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Artificial Intelligence (for Games) in a Minute
 
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If you have absolutely no idea how you would go about making an AI for a game, here is a one-minute version of one of the most basic, core concepts. Hopefully this gets you in the right direction! I'm planning on many more videos on this topic, so this isn't all there is to it! If you have any interesting ideas, comments or questions, submit them here for my upcoming Q&A video: https://goo.gl/B1kc0X Twitter: https://twitter.com/TheHappieCat Facebook: https://www.facebook.com/TheHappieCat/
Views: 40101 TheHappieCat
Artificial Intelligence Research in Games [AI & Games Lecture #1]
 
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The first in a multi-part series of public lectures on AI in games. Recorded on 20th October 2014 at the University of Derby. In this first video, we detail some of the most interesting work in using video games as benchmarks within the AI research community. This is largely focussed on four competition benchmarks: - The Ms. Pac-Man Competition - The Mario AI Competition - The 2K BotPrize - The Starcraft AI Competition Full credit must be given to the following authors of videos used in this presentation: "Ms. Pac-Man AI - ICE Pambush 3" (RTNO1) https://www.youtube.com/watch?v=Zo0YujjX1PI "ICEP-IDDFS (Ms. Pac-Man) vs Memetix (Ghosts)" (RTNO1) https://www.youtube.com/watch?v=Zo0YujjX1PI "Infinite Mario AI - Long Level" (Robin Baumgarten) https://www.youtube.com/watch?v=DlkMs4ZHHr8 "Infinite Mario AI vs Hard Level" (UCSCweber) https://www.youtube.com/watch?v=V06nEHw70b4 "CIG 2010 Level Generator Entry" (UCSCweber) https://www.youtube.com/watch?v=8Eu8sRV2yd4&index=46 "BotPrize Video" (Sean Williams) https://www.youtube.com/watch?v=mUNfjMDhCpM "2012 AIIDE StarCraft AI Competition - Highlight Reel" (serendib7) https://www.youtube.com/watch?v=jYzSffdvvwo The remaining videos have all been crafted by myself. More videos and articles on work in Ms. Pac-Man and Super Mario can be found at the following links: "The Mario AI Competition": http://www.patreon.com/creation?hid=614578 "What's the Deal with Pac-Man?": http://www.patreon.com/creation?hid=614536 For those interested in a copy of the slides (without videos), they can be found at: https://t2thompson.files.wordpress.com/2015/02/part-1-ai-research-in-video-games-minus-video.pptx
Views: 17233 AI and Games
Artificial intelligence, video games and the mysteries of the mind | Raia Hadsell | TEDxExeterSalon
 
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Artificial intelligence could be the powerful tool we need to solve some of the biggest problems facing our world, argues Raia Hadsell. In this talk, she offers an insight into how she and her colleagues are developing robots with the capacity to learn. Their superhuman ability to play video games is just the start. Raia is a senior research scientist on the Deep Learning team at DeepMind, with a particular focus on solving robotics and navigation using deep neural networks. --- TEDxExeterSalon: From driverless cars to diagnosis of medical imaging, artificial intelligence is being heralded as the next industrial revolution. But how does AI relate to us in all our glorious complex humanity? Our first TEDxExeterSalon explored the ways in which we’ll interact with algorithmic intelligence in the near future. TEDxExeter: Now in our 7th year, TEDxExeter is Exeter’s Ideas Festival with global reach, licensed by TED and organised by volunteers who are passionate about spreading great ideas in our community. More information: http://www.tedxexeter.com Production: http://www.youtube.com/familygamertv Filming: http://firstsightmedia.co.uk/ Raia Hadsell is a research scientist on the Deep Learning team at DeepMind. She moved to London to join DeepMind in early 2014, feeling that her fundamental research interests in robotics, neural networks, and real world learning systems were well-aligned with the agenda of Demis, Shane, Koray, and other members of the original team. Raia’s research at DeepMind focuses on a number of fundamental challenges in AGI, including continual and transfer learning, deep reinforcement learning, and neural models of navigation. Raia came to AI research obliquely. After an undergraduate degree in religion and philosophy from Reed College, she veered off-course (on-course?) and became a computer scientist. Raia’s PhD with Yann LeCun, at NYU, focused on machine learning using Siamese neural nets (often called a ‘triplet loss’ today) and on deep learning for mobile robots in the wild. Her thesis, ‘Learning Long-range vision for offroad robots’, was awarded the Outstanding Dissertation award in 2009. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Views: 7551 TEDx Talks
16 Games With Incredible Artificial Intelligence
 
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Artificial intelligence can make or break a video game. That is no exaggeration. Good AI can elevate average game to an excellent one, while at the same time, bad AI can turn a great concept into a poorly executed mess. Immersion, challenge, thrill, enjoyment, atmosphere- there are so many things that are dependent on a game’s AI. Which is why when we say that AI is perhaps one of the most important aspects of any modern video game, you know we’re not being hyperbolic. Just like anything else in the video games industry though, there are some developers and video games that handle artificial intelligence much better than others. Here, in this list, we’ve compiled what we feel are the sixteen games with the best AI we’ve ever seen. As always, if in any way you disagree with our selections, let us know why that is via your comments below. SUBSCRIBE FOR MORE VIDEOS: https://www.youtube.com/user/GamingBoltLive LIKE US ON FACEBOOK: https://www.facebook.com/GamingBolt-Get-a-Bolt-of-Gaming-Now-241308979564/?fref=ts FOLLOW US ON TWITTER: https://twitter.com/GamingBoltTweet
Views: 118253 GamingBolt
How Games Use Artificial Intelligence To Play Hide & Seek
 
