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Berkeley COMPSCI 188 - Lecture 25: Conclusion

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1CS 188: Artificial IntelligenceFall 2011Lecture 25: Conclusion12/1/2011Dan Klein – UC BerkeleyPacman Contest Challenges: Long term strategy Multiple agents Adversarial utilities Uncertainty about other agents’ positions, plans, etc.22CONTEST SLIDES HIDDEN3…and Congratulations to All! Amazing work by everyone You should all be proud of what you’ve accomplished!93Example: Starcraft10What is Starcraft?11Image from Ben Weber4Why is Starcraft Hard? Starcraft is: Adversarial Long Horizon Partially Observable Realtime Concurrent … No single algorithm (e.g. minimax) will solve it off-the-shelf12The Berkeley Overmind13Search: path planningMinimax: targetingLearning: micro controlInference: tracking unitsScheduling: resource managementHierarchical controlhttp://overmind.eecs.berkeley.edu5Search for Pathing14[Pathing]Minimax for Targeting15[Targeting]6RL for Micro Control16[RL, Potential Fields]Inference / VPI / Scouting17[Scouting, Cloaking]7Starcraft AIs: AIIDE 2010 28 Teams: international entrants, universities, research labs…18AIIDE 2010 Competition19See http://eis.ucsc.edu/StarCraftAICompetition8Pacman: Beyond Simulation?Students at Colorado University: http://pacman.elstonj.com[DEMO]Bugman? AI = Animal Intelligence? Wim van Eck at Leiden University Pacman controlled by a human Ghosts controlled by crickets Vibrations drive crickets toward or away from Pacman’s locationhttp://pong.hku.nl/~wim/bugman.htm[DEMO]219Where to go next? Congratulations, you’ve seen the basics of modern AI … and done some amazing work putting it to use! How to continue: Machine learning: cs281a / cs281b (also a 194) Cognitive modeling: cog sci 131 Vision: cs280 Robotics: cs287 NLP: cs288 Decision making: cs289 … and more; ask if you’re interested Next term: cs280 (vision) cs281b (classificiation)22That’s It! Help us out with some course evaluations Have a good break, and always maximize your expected


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Berkeley COMPSCI 188 - Lecture 25: Conclusion

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