Neural Networks

1
Neural Networks

• Slides by Megan Vasta

2
Neural Networks

• Biological approach to AI
• Developed in 1943
• Comprised of one or more layers of neurons
• Several types, well focus on feed-forward
  networks

3
Neurons
Biological
Artificial
4
Neural Network Neurons

• Receives n-inputs
• Multiplies each input by its weight
• Applies activation function to the sum of results
• Outputs result

5
Activation Functions

• Controls when unit is active or inactive
• Threshold function outputs 1 when input is
  positive and 0 otherwise
• Sigmoid function 1 / (1 e-x)

6
Neural Network Layers

• Each layer receives its inputs from the previous
  layer and forwards its outputs to the next layer

7
Simple Example Application

• Smart Sweepers
• Red mine sweepers are best generated so far
• Hit F key to switch between displays
• Every generation changes neural net weights by a
  genetic algorithm

8
Neural Network Learning

• Back-Propagation Algorithm

9
Back-Propagation Illustration
ARTIFICIAL NEURAL NETWORKS Colin Fahey's Guide
(Book CD)
10
Neural Networks and Game AI

• simplify coding of complex state machines or
  rules-based systems
• potential for AI to adapt as the game is played

• I am very confident about it as well, because I
  understand what the neural net is doing. I
  haven't just created a big mysterious black box,
  I can map out the internal workings of it.

Jeff Hannan, creator of AI for Colin McRae Rally
2.0
http//www.generation5.org/content/2001/hannan.asp

11
Colin McRae Rally 2.0

• Used standard feedforward multilayer perceptron
  neural network
• Constructed with the simple aim of keeping the
  car to the racing line

http//www.generation5.org/content/2001/hannan.asp

12
BATTLECRUISER 3000AD

• 1998 space simulation game
• 'I am a Gammulan Criminal up against a Terran
  EarthCOM ship. My ship is 50 damaged but my
  weapon systems are fully functional and my goal
  is still unresolved' what do I do?

http//www.3000ad.com/shots/bc3k.shtml
http//www.gameai.com/games.html
13
Black and White

• Uses neural networks to teach the creature
  behaviors
• Tickling increase certain weights
• Hitting reduces certain weights

14
Usefulness in Games

• Control motor controller, for example, control
  of a race car or airplane
• Threat Assessment input number of enemy ground
  units, aerial units, etc.
• Attack or Flee input health, distance to enemy,
  class of enemy, etc.
• Anticipation Predicting players next move,
  input previous moves, output prediction

http//www.onlamp.com/pub/a/onlamp/2004/09/30/AIfo
rGameDev.html
15
Influence Maps Refresher

• From section 7.1
• Creates a spatial database of game world info
• Static or dynamic

Openness Layer
Occupancy Layer
Static Cover Layer
Desirability Layer
16
Neural Networks and Influence Maps

• Usually influence map layers are combined using a
  weighted sum
• Neural network can be used instead

• Finding correct weight for each layer usually
  results in trial and error
• Choosing relevant layers can be difficult
• Potential loss of important information if
  factors cancel each other

17
Computational Complexity

• Could lead to a very large number of calculations

18
Optimizations

• Only analyze relevant portion of map
• Reduce grid resolution
• Train network during development, not in-game

19
Design Details

• Need an input for each cell in each layer of the
  influence map
• Need one output for each cell on map
• Hidden units are arbitrary, usually 10-20 with
  some guess and test to prune it

20
Different Decisions and Personalities

• One network for each decision
• Implemented as one network with a different array
  of weights for each decision
• Different personalities can have multiple arrays
  of weights for each decision

21
Training

• Datasets from gaming sessions of human vs. human
  are best
• Must decide whether training will occur in-game,
  during development, or both
• Learning during play provides for adaptations
  against individual players

22
Conclusions

• Neural networks provide ability to provide more
  human-like AI
• Takes rough approximation and hard-coded
  reactions out of AI design (i.e. Rules and FSMs)
• Still require a lot of fine-tuning during
  development

23
References

• Four Cool Ways to Use Neural Networks in Games
• Interview with Jeff Hannan, creator of AI for
  Colin McRae Rally 2.0
• Interview with Derek Smart, creator of AI for
  Battlecruiser 3000AD
• Neural Netware, a tutorial on neural networks
• Sweetser, Penny. Strategic Decision-Making with
  Neural Networks and Influence Maps, AI Game
  Programming Wisdom 2, Section 7.7 (439 46)
• Russell, Stuart and Norvig, Peter. Artificial
  Intelligence A Modern Approach, Section 20.5
  (736 48)