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We've always had AI systems in games, but how exactly do things work when an enemy searches for you while the computer always knows where you are in the first place? Let's talk. Subscribe for more: http://youtube.com/gameranxtv
Views: 200089 gameranx
09 Game Playing in Artificial intelligence
 
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Computers can play Games has existed as long as computers. CHARLESS Babbage build a machine to play tic-tac-toe. Arthur Samuel in 1960s succeded in building the first operational game playing program. Istead of legal move generator ,we use plausible move generator in which only some small number of promising moves are generated. as the depth of resulting tree increases,in the amount of time available , we choose most advantageous. This is done using static evaluation function.
Views: 2265 Nisha Mittal
AI Evolved - artificial intelligence for games
 
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AI-Evolved is my first working version of a new system to automatically create artificial intelligence. It uses an evolutionary algorithm to evolve a neural networks that can control any kind of character. I will use this technology in my game Testank to create opponents that behave differently in every game. While the players play the game they train the AI and thus keep producing newer, stronger AI! A ranking system will rank players and AI to make sure everyone gets an interesting opponent.
Views: 9620 because why not?
The AI Gaming Revolution
 
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Artificial intelligences that play abstract, strategic board games have come a long way, but how do their "brains" work? Hosted by: Hank Green ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters -- we couldn't make SciShow without them! Shout out to Kevin Bealer, Justin Lentz, Mark Terrio-Cameron, Patrick Merrithew, Accalia Elementia, Fatima Iqbal, Benny, Kyle Anderson, Mike Frayn, Tim Curwick, Will and Sonja Marple, Philippe von Bergen, Chris Peters, Kathy Philip, Patrick D. Ashmore, Thomas J., charles george, and Bader AlGhamdi. ---------- Like SciShow? Want to help support us, and also get things to put on your walls, cover your torso and hold your liquids? Check out our awesome products over at DFTBA Records: http://dftba.com/scishow ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: http://www.aaai.org/ojs/index.php/aimagazine/article/view/1848/1746 http://www.sciencedirect.com/science/article/pii/S0004370201001291 http://www.usgo.org/brief-history-go http://www.theatlantic.com/technology/archive/2016/03/the-invisible-opponent/475611/ http://journals.lww.com/neurosurgery/Fulltext/2016/06000/Ready_or_Not,_Here_We_Go___Decision_Making.2.aspx https://www-03.ibm.com/ibm/history/exhibits/mainframe/mainframe_PP704.html http://www.welivesecurity.com/2010/12/29/what-are-heuristics/ http://www.cs.bham.ac.uk/~jxb/IAI/w7.pdf http://www.readcube.com/articles/10.1038/nature14541?r3_referer=nature Images: https://commons.wikimedia.org/wiki/File:Chatbot.jpg https://commons.wikimedia.org/wiki/File:IBM_Electronic_Data_Processing_Machine_-_GPN-2000-001881.jpg https://commons.wikimedia.org/wiki/File:IBM_729_tape_drives.agr.jpg https://commons.wikimedia.org/wiki/File:Opening_chess_position_from_black_side.jpg https://commons.wikimedia.org/wiki/File:Kasparov-26.jpg https://commons.wikimedia.org/wiki/File:Deep_Blue.jpg https://commons.wikimedia.org/wiki/File:Go_game.jpg https://commons.wikimedia.org/wiki/File:Blank_Go_board.png https://commons.wikimedia.org/wiki/File:Chess_Board.svg https://commons.wikimedia.org/wiki/File:Antlia_Dwarf_PGC_29194_Hubble_WikiSky.jpg https://commons.wikimedia.org/wiki/File:Go_game_(2094497615).jpg https://commons.wikimedia.org/wiki/File:FloorGoban.JPG https://commons.wikimedia.org/wiki/File:Lee_Se-dol_2012.jpg
Views: 592631 SciShow
Outrageous Artificial Intelligence: (Game 1) DeepMind’s AlphaZero crushes Stockfish Chess WC
 
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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 some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality. In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs." What is this company called Deepmind ? https://en.wikipedia.org/wiki/DeepMind DeepMind Technologies Limited is a British artificial intelligence company founded in September 2010. Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7] The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film. ♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053 ►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp ►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396 ►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053 http://goo.gl/7HJcDq ►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp ►Playlists: http://goo.gl/FxpqEH ►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher
Views: 59861 kingscrusher
Artificial Intelligence in Video Games
 
01:06:46
Video game developer and Mad Doc Software founder Ian Davis lectures on interactive multimedia, game engineering, and development of characters in video games. Hosted by Metropolitan College Department of Computer Science on November 28, 2007.
Views: 12773 Boston University
MarI/O - Machine Learning for Video Games
 
