The History of AI in games is one of close integration, as games provide tightly defined spaces where Artificial Intelligence excels in. This video traces the history of that collaboration, from computers like Deep Blue and Alphago, that beat us at chess and go, to the AI director of a game like Alien Isolation. However, there is much dispute within game design as to where AI should go in the future, whether the systems we currently have just need to be expanded on, or if we need to start incorporating machine learning, procedural systems, and dynamic narrative authors. Perhaps in studying the history of AI, we can get a glimpse of where it might be going in the future. Support on Patreon https://www.patreon.com/gameoveranalyser Sources -Playing Smart Julien Togelius -Deep Blue Artificial IntelligenceVol. 134, No. 1-2 https://dl.acm.org/doi/10.1016/S0004-3702(01)00129-1 -Deep Blue https://reader.elsevier.com/reader/sd/pii/S0004370201001291?token=FBF053C61313290591FA84C43CD2388F519563A0B5F0A620C68DB0C0A16A9139518550F3AC07F9A5A7E2A2C8CB54BB27 – A survey of Monte Carlo Tree Search Methods http://www.diego-perez.net/papers/MCTSSurvey.pdf – A brief history of ai up to alphago , Andrey Kurenkov https://www.andreykurenkov.com/writing/ai/a-brief-history-of-game-ai/ – How Ai has influenced the history of gaming https://www.gamedev.net/tutorials/programming/artificial-intelligence/how-artificial-intelligence-has-shaped-the-history-of-gaming-r4782/ -The history of A.i https://sites.google.com/site/myangelcafe/articles/history_ai -Finite State Machines Theory and Implementation https://gamedevelopment.tutsplus.com/tutorials/finite-state-machines-theory-and-implementation–gamedev-11867 -A* algorithms https://ieeexplore.ieee.org/document/4082128 -Halo 2 GDC 2005 Damien Isla https://www.gamasutra.com/view/feature/130663/gdc_2005_proceeding_handling_.php -The A.I of Fear Geoff Orkin http://alumni.media.mit.edu/~jorkin/gdc2006_orkin_jeff_fear.pdf – The AI systems for left for dead Valve https://steamcdna.akamaihd.net/apps/valve/2009/ai_systems_of_l4d_mike_booth.pdf -The A.i of Alien Isolation Tommy Thompson https://www.gamasutra.com/blogs/TommyThompson/20171031/308027/The_Perfect_Organism_The_AI_of_Alien_Isolation.php -Facade Ai Micheal Mateas https://pdfs.semanticscholar.org/a5d2/af2a518e2c74761bdc3d976657ac48c9d2f8.pdf -Shadow of Mordor AI GDC talk Helping Players Hate (or Love) Their Nemesis https://www.youtube.com/watch?v=p3ShGfJkLcU -The Master Algorithm Pedro Domingoes -The Five tribes of machine learning http://worldofanalytics.be/blog/the-five-tribes-of-machinelearning [More]
GitHub: https://github.com/ShrineGames/UnityMirrorTutorials In this Unity multiplayer tutorial, we cover the building blocks of multiplayer Unity games and go over various network topologies. 0:00 Intro 0:41 Basic Building Blocks (Components) 2:15 Local Multiplayer 3:24 LAN 4:35 Direct P2P (Peer to Peer) 5:25 Player-hosted Client/Server P2P 6:46 Dedicated Server 7:35 Sample Multiplayer Game Architecture Requirements Overview 8:24 Dedicated Server Cloud Options 9:43 Player Authentication (PlayFab) 10:30 Matchmaking 12:08 Friend List 12:49 Leaderboards 13:35 Persistent Data (Database/API) 14:39 Analytics (gameanalytics.com) 14:53 Outro
Afshin Niktash helps you experience the audio recognition capabilities of the MAX78000 in a fun snake game application.
