Artificial Intelligence in
Game Design
Decision Making
•
Based on internal and external information
•
Defines a current action
(“eat food”)•
Changes world and internal state
“I am hungry” “There is food nearby”
“Intelligent Agent” Approach
• Reflex agent
No memory, just “if-then” rules
• Memory-based agent
Also based on current state
• Goal-based agent
Chooses actions that best help meet current goal
• Utility-based agent
Balances multiple weighted goals, choosing actions that give best
overall state Sims
Goal-based planning
input rules action
input rules action
Reflex Agents
• Example: “
orc
” reflex agent
if hitPoints < 5
then run away from player if distance to player < 2 units then attack player
if player visible
then run towards player else
move in random direction
Internal state
Scripted Actions
•
Visible actions usually limited to set
of animations created in advance
•
Often sequence of
actions (“
scripting
”)
Game AI
Self destruct sequence
Game Engine
Reflex Agents
•
Must consider cost of gathering inputs
if hitPoints < 5
then run away from player
if distance to player < 2 units
then attack player
if player visible
then run towards player
else
move in random direction
This requires complex computations
Hierarchical Rules
•
Actions often hierarchical
if hitPoints < 5
then moveTowards(player) if distance to player < 2 units then attack(player)
if player visible
then moveTowards(player) else
moveTowards(random)
Hierarchical Rules
•
Lowest level = actions supported by game engine
void moveTowards(object) if not facing object
then turn towards object
else if facing obstacle at distance < 2 units then turn right or left at random
Designing Hierarchies
•
Strategy Level
– What goal does character attempt to meet?
• Build, attack, negotiate
•
Tactical Level
– How does character meet current goal?
• Which city to attack
• Which resources to use
•
Motion Level
– What action does character take this turn?
Designing Hierarchies
• Task based
– Major tasks NPC performs
• Guard
• Patrol
• Defend
– Steps in tasks
• Wait
• Chase
• …
– …
– Motion
• Turn
• Move
• …
High level character design team (works with game designers)
Character motion team
Level of Detail in Graphics
•
Only render graphics at detail necessary for
what player can see
– Close up = full detail
– Far away = little detail
Level of Detail in AI
•
Only use full AI when necessary
– Fast approximations in other situations
– Often when NPCs “off screen” Use full passing AI
for cars visible to player
Simple rule for cars not visible to player
“Faster car has 75% chance of
Swarm Intelligence
•
Simple NPCs in groups can appear to cooperate
• Decision example:
if no other player shooting, I shoot if in open, run for cover and shout if in cover, reload and wait
• Orc motion example:
…
if NPC blocking path to player then run sideways
else run towards player
…
Randomness in Games
•
Randomness adds to playability
– Characters less predictable, more “realistic” – Greater replayability
– Rare occurrences can surprise player
•
Example: choice of weapon in Fight state
Randomness in Games
•
Goal: Randomness should not appear random
• Best solution: Randomness used to “tweak”
existing rules
if hitPoints < 5
then run away from player else if hitPoints < 10
and random# < 0.5 then run away from player else attack player
Unpredictable behavior in borderline situations
Potential Problems with Randomness
•
Possibly many parameters to tweak
– How do we know these are the best probabilities to use?
•
Difficult to thoroughly test
– Combinations of probabilities may create a bad case (i.e. easy win) which is possible but very unlikely
– That case will be found by some player
Confident Angry Frightened
Attack Left 40% 60% 30%
Attack Right 40% 35% 20%
Prioritization
Problem:
•
Multiple rules may fire with contradictory actions
– Problem if rules added by multiple developers (side effects)
if hitPoints < 5
then run away from player if distance to player < 2 units then attack player
What if next to player and have low hit
Decision Trees
•
Simple tree traversal
– Node = question
– Branch to follow = answer
– Leaf = final action to take
Hit points < 5? yes no Obstacle between
myself and player? yes
hide
no
run
Decision Trees
•
Not necessarily binary
•
Can merge branches
Hit points HP < 5
light run away
Within one unit of player? heavy
attack
HP ≥ 10
yes hold position no run towards player Weapon type
Designing Decision Trees
• Identify possible actions (leafs)
• Identify internal/external knowledge for choosing action
• Order questions in tree:
– Crucial factors (health, etc.)
– Ease of gathering knowledge (fastest first)
yes Player visible? no attack yes hide patrol HP < 5?
no yes Player visible? no attack yes hide patrol HP < 5?
Randomness in Decision Trees
•
Make decisions at some nodes based on
random number
Math.random() < 0.5 yes
defend
no
Math.random() < 0.2 yes
Swing sword from left
no
Randomness in Decision Trees
• Problem:
Randomness in high level decisions
• Stands still or patrol for a few frames at a time
• Better if it decided to do one and keep doing it for a while
Math.random() < 0.4 yes
Stand still
no
Timeouts in Decision Trees
• Execute decisions for specific time
• Problem: must have some way to interrupt actions if world changes
Math.random() < 0.4 yes
Stand still For 10 min
no