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MarI/O is a program made of neural networks and genetic algorithms that kicks butt at Super Mario World. Source Code: http://pastebin.com/ZZmSNaHX "NEAT" Paper: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf Some relevant Wikipedia links: https://en.wikipedia.org/wiki/Neuroevolution https://en.wikipedia.org/wiki/Evolutionary_algorithm https://en.wikipedia.org/wiki/Artificial_neural_network BizHawk Emulator: http://tasvideos.org/BizHawk.html SethBling Twitter: http://twitter.com/sethbling SethBling Twitch: http://twitch.tv/sethbling SethBling Facebook: http://facebook.com/sethbling SethBling Website: http://sethbling.com SethBling Shirts: http://sethbling.spreadshirt.com Suggest Ideas: http://reddit.com/r/SethBlingSuggestions Music at the end is Cipher by Kevin MacLeod
Views: 6861780 SethBling
Science of Video Games: Artificial Intelligence
 
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Black Light Network Science Channel
Outrageous Artificial Intelligence (Game 2): DeepMind’s AlphaZero crushes Stockfish
 
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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 with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality. In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs." What is this company called Deepmind ? https://en.wikipedia.org/wiki/DeepMind DeepMind Technologies Limited is a British artificial intelligence company founded in September 2010. Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7] The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film. ♚Play at: http://www.chessworld.net/chessclubs/... ►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/... ►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/i... ►FREE online turn-style chess at http://www.chessworld.net/chessclubs/... http://goo.gl/7HJcDq ►Kingscrusher resources: http://www.chessworld.net/chessclubs/... ►Playlists: http://goo.gl/FxpqEH ►Follow me at Google+ : http://www.google.com/+kingscrusher
Views: 24585 kingscrusher
AI 101 - Learning Artificial Intelligence Through Video Games
 
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Welcome to the AI 101 series, which is focussed on introducing artificial intelligence methods through video games. Before we get started, I want to introduce you to the series and discuss some important things we need to cover, before it gets underway! This video accompanies the written piece on my website: http://aiandgames.com/ai-101-introduction/ No copyright is claimed for the game footage, images and music, to the extent that material may appear to be infringed, I assert that such alleged infringement is permissible under fair use principles in copyright laws. If you believe material has been used in an unauthorized manner, please contact me.
Views: 16872 AI and Games
AI ( Game AI ) tutorial 01 - What is Artificial Intelligence (AI) ?
 
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What is Artificial Intelligence ? What is AI ? NI: (Natural Intelligence) Natural intelligence is the intelligence naturally evolved in living beings in response to the level of complexity in problems, challenges, changing environment and other factors. Natural intelligence deals with intelligent behavior of living beings with respect to solving problems and evolving by them self. Ex: Humans, animals, birds etc. Natural intelligence helps in “what to do, when we don’t know what to do”? IQ test: Intelligence quotient IQ is a test, designed to assess the intelligence level of humans or sometimes animals. AI: (Artificial Intelligence) Artificial means which is not natural. AI is the branch of computer science that deals with designing intelligent machines or systems (i.e. Intelligent Agents); which simulate an intelligent behavior of living beings (especially like humans) and exhibit an ability to solve problems, and evolve themselves. Ex: Game agents (Game AI), Robotics agents, Diagnostic agents, Web agents, Trading agents, Driving agents, etc. Turing test: Mr. Alan Turing one of the computer scientists developed the Turing Test; to determine “whether or not machines can think intelligently like humans?" in 1950s. ======================================================== Follow the link for next video: https://youtu.be/SP-w-lBgUqI ========= For more benefits & Be up to date =================== Subscribe to My YouTube channel: https://www.youtube.com/chidrestechtu... Like my Facebook fan page: https://www.facebook.com/ManjunathChidre ========================================================
Views: 2793 Chidre'sTechTutorials
Artificial Intelligence for Games - Behaviors
 
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Implementation for the algorithms in Chapter 3 of "Artificial Intelligence for Games" by Millington & Funge. Code: Jorge Palacios (pctroll) Music: Ben Landis from his album Adventures in Pixels.
Views: 196 Jorge Palacios
General AI Challenge Meetup in NYC: Artificial General Intelligence through Games
 
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The Challenge meetup in New York City took place on April 5th. Dr. Julian Togelius (Game Innovation Lab, New York University) discussed the role of video games in developing general artificial intelligence.
Deepmind artificial intelligence @ FDOT14
 
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/!\ Smartphone bad audio quality Deepmind technology wrecking old Atari games, presented by neuroscientist and game developper Demis Hassabis, Deepmind CEO. (More information on this article : http://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) Event held @ First Day of Tomorrow #1, april 2014, Paris
Views: 279509 Elizr
Unity 3D - Artificial Intelligence for a Boardgame
 
02:30:09
Made possible by the supporters at: https://www.patreon.com/quill18creates Today we're adding AI to our boardgame! Make sure to SUBSCRIBE so you don't miss a video! Download the project files: https://github.com/quill18/TheRoyalGameOfUr3D Download the other projects: http://quill18.com/unity_tutorials/ Also, please feel free to ask lots of questions in the comments. This channel is mostly all about game programming tutorials, specifically with Unity 3d. You may also be interested in my primary channel, where I play and review games: http://youtube.com/quill18 I can be reached at: [email protected] http://twitter.com/quill18 http://facebook.com/quill18
Views: 8736 quill18creates
Artificial Intelligence plays Call Of Duty (with commentary)
 
