Survey of Biologically-inspired Algorithms in Game A/I

Clint Jeffery

University of Idaho

Outline

Preliminary thoughts AIGPW Chapters EvoGames Papers Conclusions

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

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

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

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

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

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

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

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

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

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

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

Constructing Adaptive AI Using Knowledge-Based NeuroEvolution

Constructing Adaptive AI Using Knowledge-Based NeuroEvolution

Constructing Adaptive AI Using Knowledge-Based NeuroEvolution

Constructing Adaptive AI Using Knowledge-Based NeuroEvolution

Constructing Adaptive AI Using Knowledge-Based NeuroEvolution

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

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

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

Telmo Menezes

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

Swarming for Games

Flocking Alignment Cohesion Separation

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

From EvoGames 2011

Towards Procedural Strategy Game Generation: Evolving Complementary Unit TypesTobias Mahlmann, Julian Togelius, Georgios N. Yannakakis

From EvoGames 2012

Evolving Third-Person Shooter Enemies to Optimize Player Satisfaction in Real-Time, by Jose Font

Dealing with Noisy Fitness in a RTS Game Bot Design, by Mora et al

From EvoGames 2013

Generating Map Sketches for Strategy Games, by Liapis, Yannakakis, Togelius