using cellular automata and influence maps in games penny sweetser the university of queensland
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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
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
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 geographical1Tozour, P. (2001) Influence Mapping. In M. Deloura (Ed.), Game
Programming Gems 2. Hingham, MA: Charles River Media, Inc., pp. 287-297.
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
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
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)
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
Influence Propagation
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.
Top-left: Game state
Top-right: Propagation
Lower-left: Influence values
Lower-right: Influence grey scale
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
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
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
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
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. 439-446.
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.
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?
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
Cellular Automata - Traditional
1D – single line of cells, 2 neighbours
2D – 4 or 8 neighbours
1
2
1 2
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
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.
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
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
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
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.
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