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here is the original video published 3 years ago without the commentary: https://youtu.be/_dJfpvM1bd8 The most important topic regarding modern Artificial intelligence is the robot's ability to do recursive tasks. Opening a door or baking a cake or doing any human task requires recursive tasks. For example, when you open a door there are many recursive sub-tasks you must do, such as having the key, or turning the door knob. My AI program uses movie sequences called pathways to store life experiences. Therefore, recursive tasks are stored as linear possibilities. This way, I don't have to pre-define rules or goals or procedures into the robot's brain. When I filed my patents and books starting from 2006, my goal was to design a robot with human level AI. The benchmark i used to test the robot's cognitive skills and abilities is to let it play video games. If my robot can play every video game in the world, then it has achieved intelligence at a human-level. In my first patent filed in 2006 (priority), I used the popular video game, Zelda to demonstrate my robots intelligence. I chose this game because it is very complex and requires at least a 6th grade level intelligence to beat. If the robot can play Zelda it can essentially play any video game. The important thing is that I tried to demonstrate how my robot does recursive tasks. While the robot is managing multiple tasks, it's also doing other things like: 1. navigate in an unknown environment 2. attack enemies 3. generate common sense knowledge. 4. solve problems. 5. do induction and deduction reasoning. 6. do multiple recursive tasks. 7. read and understand natural language. 8. identify objects and generate logical facts. etc. The A.I. is playing this call of duty game for the first time and is using a general pathway to play the game. Since this is the first time, the robot doesn't understand the rules, goals, or procedures of the game. He uses common sense knowledge and logical reasoning to discover the objectives and rules of the game. In other words, the robot is using logical and reasoning to discover recursive tasks. This video was made over 3 years ago and this is the first time I'm trying to explain how and why the robot makes decisions in the game. And the content in this video is based on 8 patents and 5 books filed from 2006-2007. the data structure to Human-Level AI: https://youtu.be/KEyPzMi8wts website: http://www.humanlevelartificialintelligence.com tags: call of duty, ai plays call of duty, human level artificial intelligence, ai, artificial intelligence, artificial general intelligence, true ai, strong ai, human level ai, cognitive science, ai plays video game, robot plays video game, agi, digital human brain, human intelligence, human brain, human mind, human thought, ai plays role playing games, ai play rpg.
Views: 13916 electronicdave2
Minmax Algorithm in Artificial Intelligence in Hindi | Solved Example | #20
 
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Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 126938 Well Academy
Bored with your video game? Artificial intelligence could create new levels on the fly
 
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New tricks could make games not too hard, not too easy Read more - https://scim.ag/2IpzrSY CREDITS ------------------- editor/animator/narrator Chris Burns supervising producers Sarah Crespi Nguyên Khôi Nguyên story by Matthew Hutson Super Mario Bros. research footage Vanessa Volz Jacob Schrum Jialin Liu Simon M. Lucas Adam Smith Sebastian Risi Doom research footage Edoardo Giacomello Pier Luca Lanzi Daniele Loiacono Politecnico di Milano video game footage Blizzard Hello Games Mojang Pond5 music Chris Burns
Views: 3784 Science Magazine
The first artificial intelligence I ever made
 
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The first 2 minutes took longer to make than the last 15, but they're also much more watchable. The last 15 are so bad it hurts Get to the Top Although There Is No Top: http://htwins.net/gtttatint/awesome_game_dnc.html That game's AI: http://htwins.net/gtttatint/agauto.html GTTTATINT Different Platforms: http://htwins.net/gtttatint/ag-difplat.html That game's AI (my first true AI): http://htwins.net/gtttatint/ag-difplatauto.html Combo, with "proportional jumping": http://htwins.net/gtttatint/ag-combo.html My patreon: https://www.patreon.com/carykh My twitter: https://twitter.com/realCarykh Music: Prefekt - Eclipse [JompaMusic Release] https://www.youtube.com/watch?v=4Aw3WzUNDKQ JPB - High [NCS Release] https://youtu.be/Tv6WImqSuxA SoundCloud https://soundcloud.com/anis-jay Facebook https://www.facebook.com/jayprodbeatz Twitter https://twitter.com/gtaanis Instagram http://instagram.com/gtaanis Talking With You by Artificial.Music https://soundcloud.com/artificial-music Creative Commons — Attribution 3.0 Unported— CC BY 3.0 https://creativecommons.org/licenses/by/3.0/ Music provided by Audio Library https://youtu.be/NAgcAg0pW6w
Views: 331427 carykh
What is the Minimax Algorithm? - Artificial Intelligence
 
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The minimax algorithm is one of the oldest artificial intelligence algorithms ever. It uses a simple zero sum rule to find which player will win from a current position. This is arguably the most powerful and basic tool for building game playing artificial intelligence. It captures the intentions of two player games fighting against one another. Minimax is the foundation for algorithms like alpha-beta pruning and iterative deepening. Engines like deep blue use optimized versions of minimax to play chess, while other computer programs have also been successful at playing games with minimax. Source Code (Complicated, glance over the evaluate function to get a gist): https://github.com/gkcs/ChainReaction/blob/master/src/main/java/main/java/MinMax.java
Views: 21544 Gaurav Sen
Games Programming with Visual Basic lesson 5 - AI (Artificial intelligence)
 
