Science and Videogames. Computational intelligence in videogames

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Presentation with a description of the actual relationship between science and videogames, concerning several aspects and, mainly focusing on the computational intelligence applied to videogames area. This is a tutorial given at the GAME-ON 2012 conference, held at the University of Mlaga from 14 to 16th November.


<ul><li>1.SCIENCE AND VIDEOGAMESPLAYER 1 ANTONIO M. MORA GARCA - Press START - GAME-ON 2012</li></ul> <p>2. Introduction videogames market, players taxonomy, current game systems. Videogames at the University. Videogames applied to science videogame system-based science, engineering and technology. Science in videogames scientific principles of videogames. Researching in videogames main research fields in videogames. Examples: Our works 3. INTRODUCTION 4. Very big growing of videogames market, due totheir movement to new groups of interest:people older than 25 and younger than 10,including parents and grandparents, inaddition to the feminine sector. This growing is mainly due to a change in thevideogames philosophy, offering more adultcontents, or the contrary, easier and child-focused, in addition to direct and brief actiongames. There is a wide market for science!!! 5. In the current market has arisen the so-called casual gamers: sporadic players users of brief and direct action games (arcade, sports, mini- games), or the so-called no-games (training games, art games, and so on). The usual players have auto renamed as hardcore gamers.They (really) enjoy and profit the games, they are informed, like most types of games, and play for long periods (if possible). A friendly way to say Virgin until the age of 37 More fun if they plug-in the console 6. A sincere feeling: 7. In addition to PC and mobile systems (iOS, Android, etc), there are someextended systems: Home consolesWii Xbox 360Playstation 3 Portable consolesNintendoPlaystation3DS Vita 8. And many more (open-philosphy consoles), not known for most of the people.GP2X Wiz Caanoo DingooPandora 9. Another (positive) consequence is the adaptation of the study plansfor videogames development. In Spain there are arising courses in Grades and Masters Anyway, we are still far from other countries in Europe: Example: Center for Computer Game Research (Copenhagen) 10. SCIENTIFICAPPLICATIONS 11. Its (at the beginning) novel controller (Wiimote) has been very famous mainly among the scientific and technical community: Robots control, reactive/touch-detecting screens or surfaces, or pattern/subjects recognition, among others. 12. There is even a project, WiiLab, which has created a Matlab toolbox forinteracting with Wiimote and with Game Maker (an easygame development framework) 13. It was initially used for buildingconsole-based clusters (supercomputers), due to the powerfulchip Cell, and the cheap price ithad. It was possible to use anadditional Linux O.S. (YellowDog), very flexible. But later, the console wasupdated for not admitting theinstallation of any additionalO.S., so Linux was lost forever. 14. Very famous in the community due to Kinect: Robots control by movement and/or voice, pattern and personrecognition, among others. 15. SCIENCE INVIDEOGAMES 16. Videogames have always respected physics rules, even a simple one(in appearance) such as Super Mario Bros. (jumps, trajectories,inertia,). Nowadays the tendency is to develop completely realistic games inthat sense, by implementing specific engines for physics modeling. 17. The scientific principles of operation of the first main controller fora console based in movement (Wiimote) are: 18. RESEARCHIN VIDEOGAMES 19. In addition to visual and physics realism, it is desired to model enemies andpartners , with an intelligent (human) behavior. Thus a big amount of resources have been focused on artificial intelligence. Realistic Game 20. AI is the area of computer science devoted to implementnonliving rational agents (at least in appearance). Inside a videogame, AI is focused on defining behaviortechniques for non-playable characters (NPCs), commonlynamed bots, which simulate being rational. These characterscould be enemies or partners. It is not a matter of literally showing human behavior, since itmeans the consideration of mistakes. 21. In the very beginning, NPCs followed somepredefined behavior patterns, that theprogrammer implemented at gameimplementation and which were invariable. Reactive AIs proposed NPCs actions as aresponse to players actions. Dedicated AIs set different personalitiesfor NPCs. 22. Later there were introduced the finite state machines, whichdefine a set of possible states for the NPC, and a set oftransitions between them. Transitions are based inperceptions about the game or about the players. By Fergu 23. Other extended methods include rule-based systems anddecision trees. In both cases, there is a set of rules that theNPC will follow, depending on the inputs or perceptions aboutits environment. 