1. Using Cellular Automata and Influence Maps in Games
Penny Sweetser
The University of Queensland

2. Overview Cellular Automata Influence Maps Grid-Based Techniques
Decision making, environmental modelling
Spread information in different ways
Simple and powerful, separately or together
Design, implementation, application to games

3. Cells Divide game world into cells
Each cell a database containing info about:
combat strength, vulnerable assets, area visibility, body count, resources, weather, passability
Cell size – accuracy / efficiency
10-20 standard units side by side

4. Influence Maps1 Strategic assessment / decision-making
Usually strategy games
Spatial representation of AI’s knowledge about the game world
Strategic perspective of game state layered over geographical
1Tozour, P. (2001) Influence Mapping. In M. Deloura (Ed.), Game Programming Gems 2. Hingham, MA: Charles River Media, Inc., pp

5. Influence Maps Influence map indicates:
where the AI’s forces are deployed
where the enemy is / most likely to be
where the “frontier” between players lies
what areas are yet to be explored
where significant battles have occurred
where enemies are most likely to attack in the future

6. Influence Maps
IM’s structure makes it possible to make intelligent inferences about:
areas of high strategic control
weak spots in an opponent’s defences
prime “camping” locations
strategically vulnerable areas
choke points on the terrain
other meaningful features that human players would choose through intuition or practice

7. Influence Maps
IM tracks variables separately for each player (multiple parallel IM’s)
Each AI keeps one IM for itself and one for every other player
Could keep one IM and let all AI’s access it (but this is cheating)

8. Influence Propagation
Once initial values given to cells, needs to be propagated
More accurate strategic perspective – current influence / potential influence
Spread influence with “falloff” rule
Selection of falloff rules is subjective, requires tweaking and tuning
Exponential falloff – choose a constant 0..1
Need to terminate falloff (never reaches 0)
Falloff should be proportional to cell size

9. Influence Propagation
Top-left:
Game state
Top-right:
Propagation
Lower-left:
Influence values
Lower-right:
Influence grey scale
2Sweetser, P. (2004) Strategic Decision-Making with Neural Networks and Influence Maps. In S. Rabin (Ed.), AI Game Programming Wisdom 2. Hingham, MA: Charles River Media, Inc., pp

10. Desirability Value
Estimates cell’s value with respect to a certain decision (e.g. where to attack)
Cells can be ranked by how good they appear for the decision
Usually calculated with weighted sum
Choose relevant variables for decision
Multiply by coefficient (roughly indicates variable’s importance for decision)
Sum all weighted variables together
Choice of variables / weights is subjective

11. Desirability Value
Variables used depends on game / design / decisions being made
Need to compensate for different units of measure (e.g. health vs. rate of fire)
Example desirability values:
attack and defence desirability, exploration, defensive asset placement, resource-collection asset placement, unit-producing asset placement, vulnerable asset placement

12. Weighted Sums for Desirability
Weighted sums are simple / transparent
But:
Choosing the relevant variables is difficult
Finding good weights is time-consuming
Important info might be lost

13. Alternative to Weighted Sums
Simulated annealing or evolutionary approaches to find weights
Neural networks:
Determine variables that most influence decision / ignore irrelevant variables
Variables are analysed in parallel, info in individual variables is not lost
Weights are determined during training

14. Neural Networks in IM’s2
Computational complexity
Number of inputs and weights
But don’t need to analyse whole map
Train before shipping
Different AI personalities / strategies
Learn to mimic human players
2Sweetser, P. (2004) Strategic Decision-Making with Neural Networks and Influence Maps. In S. Rabin (Ed.), AI Game Programming Wisdom 2. Hingham, MA: Charles River Media, Inc., pp

15. Cellular Automata in Games
Proposed as a solution to static environments in games3
More dynamic / realistic behaviour of scripted elements – fire, water, explosions, smoke, heat
Conducting research into using CA in games for environmental modelling
3Forsyth, T. (2002) Cellular Automata for Physical Modelling. In D. Treglia (Ed.), Game Programming Gems 3. Hingham, MA: Charles River Media, Inc.

16. Cellular Automata Research
No research or implementation of CA in games
Are CA appropriate for use in games?
Can CA facilitate emergent gameplay?
What effect will this have on the player?

17. Cellular Automata - Traditional
Spatial, discrete time model
Space represented as uniform grid
Each cell has a state (from a finite set)
Time advances in discrete steps
Each step, cells change state according to a set of rules
New state = function of previous state of the cell and state of neighbour cells

18. Cellular Automata - Traditional
1D – single line of cells, 2 neighbours
2D – 4 or 8 neighbours 1 2 1 2

19. Cellular Automata in Games
States are continuous (not discrete)
E.g. heat =
States have multiple variables
E.g. heat, pressure, water
Rules are continuous
Damage = temp * burning rate

20. CA in Games Research4 Environmental systems Heat and Fire
Rain and Fluid Flow
Pressure and Explosions
Integrated System
4 Sweetser, P. & Wiles, J. (unpublished) Using Cellular Automata to Facilitate Emergence in Game Environments. Submitted to the Journal of Game Development.

21. CA and IMs in Games
Cellular automata and influence maps can be integrated
Values generated by CA used for decision-making by influence map
E.g. AI can consider environmental factors when making a decision

22. CA & IM in Games Research
Agents used CA and IM to determine how to react to the environment
Agents use the cellular automata values to determine “comfort”
Added a goal (desirability)
Desirability of goal is propagated

23. Conclusion Grid-based techniques Advantages Disadvantages
Cellular Automata
Influence Maps
Advantages
Allow type of behaviour to be specified
Disadvantages
Lots of tuning / testing to get desired behaviour

24. References
Forsyth, T. (2002) Cellular Automata for Physical Modelling. In D. Treglia (Ed.), Game Programming Gems 3. Hingham, MA: Charles River Media, Inc.
Sweetser, P. (2004) Strategic Decision-Making with Neural Networks and Influence Maps. In S. Rabin (Ed.), AI Game Programming Wisdom 2. Hingham, MA: Charles River Media, Inc., pp
Sweetser, P. & Wiles, J. (unpublished) Using Cellular Automata to Facilitate Emergence in Game Environments. Submitted to the Journal of Game Development.
Tozour, P. (2001) Influence Mapping. In M. Deloura (Ed.), Game Programming Gems 2. Hingham, MA: Charles River Media, Inc., pp