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In this tutorial we program a function which makes the enemy object follow the player object around. Completed game can be downloaded here: http://magicmonktutorials.com/videolist.php?key1=20
Views: 5970 Magic Monk
Artificial Intelligence in Google's Dinosaur (English Sub)
 
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Link for the code: https://github.com/ivanseidel/IAMDinosaur This is a project made for my university, using a Neural Network and Genetic Algorithm to teach Google's dinosaur from Chrome to jump cactus without dying so easily. All the implementation was using Node.js, and the game was not modified to allow interaction with the game, instead, I used pixel readings and virtual key presses from Node.js. Presentation: I normally use Apple's Keynote to make presentations and record it livelly with my screen. Music: It's my own composition and improvisation. Link: https://soundcloud.com/ivan-seidel/at-night-with-headphones
Views: 1133355 Ivan Seidel
Artificial Intelligence for Games
 
57:13
For the module Subjects in Computer Science Presented by Dr. Tommy Thompson
Views: 3817 [email protected]
Programming Artificial Intelligence For Games - A Student's Perspective
 
22:48
My talk about Artificial Intelligence at SGA Conference 2014 in Stockholm, Sweden. Visit my website! http://www.adabelletc.com For more info about Troglobyte Studios: https://www.facebook.com/TroglobyteStudios The school where awesome games are made: http://thegameassembly.com Learn more about Swedish Game Awards over at: - http://gameawards.se - http://facebook.com/swedishgameawards - http://twitter.com/SweGameAwards
Views: 836 Adabelle
Using Artificial Intelligence to Play the Atari Centipede Game
 
04:48
A simple AI agent playing the centipede game for the Atari 2600 gaming system. This video was produced summarizing my class project in the Artificial Intelligence Course taught at the University of Wyoming by Professor Jeff Clune. For more see http://JeffClune.com.
Views: 19077 EvolvingRobots
How to Make an Amazing Video Game Bot Easily
 
07:34
In this video, we first go over the history of video game AI, then I introduce OpenAI's Universe, which lets you build a bot that can play thousands of different video games. It has environments for all sorts of games, from Space Invaders, to Grand Theft Auto, to Protein folding simulations. CODING CHALLENGE DUE DATE: Thursday, December 15th. (which is 2 weeks, not 1 week from now like usual) The coding challenge for this video is to make a bot that's better than this video's demo code. Post your Github link in the comments! For your README, just include a 1-3 sentence description of your strategy and instructions on how to run the code.The demo code can be found in the README of the Universe repo. : https://github.com/openai/universe And a Tensorflow based starter bot can be found here: https://github.com/openai/universe-starter-agent Some great learning resources: https://www.nervanasys.com/openai/ http://karpathy.github.io/2016/05/31/rl/ http://kvfrans.com/simple-algoritms-for-solving-cartpole/ https://kofzor.github.io/Reinforcement_Learning_101/ Join other Wizards on our Slack channel: https://wizards.herokuapp.com/ OpenAI asked me to make this video and I gladly said yes. They are awesome!! Please subscribe! And like and comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Views: 221454 Siraj Raval
10 Minimax algorithm in Artificial intelligence  | minimax search procedure
 
14:16
*Minimax search procedure is known for calculating in the two player games * Each nice is represented as loss and gain for one of the players . * Minimax algorithm is used in Zero Sum Games . Stay tuned for more updates .
Views: 2757 Nisha Mittal
Elon Musk’s A.I. Destroys Champion Gamer!
 
10:23
Subscribe here: https://goo.gl/9FS8uF Check out the previous episode: https://www.youtube.com/watch?v=VadUK8-5OSA Become a Patreon!: https://www.patreon.com/ColdFusion_TV Hi, welcome to ColdFusion (formerly known as ColdfusTion). Experience the cutting edge of the world around us in a fun relaxed atmosphere. Sources: Dota 2 Championship: https://www.youtube.com/watch?v=92tn67YDXg0 Demis Hassabis Talk: https://www.youtube.com/watch?v=Ia3PywENxU8 https://blog.openai.com/robots-that-learn/ http://www.dailystar.co.uk/tech/gaming/637125/Paris-2024-Olympic-Games-eSports-Call-of-Duty-Overwatch-DOTA-Paris-IOC www.theverge.com/platform/amp/2017/8/11/16137388/dota-2-dendi-open-ai-elon-musk https://www.theverge.com/2017/5/16/15648158/openai-elon-musk-robotics-ai-one-shot-imitation-learning //Soundtrack// 1:00 Sinoptik Music - Don't Leave Me (Original Mix) 3:19 Uppermost - Machine Code 4:20 Grifta – Dawn 7:10 Valotihkuu - First Light 9:07 Scullious - Meant To Be » Google + | http://www.google.com/+coldfustion » Facebook | https://www.facebook.com/ColdFusionTV » My music | http://burnwater.bandcamp.com or » http://www.soundcloud.com/burnwater » https://www.patreon.com/ColdFusion_TV » Collection of music used in videos: https://www.youtube.com/watch?v=YOrJJKW31OA Producer: Dagogo Altraide » Twitter | @ColdFusion_TV
Views: 1577681 ColdFusion
Unreal Engine AI Tutorial: Create Artificial Intelligence with Behavior Trees
 
