Biologically Inspired Computing: Introduction to Evolutionary Algorithms
Survey of Biologically-inspired Algorithms in Game A/I
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Transcript of Survey of Biologically-inspired Algorithms in Game A/I
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Survey of Biologically-inspired Algorithms in Game A/I
Clint JefferyUniversity of Idaho
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Outline
Preliminary thoughts AIGPW Chapters EvoGames Papers Conclusions
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Preliminary Thoughts
ANN and related technologies are rare in commercial games
Behavior of ANN-based agents often perceived as bizarre or unrealistic
Biologically inspired algorithms (ANNs, GAs, and relatives) are nevertheless used in a surprising range of roles in games and simulations
Personal interest: want self-balancing dynamic MMOs
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AI Game Programming Wisdom
4 anthologies Not technical / academic / detailed Selected for today
Imitating Random Variations in Behavior Using a Neural Network, John Manslow
Genetic Algorithms: Evolving the Perfect Troll, F. Laramee
Constructing Adaptive AI Using Knowledge-Based NeuroEvolution, R. Cornelius et al
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Imitating Random Variations in Behavior Using a Neural Network
Tank battle, human vs. computer “although neural networks can be taught
to imitate human players…they are able to reproduce only the deterministic aspects of their behavior”
Chapter is really about augmenting ANN with random sampling
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Imitating Random Variations…Unconditional Distribution
Log difference between human error and ANN calculated optimal angle for 5000 samples
Partition 5000 samples into bins, assign probabilities to each bin
Generate new shots by selecting bin based on probability, and picking random value from the interval range of the bin
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Imitating Random Variations…Conditional Distribution
Human error events not independent: error of current shot depends on error of previous shot
Assign probabilities to bins using a standard classifier multilayer perceptron (MLP) neural network
Record 5000 samples of error + previous shot’s error
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Genetic Algorithms: Evolving the Perfect Troll
Hand-coded behavior/strategy is time-consuming, limits monster thinking
GAs to the rescue: Initialize population Test population, rank fitness Mate best performers using crossover and
mutation Add new random organisms Rinse and repeat
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Genetic Algorithms: Evolving the Perfect Troll
Complex fitness criteria Individual vs. group performance vs. co-
evolution with other species Chapter considers only individual fitness
Gene representation uses array of reals to represent troll’s bias towards 5 possible goals
Fitness determined by simulation
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Genetic Algorithms: Evolving the Perfect Troll
Reproduction rights could be reserved exclusively for “fittest” ranked individuals, or by stochastic sampling
Cross-over: many possible methods, author prefers “uniform crossover”
Mutation: probability .001 or less NextGen=top 20%, 70% children, 10%
new Population size: 100-250
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Genetic Algorithms: Evolving the Perfect Troll
5 Troll Goals: eat Sheep, kill/chase Knight, Flee from harm, Heal, Explore
Each goal gets a behavior function that is “sensible” in-game
Genome: 0.0 – 1.0 for each goal serve as weights (priority = G[goal]*need)
30x30 squares contain: havens, traps, sheep, knights, towers
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Genetic Algorithms: Evolving the Perfect Troll
Score=8*K+10*S+1.5*Age-1*Capt-2.5*Dam After 50 generations…you get trolls who
spend all their time trying to eat
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Constructing Adaptive AI Using Knowledge-Based NeuroEvolution Use Neural Networks to make NPC’s less
predictable/exploitable Preinitialize ANNs with “normal” NPC AI Convert FSM to ANN
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Constructing Adaptive AI Using Knowledge-Based NeuroEvolution
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Constructing Adaptive AI Using Knowledge-Based NeuroEvolution
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Constructing Adaptive AI Using Knowledge-Based NeuroEvolution
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Constructing Adaptive AI Using Knowledge-Based NeuroEvolution
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Constructing Adaptive AI Using Knowledge-Based NeuroEvolution
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EvoGames
Workshop on Biologically-Inspired Algorithms in Games
2011 is the 3rd year Part of Evostar.org UI CS faculty Terence Soule has been on
their program committee Criterion for mention today:
Selected interesting papers available on web
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From EvoGames 2009
Coevolution of Competing Agent Species in a Game-like Environment. Telmo Menezes, Ernesto Costa
Swarming for Games---Emergence as a Gaming Principle. Sebastian von Mammen, Christian Jacob
Evolving Teams of Cooperating Agents for Real-Time Strategy Game. Pawel Lichocki, Krzysztof Krawiec, Wojciech Jaskowski
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Telmo Menezes
http://telmomenezes.com/curriculum-vitae/phd/, Coimbra, Portugal
evoGames paper not on web, but his whole Ph.D. dissertation is…
Gridbrain, a sequentialized, von-Neumann-inspired, evolutionary computation model
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Telmo Menezes
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Swarming for Games
http://www.vonmammen.org/science/SwarmGames.pdf
2 kinds of play indirectly guide a swarm system optimize flocking parameters
Flocking formations widely used in RTS games, e.g. Lord of Magic
Leading vs. Herding
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Swarming for Games
Flocking Alignment Cohesion Separation
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From EvoGames 2010 Evolving Bot's AI in UnrealAntonio Mora, Juan Julián Merelo, et al Towards a Generic Framework for Automated Video Game Level
CreationNathan Sorenson, Philippe Pasquier Evolution of Artificial Terrains for Video Games Based on
AccessibilityMiguel Frade, F. F. de Vega, Carlos Cotta Evolving Behaviour Trees for the Commercial Game DEFCON
Chong-U Lim, Robin Baumgarten, Simon Colton Evolving 3D Buildings for the Prototype Video Game Subversion
Andy Martin, Andrew Lim, Simon Colton, Cameron Browne
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From EvoGames 2011
Towards Procedural Strategy Game Generation: Evolving Complementary Unit TypesTobias Mahlmann, Julian Togelius, Georgios N. Yannakakis
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A Plug for Dr. Soule
One of our department faculty is a specialist in this area
Check out: http://www2.cs.uidaho.edu/~tsoule/ladybug
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A Plug for Dr. Soule
http://www2.cs.uidaho.edu/~tsoule/ladybug