firing rates at the cost of inferior precision Target not people but areas

• hardly ever moved• Did not have a lot of autonomy• Short firing burst

Putting It All Together

■ Putting It All Together Blend these simple behaviors into a AI system

• Parallel automata• AI synchronization

Parallel automata• The locomotion AI

Control locomotion− Chasing, evading, patrolling, hiding

• The gunner AI Evaluate the firing opportunities

− Targeting and shooting down enemies

Putting It All Together

AI Synchronization• Using shared memory • Ex) group of enemy (half-life)• Synchronization become more complex

more sophisticated interaction Better use artificial life techniques

Action Based Game

■ Platform Games Platform/jump’n run games

• Ex) Mario or Jak and Daxter AIs are not very complex

• Easily coded using finite-state machine Chasers

• Get activated whenever the player is within a certain range The turret

• A fixed entity that rotates, and when aiming at the player, shoots at him Examples

• Gorilla (in Jak and Daxter) Chase the player and kill on contact Make game too boring add chest-hitting routine Weak point can attack the gorilla whenever he is hitting his chest

• Bosses Not much different than basic chaser Complex, long-spanning choreographies Ex) Jak and Daxter: The boss flower

− Fixed to the ground− Spawn small spider become chaser

Action Based Game

■ Shooters A bit more complex than platform

• because the illusion of realistic combat must be conveyed Usually built around FSMs Need a logical way to layout the scenario

• Use a graph structure with graph nodes for every room/zone

Group behavior (Ex: Half-Life)

Action Based Game

■ Fighting Games State machine

• States: attack, stand, retreat, advance, block, duck, jump• Compute the distance to the player• Decide which behavior to trigger• Adding a timer dose not stay in the same state forever

Predictive AI• Enemy learn to detect action sequences as he fights us• FSM plus correlation approach

Higher-difficulty enemies• State-space search

Build a graph with all the movement possibilities Search optimal move sequence

• Tabulated representation A table with a simple attack moves and their associated damage Ex) Distance = 3 meters, high kick, jump, punch, Damage = 5

Action Based Game

■ Racing Titles Implemented by Rule based system

• If we are behind another vehicle and moving ->faster advance• If we are in front of another vehicle and moving slower -> block

his way• Else -> follow the track

Advance behavior• Using prerecorded trajectory• Plug-in

Analyze the track and generate the ideal trajectory

GameAIGameAI((Finite State Machines)Finite State Machines)

GameAIGameAI((Finite State Machines)Finite State Machines)

Finite State Machines

■ Finite State Machines (FSMs) Also called deterministic finite automata (DFA)

or state machine or automata Definition

• A set of states Represent the scenarios or configurations the AI can be

immersed in

• A set of transitions Conditions that connect two states in a directed way

Advantages• Intuitive to understand, Easy to code• Perform well, and represent a broad range of behaviors

Finite State Machines

Example

1. A dog is HUNGRY

2. If you give him a bone, he will not be hungry anymore

3. He’ll be QUIET after eating the bone

4. And he’ll become hungry after four hours of being quiet States (using circles) 1 and 3 Transitions (using lines) 2 and 4

HENGRY QUIET

Give him a born

After 4 hours

Example FSM

Events:

E=Enemy Seen

S=Sound Heard

D=Die

SpawnD

Wander-E, -S, -D

D

-E

E

AttackE, -D

E

-E

D

-S

ChaseS, -E, -D

S

E

D

S

Action (callback) performed when a transition occurs

Code

…

…

Problem: No transition from attack to chase

Example FSM - Better

Events:

E=Enemy Seen

S=Sound Heard

D=Die

S

E

-S

AttackE, -D, -S

E

-E

SpawnD

Wander-E, -S, -D

D

-E

ChaseS, -E, -D

D

D

S

Attack-SE, -D, S

S

-E

-S

ED

Example FSM with Retreat

SpawnD

(-E,-S,-L)