12:00
Join me in this step-by-step tutorial on using behavior trees to create AI in Unreal Engine 4. Click the link to get a special discount for the full course: https://www.udemy.com/unrealengine-cpp/?couponCode=YT14PR This video is part of my Udemy course on Unreal Engine 4 Mastery! Learn C++, AI and Multiplayer programming for Unreal Engine 4! Course Description: Approved by Epic Games and taught by former Epic Games engineer, Tom Looman, this course teaches you how to use C++ so you can build your own games and artificial intelligence in Unreal Engine 4. If you have a bit of programming know-how but are new to C++ game development, then this course is for you! Unreal Engine 4 Mastery is also a great fit for current developers who have previous experience with Unity or other game engines. Unleash the full power of the Unreal Engine by taking this step-by-step guide. In this course, you will: - Create two multiplayer-ready games in C++ - Create multiple types of AI enemies - Expose C++ code to Blueprint to unlock the full power of the engine - Discover the fundamental classes required to build games - Code many common gameplay mechanics like weapons, power-ups, characters, guards, and more - Challenge yourself with fun activities that further test your programming knowledge - Discover many tricks and features in C++ to get the most out of Unreal Engine - Master the fundamentals to build your own dream game Discount URL: https://www.udemy.com/unrealengine-cpp/?couponCode=YT14PR More info: http://tomlooman.com Twitter: https://twitter.com/t_looman
Views: 13250 Tom Looman
AI ( Game AI ) tutorial 02 - History of Artificial Intelligence (AI)
 
03:15
History of AI : Brief history of Artificial Intelligence: Mr. John McCarthy coined the term AI (Artificial Intelligence) in the year 1956 at the Dartmouth conference and placed a proposal. The proposal was something like this: “We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College.” The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. Note: Mr. John McCarthy demonstrated the first running AI program at Carnegie Mellon University in 1956. Later in 1958 he designed the LISP programming language for programming AI. Then on wards the AI has become most popular and got spread into wide variety of domains like games, robotics, automobiles, neural networks, computer networks, web, medical science, business, etc. ======================================================== Follow the link for next video: https://youtu.be/0wGMGV1g-5g Follow the link for previous video: https://youtu.be/WACdH-FL6AQ ========= For more benefits & Be up to date =================== Subscribe to My YouTube channel: https://www.youtube.com/chidrestechtu... Like my Facebook fan page: https://www.facebook.com/ManjunathChidre ========================================================
Views: 2531 Chidre'sTechTutorials
Virtual Reality And Artificial Intelligence Driven Video Games (Computer Games)
 
01:35:03
A fun-filled glimpse into the not so distant history of video games. Since inception, the gaming industry has been a driving force in computer technology and. A fun-filled glimpse into the not so distant history of video games. Since inception, the gaming industry has been a driving force in computer technology and. Futurist Ray Kurzweil explores the next phase of virtual reality. Question: How will next-gen virtual reality change our lives? Ray Kurzweil: Well, start fro. Virtual Reality Practices Including Virtual Reality Practices Of Virtual Reality Practicesuite Virtual Reality Practicespot Virtual Reality Practices For Vir.
Views: 9596 Documentary Lab
Google DeepMind's Deep Q-learning playing Atari Breakout
 
01:43
Google DeepMind created an artificial intelligence program using deep reinforcement learning that plays Atari games and improves itself to a superhuman level. It is capable of playing many Atari games and uses a combination of deep artificial neural networks and reinforcement learning. After presenting their initial results with the algorithm, Google almost immediately acquired the company for several hundred million dollars, hence the name Google DeepMind. Please enjoy the footage and let me know if you have any questions regarding deep learning! ______________________ Recommended for you: 1. How DeepMind's AlphaGo Defeated Lee Sedol - https://www.youtube.com/watch?v=a-ovvd_ZrmA&index=58&list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e 2. How DeepMind Conquered Go With Deep Learning (AlphaGo) - https://www.youtube.com/watch?v=IFmj5M5Q5jg&index=42&list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e 3. Google DeepMind's Deep Q-Learning & Superhuman Atari Gameplays - https://www.youtube.com/watch?v=Ih8EfvOzBOY&index=14&list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Subscribe if you would like to see more content like this: http://www.youtube.com/subscription_center?add_user=keeroyz - Original DeepMind code: https://sites.google.com/a/deepmind.com/dqn/ - Ilya Kuzovkin's fork with visualization: https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner - This patch fixes the visualization when reloading a pre-trained network. The window will appear after the first evaluation batch is done (typically a few minutes): http://cg.tuwien.ac.at/~zsolnai/wp/wp-content/uploads/2015/03/train_agent.patch - This configuration file will run Ilya Kuzovkin's version with less than 1GB of VRAM: http://cg.tuwien.ac.at/~zsolnai/wp/wp-content/uploads/2015/03/run_gpu - The original Nature paper on this deep learning technique is available here: http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html - And some mirrors that are not behind a paywall: http://www.cs.swarthmore.edu/~meeden/cs63/s15/nature15b.pdf http://diyhpl.us/~nmz787/pdf/Human-level_control_through_deep_reinforcement_learning.pdf Web → https://cg.tuwien.ac.at/~zsolnai/ Twitter → https://twitter.com/karoly_zsolnai
Views: 840076 Two Minute Papers
Java Artificial Intelligence
 
