Artificial Intelligence Research in Video Games By Jacob
Artificial Intelligence Research in Video Games By Jacob Schrum [email protected] Motivation Why do research in video games? Video games Simulated, controlled, environments Complex enough to be challenging Applications Video games and non-game simulators Robotics Beyond Insight into nature of intelligence Sufficient conditions for complex behavior Super Mario AI Competition
Goal: Create skilled Mario agent Placed in random levels International competition Victory: Entry by Robin Baumgarten Uses A* Search Start-up code: http://www.marioai.org/home Video:
https://youtu.be/DlkMs4ZHHr8 Uses perfect model of game Knows result of each action Plans ahead using model Searches for safe route to end Cons: A* requires accurate model Result is skilled, but inhuman Turing Test Invented by Alan Turing Father of Computer Science Cracked Enigma code Invented Turing machine Test of human-like intelligence Chat session with computer and human Which is which? Fool humans 50% of time to pass test Turing Test for Games
Goal: Bot for UT2004 Make it human-like Fool humans 50% of time Victory: Software: http://pogamut.cuni.cz/ BotPrize: http://botprize.org/ Video: https://youtu.be/1BdcNaexk3M UT^2: http://nn.cs.utexas.edu/?ut2 UT^2 won BotPrize 2012 By Jacob Schrum, Igor Karpov, and Risto Miikkulainen Used neuroevolution and human trace data
Cons: Made bot weaker to make it convincing Does not adjust challenge level Artificial Neural Networks Brain = network of neurons ANN = abstraction of brain Neurons organized into layers Inputs Outputs Neuroevolution Example Start With Parent Population Neuroevolution Example Start With Parent Population Evaluate and Assign Fitness
100 90 75 61 56 50 31 Neuroevolution Example Start With Parent Population Evaluate and Assign Fitness Clone, Crossover and Mutate To Get Child
Population 100 90 75 61 56 50 31 Neuroevolution Example Start With Parent Population Evaluate and Assign Fitness 100
90 75 61 56 50 31 100 120 69 99 60
83 50 Clone, Crossover and Mutate Children Are Now the New Parents Repeat Process: Fitness Evaluations As the process continues, each successive population improves performance Neuroevolution Game Neuro-Evolving Robotic Operatives (NERO) By Kenneth Stanley, Bobby Bryant, and Risto Miikkulainen Goal: Make game based on Machine Learning Player is drill sergeant
Create increasingly harder tasks for evolving bots Success: Behavior evolves in real time Interactive evolution: Player manipulates environment Evolved teams can face off Software: http://nerogame.org/ Open Source Reimplementation: https://opennero.github.io Cons: Evolved agents become specialists (e.g. snipers) Need multimodal behavior Ms. Pac-Man
Goal: Develop multimodal behavior Ms. Pac-Man requires behaviors for threat and edible ghosts Evolve modular policies Success: My dissertation under Risto Miikkulainens supervision Modular neural networks Evolution discovers when to use modules Unexpected task division discovered: luring behavior Cons: What if there are many agents? What if there are many actions?
Software (MM-NEAT): http://nn.cs.utexas.edu/?mm-neat Screen capture competition: http://dces.essex.ac.uk/staff/sml/pacman/PacManContest.html Videos: http://nn.cs.utexas.edu/?ml-pm StarCraft AI Competition Goal: Handle complexity of RTS game Unit control Path finding Build order
High-level strategy Be competitive with humans Victories: Different winner each year Many strategies Student Tournament: http://sscaitournament.com/ AIIDE Competition: https://webdocs.cs.ualberta.ca/~cdavid/starcraftaicomp/
Past Competitions: http://webdocs.cs.ualberta.ca/~cdavid/starcraftaicomp/media.shtm l Hard-coded rules Finite state machines Planning Supervised learning Probabilistic models What can AI do besides agent control? Galactic Arms Race Content Creation Goal: Evolve interesting content Insert into commercial game Success:
Space shooter Weapon behavior evolves Different firing patterns Based on user popularity Interactive evolution What about going beyond a single game? Game: http://galacticarmsrace.blogspot.com/ Video: https://youtu.be/7lBmiyGkQyg
Atari Games all of them Goal: System that can play any game Only use information human has Raw pixel data Success: Googles Deep Mind team Used Deep Neural Network Can learn any Atari 2600 game Can we get more general? Video: https://youtu.be/V1eYniJ0Rnk Code: https://sites.google.com/a/deepmind.com/dqn/ General Video Game Playing Goal: Play any game Dont know the game in advance Described in formal language
Competition: Previously unseen games Many skills needed Different tracks Competition: http://www.gvgai.net/ Explanation Video: https://youtu.be/iAaleW3ofyk Planning Learning Content Generation If these topics interest you
SCOPE you should apply to SCOPE! Summer research program at Southwestern First time CS has participated Two/Three students will do research with me Get paid for your time Application deadline: November 13th, 5pm Application Link: http://www.southwestern.edu/departments/ hhmi/scope-application/ Questions? Contact me [email protected]
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