Wander-E,-D,-S,-L

E

-SAttack-EE,-D,-S,-L

E

Chase-E,-D,S,-L

S

D

S

D

Events:E=Enemy SeenS=Sound HeardD=DieL=Low Health

Each feature with N values can require N times as many statesD

Retreat-EE,-D,-S,L

L

-E

Retreat-S-E,-D,S,L

Wander-L-E,-D,-S,L

Retreat-ESE,-D,S,L

Attack-ESE,-D,S,-L

E

E-E

-L

S

-S

L

-E E

L-L

-L

-L

L

D

Hierarchical FSM

■ Expand a state into its own FSM

Wander

Die

S/-S

E/-E

Attack

Chase

Spawn

StartTurn Right

Go-throughDoor

Pick-upPowerup

Non-Deterministic HierarchicalFSM (Markov Model)

Attack

Die

No enemy

Wander

Start

Start

Approach

Aim & Jump &Shoot

Aim & Slide Left& Shoot

Aim & Slide Right

& Shoot .3.3

.4

.3.3

.4

Spring 200549

FSM Evaluation

■ Advantages: Very fast – one array access Can be compiled into compact data structure

• Dynamic memory: current state

• Static memory: state diagram – array implementation Can create tools so non-programmer can build behavior Non-deterministic FSM can make behavior unpredictable

■ Disadvantages: Number of states can grow very fast

• Exponentially with number of events: s=2e

Number of arcs can grow even faster: a=s2

Hard to encode complex memories

Decision TreesDecision TreesDecision TreesDecision Trees

Classification Problems

■ Task: Classify “objects” as one of a discrete set of “categories”

■ Input: set of facts about the object to be classified Is today sunny, overcast, or rainy Is the temperature today hot, mild, or cold Is the humidity today high or normal

■ Output: the category this object fits into Should I play tennis today or not? Put today into the play-tennis category or the no-tennis

category

Example Problem

■ Classify a day as a suitable day to play tennis Facts about any given day include:

• Outlook = <Sunny, Overcast, Rain>• Temperature = <Hot, Mild, Cool>• Humidity = <High, Normal>• Wind = <Weak, Strong>

Output categories include:• PlayTennis = Yes• PlayTennis = No

■ Outlook=Overcast, Temp=Mild, Humidity=Normal, Wind=Weak => PlayTennis=Yes

■ Outlook=Rain, Temp=Cool, Humidity=High, Wind=Strong => PlayTennis=No

■ Outlook=Sunny, Temp=Hot, Humidity=High, Wind=Weak => PlayTennis=No

Classifying with a Decision Tree

Outlook?

Sunny Overcast Rain

NoTemp? Wind?

Hot CoolMild

No Yes Yes

WeakStrong

Yes No

Decision Trees

■ Nodes represent attribute tests One child for each possible value of the attribute

■ Leaves represent classifications■ Classify by descending from root to a leaf

At root test attribute associated with root attribute test Descend the branch corresponding to the instance’s value Repeat for subtree rooted at the new node When a leaf is reached return the classification of that leaf

■ Decision tree is a disjunction of conjunctions of constraints on the attribute values of an instance

Example FSM with Retreat

SpawnD

(-E,-S,-L)

Wander-E,-D,-S,-L

E

-SAttack-EE,-D,-S,-L

E

Chase-E,-D,S,-L

S

D

S

D

Events:

E=Enemy

S=Sound

D=Die

L=Low Health

Each new feature can double number of states

D

Retreat-EE,-D,-S,L

L

-E

Retreat-S-E,-D,S,L

Wander-L-E,-D,-S,L

Retreat-ESE,-D,S,L

Attack-ESE,-D,S,-L

E

E-E

-L

S

-S

L

-E E

L-L

-L

-L

L

D

Decision Tree for Quake

■ Input Sensors: E=<t,f> L=<t,f> S=<t,f> D=<t,f>■ Categories (actions): Attack, Retreat, Chase, Spawn, Wander

D?

Spawn E?

L? S?