02:07
This Is Some Artificial Intelligence I've Programmed In Java For My Game. I've Only Been Learning Java For 4 Months Now, Through YouTube And Teaching Myself, So Don't Hate Pl0x. I'll Probably Release It On Some Blog If You Really Want To Play It When It's Done, Just Comment And Ask.
Views: 17685 Aaronster8
Jonathan Schaeffer Artificial Intelligence and Games Part I
 
38:47
Part I of Dean of Science Jonathan Schaeffer's presentation "Artificial Intelligence and Gaming" November 1, 2012 in the Technology and Future of Medicine course LABMP 590 http://www.singularitycourse.com at the University of Alberta in Edmonton, Alberta, Canada. Table of Contents 00:00:48 Marcus Hutter's presentation complicated, 00:01:15 games as a subject for AI study, 00:02:44 Finally was able to acknowledge games as main focus for research, 00:03:50 Bioware, Electronic Arts, 00:04:42 Millions of games sold where participated in software development 00:05:10 What is AI?, 00:06:40 AI - Processing information, speech, vision, reasoning, learning, planning, machines can mimic all these human activities, 00:08:00 Computer (R)evolution, 00:08:54 One of most profound contributions of the 20th century is the realization that intelligent behavior can be realized by non-human information processing architectures, 00:11:01 AI a moving target, constantly raising the bar for what is AI, 00:13:10 core piece that keeps shrinking, the more we learn about intelligence, the more we realize there is not a lot of magic in human intelligence, 00:13:40 at least two different architectures for intelligence, humans and computers, the two architectures able to solve the same problems, 00:14:45 two physical models for exhibiting intelligence, different solving methods, 00:15:38 Two architectures complementary, 00:17:08 Human strengths are computer weaknesses and vice versa, computer have difficulty writing sonnets, discussing baseball and Stephen Harper's new pet, 00:17:20 Play to strengths of computer, 00:17:50 Look under hood of computer, how can dumb things give rise to intelligent behavior, a paradox, 18:58 Kasparov playing against Deep Blue in 1997, Murray Campbell from Edmonton, one of three coauthors of Deep Blue, 00:23:30 Man vs. Machine specs, Deep Blue 0.5 year old, could analyze 200 million chess positions per second, 00:26:35 computers today superhuman at chess, 00:27:14 crossword puzzles, crossword solving program scores 97%, 00:29:44 program does not understand the question but gets the answer anyway!, Information retrieval without understanding the information is a powerful tool, 00:31:40 AI exhibits the illusion of intelligent behavior, 00:32:30 Artificial stupidity, 00:32:40: Games research, Games the most visible AI, 00:33:30 realistic behavior and conversations, 00:34:15 Facade. First game to evoke emotional responses, 00:35:30 still have a long way to go, game Oblivion, 00:37:10 flame thrower, no reaction, reaction to feather, Copyright 2012 Transpath Inc. Part II is at http://youtu.be/sMZx0YdkHUw
Views: 4449 Kim Solez
Let us play: Artificial and human intelligence in games
 
49:54
To mark the launch of our new research group in Games, we held a public lecture, given by Professor Peter Cowling and Dr Paul Cairns which looked at some of the research being done at York. Digital games are socially, culturally and economically important - bigger than film or music - with a UK industry valued at over £3 billion. The UK government and the games industry have over £30 million invested currently for research into games and digital creativity led by the University of York, in the NEMOG (www.nemog.org) and IGGI (www.iggi.org.uk) projects. This talk focusses on three aspects of the research to date. (i) how we have used search approaches based on random simulations (so called Monte Carlo approaches) to make better Artificial Intelligence (AI) for games; (ii) work done at York to investigate immersive and social experiences that players have while playing, using the tools of human-computer interaction (HCI); (iii) a glimpse of the future of games research at York and the tantalising prospects of games as a tool to understand and help people, do science, provide new cultural experiences, and provide economic prosperity through working with the games industry.
Future of Video games / Artificial Intelligence
 
22:58
Future of Video games / Artificial Intelligence
Views: 249 PLUR
Outrageous Artificial Intelligence: (Game 3) : DeepMind’s AlphaZero crushes Stockfish Chess computer
 
12:30
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 with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality. In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs." What is this company called Deepmind ? https://en.wikipedia.org/wiki/DeepMind DeepMind Technologies Limited is a British artificial intelligence company founded in September 2010. Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7] The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film. ♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053 ►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp ►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396 ►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053 http://goo.gl/7HJcDq ►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp ►Playlists: http://goo.gl/FxpqEH ►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher
Views: 17614 kingscrusher
Introduction to AI for Video Games
 
09:50
Welcome to my new reinforcement learning course! For the next 10 weeks we're going to go from the basics to the state of the art in this popular subfield of machine learning using video game environments as our testbed. RL is a huge reason DeepMind and OpenAI have been so successful thus far in creating world changing AI bots. Make sure to subscribe so you'll get updated with every new video I release. And don't worry if you don't understand policy iteration or value iteration just yet, I merely wanted to introduce these phrases in this video, next week i'm going to really dive into what these 2 methods look like programmatically. Code for this video (with coding challenge): https://github.com/llSourcell/AI_for_video_games_demo Syllabus for this course: https://github.com/llSourcell/AI_for_Video_Games_Syllabus Please Subscribe! And like. And comment. That's what keeps me going. Want more inspiration & education? Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0 http://icml.cc/2016/tutorials/deep_rl_tutorial.pdf https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/ https://web.mst.edu/~gosavia/tutorial.pdf http://karpathy.github.io/2016/05/31/rl/ http://www.wildml.com/2016/10/learning-reinforcement-learning/ https://www.quora.com/What-are-some-good-tutorials-on-reinforcement-learning Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Views: 36344 Siraj Raval
AiLive LiveCombat: Artificial intelligence and behavior capture for games
 
