Solving Problems with Artificial
Neural Networks (ANNs)
ANN Application Areas
•
•
•
•
Game AI *
Language processing
Character recognition
Data compression
Neural Networks in Games
• Control
– Use sensor data to make smart moves (collision
detection)
• Threat assessment
– Use player units and proximity to determine threat
level
• Fight or flight
– Is the mob likely to win the fight? Acts on that data
• Anticipation
– Takes in player history to predict the players next
move, responds to best counter that move
Neural Networks in Games
• Control (robots)
– Movement
• E.g.: Colin McRae Rally
– Input: Sensor results
– Outputs: Directions to move
– http://www.youtube.com/watch?v=Es1pAg_jHX4
– http://www.onlamp.com/pub/a/onlamp/2004/09/30/AIf
orGameDev.html
Neural Networks in Games
• Threat assessment (strategy)
– Inputs: types of units and their movement
– Output: Aerial threat, a ground threat, both an
aerial and ground threat, or no threat.
http://www.onlamp.com/pu
b/a/onlamp/2004/09/30/AIf
orGameDev.html
Neural Networks in Games
• Fight or flight (persistent rpg)
– Used for mobs
– Inputs: class, group size, level, range
– Decision making process: that is, whether the creature will attack,
evade, or wander, depending on whether or not there's an enemy (a
player) in the creature's proximity.
http://www.onlamp.com/pub/a/onlamp/2004/09/30/AIforGameDev.html
Neural Networks in Games
• Anticipation (mortal kombat, streetfighter)
– Computer tell what the player is going to do based on past
attacks (low kick, low kick, high kick, punch)
– Inputs: a few previous attacks
– Outputs: next predicted attack
• Computer can then decide to block, move, punch, etc
http://www.onlamp.com/pub/a/onlamp/2004/09/30/AIforGameDev.h
tml
Challenges with Neural Network
Representation
• Choosing inputs!
• Choosing structure
• How do we evolve the individuals - how do
we mate the chromosomes/individuals?
Inputs
• Problem dependent
– Control – sensor data
– Threat assesment – troops, troop movement
– Flight or fight(mob) – player data (level,
range, grouped?)
– Anticipation – player history
Outputs
• What should the network do?
– Control – movement directions
– Threat assesment – react to opposing
player’s troops?
– Flight or fight(mob) – run or fight
– Anticipation – player’s next move
Mating
• Several different methods
– If networks have the same structure, they could
exchange weight values, or mutate current values
Mother
Father
2
1
3
4
Mating
2
3
2
1
Mother
4
4
Father
3
1
Mating
• Several different methods
– If networks have the same structure, they
could exchange weight values, or mutate
values
– Cross over/mutate nodes
– Could mutate connections and nodes
• Structure
– We can see how the different mating methods might
cause problems with different network structures
5
6
2
5
1
3
2
4
4
3
1
6
• How do we choose a structure?
– NEAT – method to mate NNs of different topologies
(connections and node arrangements)
6
5
6
2
5
1
3
2
4
4
3
1
NEAT
• Problem: Does my problem need 2 nodes
or 5 nodes, 25, 100?
• NEAT
– Starts with very little nodes (input, output)
– Evolves the necessary structure
Example: NEAT Drummer
Time
Measure
Beat
Example: NEAT Drummer
• Problem: We don’t know how to write a
drum part for a song that we made. We
want to use ANNs to make one for us.
– 1. Find a suitable network structure.
• Number of nodes
• f(x) types
– 2. Select outputs
– 2. Select inputs
Example: NEAT Drummer
Find a suitable network structure
Step 1: How many nodes should we have?
- We can choose explicitly
- Or we can use an algorithm to choose
for us (NEAT)
Example: NEAT Drummer
f1(x) =
Choose fi(x)
f2(x) =
f3(x) =
f4(x) =
f1(x)
f2(x)
f3(x)
f5(x) =
f6(x) =
f4(x)
f(x)
f5(x)
Example: NEAT Drummer
• Choosing functions
– (1/(1+e^-x) can approximate any function
when it goes through enough nodes
– What else could we choose?
– What affect would the choices have on our
overall network pattern over time?
Example: NEAT Drummer
f1(x) =
f2(x) =
f3(x) =
f4(x) =
f1(x)
f2(x)
f3(x)
f5(x) =
f6(x) =
f4(x)
f(x)
f5(x)
Example: NEAT Drummer
Choosing the outputs, we want a drum set
f(x)
f(x)
f(x)
f(x)
f(x)
f(x)
Example: NEAT Drummer
Choosing the outputs, we want a drum set
Bass Drum
Hi-Hat
f(x)
f(x)
f(x)
Snare Drum
f(x)
f(x)
f(x)