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers ❤️ Their blog post is available here: https://www.wandb.com/articles/better-paths-through-idea-space 📝 The paper “Emergent Tool Use from Multi-Agent Interaction” is available here: https://openai.com/blog/emergent-tool-use/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: – https://www.patreon.com/TwoMinutePapers – https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join  🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Bryan Learn, Christian Ahlin, Claudio Fernandes, Daniel Hasegan, Dennis Abts, Eric Haddad, Eric Martel, Evan Breznyik, Geronimo Moralez, James Watt, Javier Bustamante, John De Witt, Kaiesh Vohra, Kasia Hayden, Kjartan Olason, Levente Szabo, Lorin Atzberger, Lukas Biewald, Marcin Dukaczewski, Marten Rauschenberg, Matthias Jost,, Maurits van Mastrigt, Michael Albrecht, Michael Jensen, Nader Shakerin, Owen Campbell-Moore, Owen Skarpness, Raul Araújo da Silva, Rob Rowe, Robin Graham, Ryan Monsurate, Shawn Azman, Steef, Steve Messina, Sunil Kim, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil. https://www.patreon.com/TwoMinutePapers Splash screen/thumbnail design: Felícia Fehér – http://felicia.hu Károly Zsolnai-Fehér’s links: Instagram: https://www.instagram.com/twominutepapers/ Twitter: https://twitter.com/twominutepapers Web: https://cg.tuwien.ac.at/~zsolnai/ #OpenAI
♚ Play turn style chess at http://bit.ly/chessworld Subscribe to best Youtube Chess Video Channel : http://bit.ly/2YEqePX Game 2 featured in research paper 1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns Research paper: “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm” : David Silver,1∗ Thomas Hubert,1∗ Julian Schrittwieser,1∗ Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1 Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1 Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1 https://arxiv.org/pdf/1712.01815.pdf The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case …. Read more at: https://arxiv.org/pdf/1712.01815.pdf What is reinforcement learning? https://en.wikipedia.org/wiki/Reinfor… “Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned [More]
Never thought this day would come where I was writing my own Machine Learning Neural Network Projects… prepare to have SOME FUN! CODE IS IN PART 4: https://www.youtube.com/watch?v=g-HePO2bcTY PATREON: https://www.patreon.com/Jabrils SUBSCRIBE FOR MORE SEFD SCIENCE: http://sefdstuff.com/science Table Of Contents —– 0:00 – Intro 0:10 – My AI Story 1:58 – Starting point 2:16 – Introducing Forrest 2:35 – Discovering Forrest’s Problem 3:20 – How the joystick works 3:59 – Exploring our A.I. options 4:47 – Monster Boss Battle Course 4:53 – Recap on whats going on 5:40 – Setting up our inputs 6:30 – Our Neural Network structure & how it works 8:11 – Inputting our Neural Network into Forrest 8:56 – Conclusion Please follow me on social networks: twitter: http://sefdstuff.com/twitter instagram: http://sefdstuff.com/insta reddit: https://www.reddit.com/r/SEFDStuff/ facebook: http://sefdstuff.com/faceb Music —– Coming soon REMEMBER TO ALWAYS FEED YOUR CURIOSITY #AI #MachineLearning #gamedev
♚ Play turn style chess at http://bit.ly/chessworld 1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns Research paper: “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm” : David Silver,1∗ Thomas Hubert,1∗ Julian Schrittwieser,1∗ Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1 Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1 Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1 https://arxiv.org/pdf/1712.01815.pdf The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case …. Read more at: https://arxiv.org/pdf/1712.01815.pdf What is reinforcement learning? https://en.wikipedia.org/wiki/Reinforcement_learning “Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize [More]
If it wins does that make it the worlds best AI? NEXT LEVEL: https://www.youtube.com/watch?v=kVwkLb8zxq0&t=1s Run the AI in your browser https://code-bullet.github.io/WorldsHardestGameAI/WHG/ Check out my tutorial on genetic algorithm https://www.youtube.com/watch?v=BOZfhUcNiqk&t=2s Follow me on twitter https://twitter.com/code_bullet Become a patreon to support my future content https://www.patreon.com/CodeBullet Check out my Discord server https://discord.gg/UZDMYx5