24. Nowadays it is usual to mix some of these techniques, thus in most gamesNPCs follow predefined behavioral models (scripts), depending on playersactions. Their advantage is that it is easy to define them, considering programmersexperience and modeling players behavior. Their main disadvantage is the low flexibility they have in order to adapt tonew situations/events. Moreover, NPCs have additional advantages over the human player, such asperfect aim (based in exact coordinates), or navigation points (waypoints inmaps modeling advantageous routes, shortest paths, etc). Just a few scientific techniques have been used in commercial games 25. Traditionally in the scientific area it was called Game Theory, abranch of applied mathematics in which there are some rewardsdepending on the chosen decision. It involved simple, but difficultto solve, games: Hanoi towers, prisoners dilemma, game of life. These games proposed problems to be solved by means of exactmethods, heuristics or metaheuristics: tree-based search, A*,evolutionary algorithms, ant colony optimization, Moreover, the resolution of traditional games (usually puzzles) hasalso been studied from the ancient times in science life: chess,backgammon, mastermind, sudoku 26. videogames provide a new environment for solving heterogeneousproblems. The most famous (and probably the first) problem addressed was AIrelated issues. It still remains as the main (the most studied) problemin the area. However, with the advances and improvement of technology,videogames have increased their complexity, so new researching lineshave been arisen: Search in maps, combat prediction, or simulation, to cite a few Nowadays, there are a huge number of research fields insidevideogames scope, so research studies and publications have grownexponentially. 27. AI branch which applies metaheuristics and bioinspiredmethods for the resolution of complex problems, usually bymeans of adaptive systems. It is necessary to model the game (or a part of it) as anoptimization, search or learning problem, among others. Examples: Pathfinding Combat prediction Automated generation of behavioral rules Parameter tuning Objective decision 28. The most used metaheuristics are: Genetic Algorithms (GA),Ant Colony Optimization (ACO), Monte-Carlo Tree Search(MCTS), A*, Genetic Programming (GP), Fuzzy Logic, NeuralNetworks Which are mostly applied over finite state machines (FSM),scripts, rule-based systems (RS) or expert system (ES), amongothers. 29. NPCs AI: try to model AI aspects for enemiesor partners. It is usual to apply GAs tooptimize parameters considered in behavioralrules. Rule system generation: automated definitionof behavioral rules sets, which determine theway the NPCs act in different situations. It isusual to apply GP. Human-like behavior analysis and modeling: theobjective is to model NPCs which behave ashuman players. Data mining and learningtechniques are usually employed. 30. Cheating detection: trick detectiontechniques, based on the study of statisticsabout matches. Move and battle prediction: predictionmethods are trained (using neural networks)analyzing data from recorded matches,trying to anticipate future movements andactions. Learning in games: adaptive agents can becreated by means of reinforcement learning. 31. Game mechanics and features analysis: gamecomponents are analyzed and parameterized inorder to get numeric valuations of the gamecomponents. Exploration and search in games: searchalgorithms are applied in order to find the bestpaths to objectives in maps, or to explore someareas maximizing the covering, for instance. Content, characters, levels and story generation:is the so-called procedural content generation,and is aimed to generate automatically contents.They are valued by the players (interactivemethods) or by means of mathematical models. 32. EXAMPLES 33. i initial population f evaluation function (fitness) ? stop condition Se selection Cr crossover Mu mutation Re replacementby Johann Dro 34. 35. Unreal is a first person shooter (FPS).Famous due to the excelent AI of the enemies (bots), which makes it anamazing multiplayer game. Unreal Tournament series is very well considered.It offers an editor (UnrealEd) which lets us change almost anything in the gameeven the behavior of the bots. It uses the language UnrealScript. 36. A java middleware for Unreal Tournament series games andDefcon games.The architecture is as follows:It is possible to interact with the game from a java program, getting higherindependence (avoiding Unrealscript restrictions) and increasing thePossibilities (java libraries).On the contrary, the structures, classes, functions and workflows definedin the Unreal engine, cannot be accessed, nor used. 37. Analyze FSM Identify behavioral parameters Optimize themBot based in GA FITNESS EVALUATION (GA-Bot)population Std AI StdStdAI AI Evolutionary process A.M. Mora et al.: Evolving bot AI in Unreal. EVO* 2010. LNCS 6024, Springer, pp. 170179 38. Analyze FSM Identify parameters devoted toteam performance Optimize them Team of bots based on GAsFITNESS EVALUATION (GT-Bot) StdStdAIAI Std AI populationvsEvolutionary ProcessOr A.M. Mora et al.: Evolving the cooperative behaviour in unreal bots. IEEE CIG 2010, pp. 241248 39. Define a FSM based in experts knowledge: Two state levels, Set of rules Optimize parameters by means of a GA 40. Examples of NPCs/Bots/Agents: 41. A good way to start working: 2K BotPrize: Unreal bots which should behave as human as possible. Starcraft: combats inside the famous RTS. Planet Wars: simpler RTS game. Google AI Challenge 2010. ANTS: RTS modeling ants fighting. Google AI Challenge 2011. Pac-Man: It can be implemented pac-mans or ghosts intelligence. Simulated Car Racing: Car races, track generation, mechanicaloptimization. Mario AI: Agent, learning, level generation. 42. Conferences: IEEE CIG CGAMES GAME-ON CGAT Special Sessions: LION, IWANN, EVO*, GECCO, WCCI Journals: Transactions on Computational Intelligence and AI in Games (IEEE) Entertainment Computing (Springer) Anyone which accept your paper </p>


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