Using Cellular Automata and Influence Maps in Games

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

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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

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Influence Maps1

• Strategic assessment / decision-making
• Usually strategy games
• Spatial representation of AIs 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. 287-297.
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Influence Maps

• Influence map indicates
• where the AIs 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

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Influence Maps

• IMs structure makes it possible to make
  intelligent inferences about
• areas of high strategic control
• weak spots in an opponents defences
• prime camping locations
• strategically vulnerable areas
• choke points on the terrain
• other meaningful features that human players
  would choose through intuition or practice

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Influence Maps

• IM tracks variables separately for each player
  (multiple parallel IMs)
• Each AI keeps one IM for itself and one for every
  other player
• Could keep one IM and let all AIs access it (but
  this is cheating)

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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

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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.
439-446.
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Desirability Value

• Estimates cells 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
  variables importance for decision)
• Sum all weighted variables together
• Choice of variables / weights is subjective

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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

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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

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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

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Neural Networks in IMs2

• Computational complexity
• Number of inputs and weights
• But dont 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.
439-446.
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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.
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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?

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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

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Cellular Automata - Traditional

• 1D single line of cells, 2 neighbours
• 2D 4 or 8 neighbours

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Cellular Automata in Games

• States are continuous (not discrete)
• E.g. heat 657.21
• States have multiple variables
• E.g. heat, pressure, water
• Rules are continuous
• Damage temp burning rate

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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.
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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

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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

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Conclusion

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

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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.
  439-446.
• 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. 287-297.