Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Computational Intelligence in Games: An Overview Zahid Halim Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi. [email protected] Layout • • • • • • What is AI/CI and ML Why Computer Games? How CI helps computer Games? Some Examples Key venues to publish work Future directions 12/19/2012 Computational Intelligence in Games: An Overview 2 AI vs. CI vs. ML • • • Artificial Intelligence (Think like human, learn from experience, recognize patterns, make complex decisions based on knowledge and reasoning) – Machine learning – Knowledge representation – Natural Language Processing – Planning Robotics etc. Machine learning – Branch of AI – Construction and study of systems that can learn from data – Email messages to learn to distinguish between spam and non-spam messages – There is difference between ML and Data Mining too Computational Intelligence (www.ieee-cis.org) – Integrating the fields • Artificial Neural Networks • Evolutionary Computation • Fuzzy Logic 12/19/2012 Computational Intelligence in Games: An Overview 3 They are related… But they are all different… I hope all of us understand difference between hard and soft computing CI ML AI 12/19/2012 Computational Intelligence in Games: An Overview 4 Why Computer Games? 49% of U.S. households own a dedicated game console 32% 37% Female 47% Male 53% Under 18 31% 18-32 36 or more The average game player age is: 12/19/2012 Computational Intelligence in Games: An Overview 30 years 5 Why Computer Games? • 42% of game players believe that computer and video games give them the most value for their money, compared with DVDs, music or going out to the movies • Gamers who are playing more video games than they did three years ago are spending less time: – 59% playing board games – 50% going to the movies – 47% watching TV – 47% watching movies at home • • 62% of gamers play games with others, either in-person or online 78% of gamers who play with others do so at least one hour per week 12/19/2012 Computational Intelligence in Games: An Overview 6 Money Matters! But its not every thing! Total: $24.75 Billion 16 16.9 16.6 Consumer Spend on Games Industry 2011 Accessories Dollars (Billions) Hardware Contents 11.7 11% 9.5 6 6.9 7 7.3 6.9 7.3 22% 67% 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 12/19/2012 Computational Intelligence in Games: An Overview 7 What can Computational Intelligence do? • • • • • • • Generate complete game Creation of intelligent game characters Creation of entertaining game characters Generating tracks for racing games. Adaptable player experience. Levels for action games. Generating maps for games. 12/19/2012 Computational Intelligence in Games: An Overview 8 Procedural Content Generation • • • • Lindenmayer system: A variant of a formal grammar, most famously used to model the growth processes of plant. Consists of: – An alphabet of symbols that can be used to make strings – A collection of production rules which expand each symbol into some larger string of symbols – An initial "axiom" string from which to begin construction – A mechanism for translating the generated strings into geometric structures. PCG can also generate weapons that player might require in a game Search based PCG is different 12/19/2012 Computational Intelligence in Games: An Overview 9 Some of the PCG based Games 12/19/2012 Game Content Year ToeJam & Earl The random levels were procedurally generated. 1991 The Elder Scrolls III: Morrowind Water effects are generated on the fly."Water Interaction" demo. 2002 RoboBlitz XBox360 live arcade and PC 2006 Borderlands Weapons were generated depending upon the levels 2009 Terraria 2D landscape was generated that a player can travel around. 2011 Computational Intelligence in Games: An Overview 10 Automated Game “entertaining” Generation Search Space Dimension Play Area Types of Pieces Number of pieces/type Initial position Movement direction Step Size Capturing Logic Game ending logic Conversion Logic Mandatory to capture Turn passing allowed 12/19/2012 Possible Values Checkers Chess Only black squares are Both white & black used squares are used Initially 1, maximum 2 6 12, variable (but max. 16 12) Black squares of first 3 Both white & black rows squares of first 2 rows Diagonal forward and All directions, straight Diagonal, forward forward, straight backward forward and backward, L shaped, diagonal forward One Step One Step, Multiple Steps Step over Step into No moves possible for No moves possible for a player the king Checkers into king Soldiers into queen or any piece of choice Yes No No No Select Values Gene Title Value Placement of gene of each type 0-6 Movement logic of each type 1-6 Step Size 0/1 Capturing logic move into cell or jump over 0/1 0/1 Piece of honour 0-6 Conversion Logic 0-6 0-6 Mandatory to capture or not 0/1 1 Both white & black squares are used 6 variable but at maximum 24 : 24 25 : Both white & black squares of first 3 rows All directions, straight