01:00
Trailer showing AiLive's LiveCombat behavior. Build your own AI in minutes! AiLive: http://www.ailive.net is a leader in providing machine learning solutions for motion recognition, tracking, gesture recognition in games.
Views: 11259 mediaAiLive
بالعربي Game Search in Artificial Intelligence - Minmax (Chance Nodes) & Alpha-Beta Pruning
 
01:08:31
Two game search strategies in artificial intelligence (AI) are discussed in this lecture which are minmax (with chance nodes) and alpha-beta pruning. A wide area of AI is dedicated for creating agents to play games that humans play in their life such as chess, tic-tac-toc (XO), and 8 queens puzzle. A critical step to create such agent in AI is by creating a search tree and searching it later for a path leading to a desired goal. Minmax and alpha-beta pruning are examples of search strategies for two players games. While minmax is a brute-force game search strategy that explores all nodes in the search space, alpha-beta pruning may exclude some nodes from being explored. A very simple way to exclude nodes without using alpha and beta values is presented. Some games are not based on goodness values. The player may not have any metric to compare paths and and select a specific path over another. For search reasons, these search strategies are modified to work with chance nodes. Chance nodes are ones at which there is no previous information to help making decision. Minmax with chance nodes is presented. --------------------------------- أحمد فوزي جاد Ahmed Fawzy Gad قسم تكنولوجيا المعلومات Information Technology (IT) Department كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI) جامعة المنوفية, مصر Menoufia University, Egypt Teaching Assistant/Demonstrator [email protected] --------------------------------- Find me on: KDnuggets: https://www.kdnuggets.com/author/ahmed-gad HlaliTech: https://hlalitech.org/author/ahmed-gad/ Amazon Book: https://www.amazon.com/dp/6202073128/ref=cm_sw_r_fa_dp_U_sZcHAbRJ1MGHS Arabic Blog : https://aiage-ar.blogspot.com.eg/ English Blog: https://aiage.blogspot.com.eg/ Scoop.it: https://www.scoop.it/u/ahmed-fawzy-gad DataScienceCentral: https://www.datasciencecentral.com/profile/AhmedGad YouTube: https://www.youtube.com/AhmedGadFCIT Google Plus: https://plus.google.com/u/0/+AhmedGadIT SlideShare: https://www.slideshare.net/AhmedGadFCIT LinkedIn: https://www.linkedin.com/in/ahmedfgad Github: https://github.com/ahmedfgad reddit: https://www.reddit.com/user/AhmedGadFCIT ResearchGate: https://www.researchgate.net/profile/Ahmed_Gad13 Academia: https://menofia.academia.edu/Gad Google Scholar: https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay: https://www.mendeley.com/profiles/ahmed-gad12 ORCID: https://orcid.org/0000-0003-1978-8574 ResearcherID.com: http://www.researcherid.com/rid/F-7859-2018 Publons: https://publons.com/a/1428952 StackOverflow: http://stackoverflow.com/users/5426539/ahmed-gad Twitter: https://twitter.com/ahmedfgad Facebook: https://www.facebook.com/ahmed.f.gadd Pinterest: https://www.pinterest.com/ahmedfgad/work
Views: 1285 Ahmed Gad
Outrageous Artificial Intelligence: (Game 7) : DeepMind’s AlphaZero crushes Stockfish Chess Engine
 
16:50
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 with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality. In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs." What is this company called Deepmind ? https://en.wikipedia.org/wiki/DeepMind DeepMind Technologies Limited is a British artificial intelligence company founded in September 2010. Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7] The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film. ♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053 ►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp ►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396 ►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053 http://goo.gl/7HJcDq ►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp ►Playlists: http://goo.gl/FxpqEH ►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher
Views: 15377 kingscrusher
Keynote "How Artificial Intelligence is changing the game"
 
33:27
CeBIT Global Conferences - 22 March 2017: Keynote "How Artificial Intelligence is changing the game" / Frank Riemensperger, Accenture Deutschland, Germany http://bit.ly/2nqZrDv
Views: 6633 cebitchannel
Outrageous Artificial Intelligence: (Game 6) : DeepMind’s AlphaZero crushes Stockfish Chess Engine
 
12:37
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 some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality. In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs." What is this company called Deepmind ? https://en.wikipedia.org/wiki/DeepMind DeepMind Technologies Limited is a British artificial intelligence company founded in September 2010. Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7] The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film. ♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053 ►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp ►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396 ►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053 http://goo.gl/7HJcDq ►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp ►Playlists: http://goo.gl/FxpqEH ►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher
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A.I.M. - Artificial Intelligence Machine - REVIEW
 
06:39
Review on this great game
Views: 4377 Seltee White

Pubg Forums Xbox Can Be Fun for Everyone

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Pubg Forums Xbox Can Be Fun for Everyone

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