Wander

Retreat

Attack

L?

t

t

t t

f

f

f f

Retreat

Chase

t f

Decision Tree Evaluation

■ Advantages Simpler, more compact representation Easy to create and understand

• Can also be represented as rules Decision trees can be learned

■ Disadvantages Decision tree engine requires more coding than FSM Need as many examples as possible Higher CPU cost Learned decision trees may contain errors

Rule-based SystemsRule-based Systems(Production Systems)(Production Systems)Rule-based SystemsRule-based Systems(Production Systems)(Production Systems)

Rule Systems

■ Finite State Machine Not easy to describe in terms of states and transitions

■ FSMs are well suited for behaviors that are Local in nature

• While we are in a certain state, only a few outcomes are possible

Sequential in nature• We carry out tasks after other tasks depending on certain

conditions

Rule Systems

Ex) virtual dog• If there’s a bone nearby and I’m hungry, I’ll eat it• If I’m hungry (but there is no bone around), I’ll wander• If I’m not hungry, but I’m sleepy, I will sleep• If I’m not hungry and not sleepy, I will bark and walk

Eat Wander

SleepBark &Walk

Not local all sates can yield any other stateThere are no visible sequencesPrioritized, global behavior rule systems

Rule Systems

■ Rule Systems Provide a global model of behavior is better when we need to model behavior that is based on

guidelines A set of rules that drive our AI’s behavior

Condition Action• LHS of the rule : condition• RHS of the rule : action

Ex) virtual dog• (Hungry) && (Bone nearby) Eat it• (Hungry) & (No bone nearby) Wander• (Not hungry) & (Sleepy) Sleep• (Not hungry) & (Not sleepy) Bark and walk

A rule closer to the top will have precedence overa rule closer to the bottom of the rule list

priority

Complete Picture

Sensors

Actions

Match

ConflictResolution

Act

Changes to Working Memory

Rule instantiations that match working memory

Selected

Rule

Rule Systems

Ex) the AI for a solider in a large squadron• If in contact with an enemy combat• If an enemy is closer than 10 meters and I'm stronger than

him chase him• If an enemy is closer than 10 meters escape him• If we have a command from our leader pending execute

it• If a friendly soldier is fighting and I have a ranged weapon

shoot at the enemy• Stay still (TRUE-> stay still)

Rule Systems

■ Coding an RS for action games A direct mapping of our rule set, in priority order, to an “if”

tree (If-then-else sentences)

if (contact with an enemy) combatelse { if (closer than 10 meters) { if (stronger than him) chase him else escape him } else { if (command from our leader pending) execute it else { if (friendly soldier is fighting and I have a ranged weapon) shoot at the enemy else stay still } } }

Rule-based System Evaluation

■ Advantages Corresponds to way people often think of knowledge Very expressive Modular knowledge

• Easy to write and debug compared to decision trees• More concise than FSM

■ Disadvantages Can be memory intensive Can be computationally intensive Sometimes difficult to debug

PlanningPlanningPlanningPlanning

Planning

■ FSMs and rule sets Very useful to model simple behavior More complex behaviors? Ex) chess

• Solve a puzzle• Decide the weakest spot in a battlefield to attack the enemy• Select the best route to attack N ground targets in a flight• Trace a path from A to B with obstacles in between

These problem require the following• Thinking in more complex terms than numbers• Planning long-term strategies

What is Planning?

■ Plan: sequence of actions to get from the current situation to a goal situation Higher level mission planning Path planning

■ Planning: generate a plan Initial state: the state the agent starts in or is currently in Goal test: is this state a goal state Operators: every action the agent can perform

• Also need to know how the action changes the current state

Two Approaches

■ State-space search Search through the possible future states that can be reached by

applying different sequences of operators

• Initial state = current state of the world

• Operators = actions that modify the world state

• Goal test = is this state a goal state

■ Plan-space search Search through possible plans by applying operators that modify

plans

• Initial state = empty plan (do nothing)

• Operators = add an action, remove an action, rearrange actions

• Goal test = does this plan achieve the goal

Two Approaches

State-space Search

Plan-space Search

Planning and Problem Solving

■ State-Space Search Exploring candidate transitions and analyzing their suitability

for reaching our goals Search the state-space to find ways to move from the initial

conditions to our goals

The key difference is that any state machine would have to be dynamic, not static

What should I do?