forward, straight forward and backward, L shaped, diagonal forward 30 31-36 37 : 42 43 One Step, Multiple Steps Step over, step into No moves possible for a player, no moves possible for the king Depends upon rules of the game 44 : 49 50 Depends upon rules of the game No Computational Intelligence in Games: An Overview 11 I = ( n K 0 k I )/n Objective Function D = ( nK0Lk )/n Duration of game (D) n m Dyn = ( (( (Ci ) / Li )/m))/n 1 Scaled value of D j1 1.2 i 1 0.8 0.6 0.4 0.2 n m i 1 k 0 0 1 U = ( (( (C k )) / | Cu |))/n • • • • • 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 1+1 Evolutionary Strategy (ES) 10 chromosomes are randomly initialized The evolutionary algorithm is run for 100 iterations Mutation only with probability of 30 percent One parent produce one child – Fitness difference is calculated – If it is greater than 4 (at least half times better) child is promoted to the next population FitnessDif ference (1 (( fitness p fitnessc ) / fitness p )) for _ all _ metrics 12/19/2012 Computational Intelligence in Games: An Overview 12 Making Racing Fun Through Player Modelling and Track Evolution • We have one or several car racing tracks with – Walls, Waypoints, Staring position of the car • Car consist of – Sensor model to sense the environment – Discrete set of control commands • Objective of the game is to pass as many waypoints in given timesteps. • Car has 6 sensors, Speed of the car and Angle to the next waypoint • • • • Fully connected feedforward nets (MLPs) with the tanh transfer function. Only the weights of the networks are changed by evolution or back propagation Nine inputs (sensors and a bias input), Six hidden neurons Two output neurons are used. – The First output is interpreted as driving command – Second as steering command. 12/19/2012 Computational Intelligence in Games: An Overview 13 Learning Behaviour: Backpropagation • • • • • Human player drove a number of laps around a track, while the inputs from sensors and actions taken by the human were logged at each timestep. This log was then used to train a neural network controller to associate sensor inputs with actions using a standard backpropagation algorithm. Several variations on this idea were tried with very little success. Training often achieved low error rates (typically 0.05), none of the trained networks managed to complete even half a lap. A small amount of noise that is applied to sensors guarantees that the car does not simply replay the human action. 12/19/2012 Computational Intelligence in Games: An Overview 14 Evolving Neural Network Agents in the NERO Video Game – real-time NeuroEvolution of Augmenting Topologies (rt-NEAT) method for evolving increasingly complex artificial neural networks in real time, as a game is being played. – rtNEAT makes possible a new genre of video games in which the player teaches a team of agents through a series of customized training exercises. – In NEAT, the population is replaced at each generation. • Everyone’s behaviour would change at once. • Behaviours would remain static during the large gaps between generations – In rtNEAT, a single individual is replaced very few game ticks 12/19/2012 Computational Intelligence in Games: An Overview 15 Conferences and Journals • • IEEE Computational Intelligence and Games IEEE Transactions on Computational Intelligence and AI in Games (IF 1.8) • • • International Journal of Computer Games Technology International Conference on Computer Games (CGAMES) CGamesUSA International Conference on Computer Games 12/19/2012 Computational Intelligence in Games: An Overview 16 Where are the opportunities? • • • • CIG for health care CIG for education Neuro Computer interface for games Physicological study via games 12/19/2012 Computational Intelligence in Games: An Overview 17 Thanks for your patience Presentation available at: http://ming.org.pk/zahid.htm Bibliography • Halim, Zahid, A. Rauf Baig, and Hasan Mujtaba. "Measuring entertainment and automatic generation of entertaining games." International Journal of Information Technology, Communications and Convergence 1.1 (2010): 92-107. • Halim, Zahid, A. Rauf Baig, and Mujtaba Hasan. "Evolutionary Search For Entertainment In Computer Games." Intelligent Automation & Soft Computing 18.1 (2012): 33-47. • Halim, Zahid, and A. Raif Baig. "Evolutionary Algorithms towards Generating Entertaining Games." Next Generation Data Technologies for Collective Computational Intelligence. Springer Berlin Heidelberg, 2011. 383-413. • http://tim.hibal.org/blog/wp-content/uploads/2010/01/speciesChange.png • http://www.sennir.co.uk/Journal/178 • ESA 2012 Sales, Demographic and Usage Data • Evolving Neural Network Agents in the NERO Video Game, Stanley et. al • Acquiring Visibly Intelligent Behavior with Example-Guided Neuroevolution, Bryant et. al. • Making Racing Fun Through Player Modeling, Togelius et. al. • Evolutionary Search for Entertainment in Games, Halim et. al. 12/19/2012 Computational Intelligence in Games: An Overview 19