Shoot?Pickup? Pickup?

Self.current-health = 20 Enemy.estimated-health = 50 Powerup.type = health-pak

Self.current-weapon = blaster Powerup.available = yes

Powerup.type = Railgun

Powerup.available = yes

One Step: Pickup Railgun

Shoot?Pickup Pickup?


Self.current-weapon = Railgun Powerup.available = yes


Powerup.available = no

One Step: Shoot

ShootPickup? Pickup?


Self.current-weapon = blaster Powerup.available = yes



One Step: Pickup Health-pak

Shoot?Pickup? Pickup


Self.current-weapon = blaster Powerup.available = no



Two Step

Shoot

Pickup


Self.current-weapon = blaster Powerup.available = no



Three Step Look-ahead

Pickup

Pickup


Self.current-weapon = Railgun Powerup.available = no


Powerup.available = no

Shoot

Opponent: New problems

Shoot?Pickup? Pickup?

Pickup? Pickup?


Self.current-weapon = blaster Enemy.current-weapon = blaster Powerup.available = yes



Planning Evaluation

■ Advantages Less predictable behavior Can handle unexpected situations

■ Disadvantages Planning takes processor time Planning takes memory Need simple but accurate internal representations

Biology-Inspired AIBiology-Inspired AIBiology-Inspired AIBiology-Inspired AI

Biology-Inspired AI

■ Biology-Inspired AI Previous AI Algorithms

• Not biological concepts but computational structures Not in computer science but in biology and neurology

■ Genetic Programming Inspired by genetic theory (by Mendel) and evolution theory (by Charles Darwin)

• The fittest survive Each generation can create a new generation

• Crossover Whose DNA code will consist of combination of the parent’s

respective DNA code

• Mutation Minor degree of mutations

Genetic Operators

■ Crossover Select two points at random Swap genes between two points

■ Mutate Small probably of randomly changing each part of a gene

Example: Competition

■ 2 virtual Creatures compete to gain control of single cube the cube is placed in the center of the world creature 들은 정반대 (180 도 ) 에 위치한다 . Creatures 는 ground plane 위와 diagonal plane 뒤에서부터

시작한다 .• ground plane 과 diagonal plane 이 만나는 곳이 시작점이다 .

일정한 시간 후 cube 에 대한 지배력이 많은 것이 승자가 된다

Example: Result

< 같은 phenotype 을 가지고 경쟁 > < 녹색 상자에 가까이 가는 것이 승리 >

< 긴 limb 를 갖도록 성장 > < 경쟁자가 오지 못하도록 limb 를 사용 >

Genetic Algorithm Evaluation

■ Advantages Powerful optimization technique Can learn novel solutions No examples required to learn

■ Disadvantages Fitness function must be carefully chosen Evolution takes lots of processing

• Can’t really run a GA during game play Solutions may or may not be understandable

Why aren’t AI’s better?

■ Don’t have realistic models of sensing.■ Not enough processing time to plan ahead.■ Space of possible actions too large to search

efficiently (too high of branching factor).■ Single evaluation function or predefined subgoals

makes them predictable. Only have one way of doing things.

■ Too hard to encode all the relevant knowledge.■ Too hard to get them to learn.

Reference

■ Reference http://www.cis.cornell.edu/courses/cis3000/2011sp/top/

lectures.php http://www.cis.cornell.edu/courses/cis3000/2009sp/top/

lectures.php AI Overview and State Machines AI for Games http://ai.eecs.umich.edu/soar/Classes/494/schedule.htm

Documents

Game Programming (Game AI Technologies)