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 Málaga from 14 to 16th November.

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Science and Videogames. Computational intelligence in videogames

  2. 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
  4. 4. • Very big growing of videogames market, due to their movement to new groups of interest: people older than 25 and younger than 10, including parents and grandparents, in addition to the feminine sector.• This growing is mainly due to a change in the videogames philosophy, offering more ‘adult’ contents, or the contrary, easier and child- focused, in addition to direct and brief action games. There is a wide market for science!!!
  5. 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. 6. • A sincere feeling:
  7. 7. • In addition to PC and mobile systems (iOS, Android, etc), there are some extended systems:• Home consoles Wii Xbox 360 Playstation 3• Portable consoles Nintendo Playstation 3DS Vita
  8. 8. • And many more (open-philosphy consoles), not known for most of the people. GP2X Wiz Caanoo Dingoo Pandora
  9. 9. • Another (positive) consequence is the adaptation of the study plans for 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)
  11. 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. 12. There is even a project, WiiLab, which has created a Matlab toolbox forinteracting with Wiimote… …and with Game Maker (an easy game development framework)
  13. 13. • It was initially used for building console-based clusters (super computers), due to the powerful chip Cell, and the cheap price it had.• It was possible to use an additional Linux O.S. (Yellow Dog), very flexible.• But later, the console was updated for not admitting the installation of any additional O.S., so Linux was lost forever.
  14. 14. Very famous in the community due to Kinect:• Robots control by movement and/or voice, pattern and person recognition, among others.
  16. 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 in that sense, by implementing specific engines for physics modeling.
  17. 17. The scientific principles of operation of the first main controller fora console based in movement (Wiimote) are:
  19. 19. • In addition to visual and physics realism, it is desired to model enemies and partners , with an ‘intelligent’ (human) behavior.• Thus a big amount of resources have been focused on artificial intelligence. Realistic Game
  20. 20. • AI is the area of computer science devoted to implement nonliving rational agents (at least in appearance).• Inside a videogame, AI is focused on defining behavior techniques for non-playable characters (NPCs), commonly named bots, which simulate being rational. These characters could be enemies or partners.• It is not a matter of literally showing human behavior, since it means the consideration of mistakes.
  21. 21. • In the very beginning, NPCs followed some predefined behavior patterns, that the programmer implemented at game implementation and which were invariable.• Reactive AIs proposed NPC’s actions as a response to player’s actions.• Dedicated AIs set different ‘personalities’ for NPCs.
  22. 22. • Later there were introduced the finite state machines, which define a set of possible states for the NPC, and a set of transitions between them. Transitions are based in perceptions about the game or about the players. By Fergu
  23. 23. • Other extended methods include rule-based systems and decision trees. In both cases, there is a set of rules that the NPC will follow, depending on the inputs or perceptions about its environment.
  24. 24. • Nowadays it is usual to mix some of these techniques, thus in most games NPCs follow predefined behavioral models (scripts), depending on player’s actions.• Their advantage is that it is easy to define them, considering programmer’s experience and modeling player’s behavior.• Their main disadvantage is the low flexibility they have in order to adapt to new situations/events.• Moreover, NPCs have additional advantages over the human player, such as perfect aim (based in exact coordinates), or navigation points (waypoints in maps modeling advantageous routes, shortest paths, etc).• Just a few ‘scientific techniques’ have been used in commercial games…
  25. 25. • Traditionally in the scientific area it was called Game Theory, a branch of applied mathematics in which there are some rewards depending on the chosen decision. It involved simple, but difficult to solve, games: Hanoi towers, prisoner’s dilemma, game of life.• These games proposed problems to be solved by means of exact methods, heuristics or metaheuristics: tree-based search, A*, evolutionary algorithms, ant colony optimization,…• Moreover, the resolution of traditional games (usually puzzles) has also been studied from the ‘ancient times’ in science life: chess, backgammon, mastermind, sudoku
  26. 26. • videogames provide a new environment for solving heterogeneous problems.• The most famous (and probably the first) problem addressed was AI related issues. It still remains as the main (the most studied) problem in the area.• However, with the advances and improvement of technology, videogames have increased their complexity, so new researching lines have been arisen: – Search in maps, combat prediction, or simulation, to cite a few• Nowadays, there are a huge number of research fields inside videogames scope, so research studies and publications have grown exponentially.
  27. 27. • AI branch which applies metaheuristics and bioinspired methods for the resolution of complex problems, usually by means of adaptive systems.• It is necessary to model the game (or a part of it) as an optimization, search or learning problem, among others.• Examples: – Pathfinding – Combat prediction – Automated generation of behavioral rules – Parameter tuning – Objective decision
  28. 28. • The most used metaheuristics are: Genetic Algorithms (GA), Ant Colony Optimization (ACO), Monte-Carlo Tree Search (MCTS), A*, Genetic Programming (GP), Fuzzy Logic, Neural Networks…• Which are mostly applied over finite state machines (FSM), scripts, rule-based systems (RS) or expert system (ES), among others.
  29. 29. • NPC’s AI: try to model AI aspects for enemies or partners. It is usual to apply GAs to optimize parameters considered in behavioral rules.• Rule system generation: automated definition of behavioral rules sets, which determine the way the NPCs act in different situations. It is usual to apply GP.• Human-like behavior analysis and modeling: the objective is to model NPCs which behave as human players. Data mining and learning techniques are usually employed.
  30. 30. • Cheating detection: trick detection techniques, based on the study of statistics about matches.• Move and battle prediction: prediction methods are trained (using neural networks) analyzing data from recorded matches, trying to anticipate future movements and actions.• Learning in games: adaptive agents can be created by means of reinforcement learning.
  31. 31. • Game mechanics and features analysis: game components are analyzed and parameterized in order to get numeric valuations of the game components.• Exploration and search in games: search algorithms are applied in order to find the best paths to objectives in maps, or to explore some areas 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 (interactive methods) or by means of mathematical models.
  32. 32. EXAMPLES
  33. 33. i  initial population f  evaluation function (fitness) ?  stop condition Se  selection Cr  crossover Mu  mutation Re  replacementby Johann Dréo
  34. 34.
  35. 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. 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. 37. • Analyze FSM• Identify behavioral parameters• Optimize them Bot based in GA FITNESS EVALUATION (GA-Bot) population Std AI Std Std AI AI Evolutionary process A.M. Mora et al.: Evolving bot AI in Unreal. EVO* 2010. LNCS 6024, Springer, pp. 170–179
  38. 38. • Analyze FSM• Identify parameters devoted to team performance• Optimize them Team of bots based on GAs FITNESS EVALUATION (GT-Bot) Std Std AI AI Std AI population vs Evolutionary Process Or A.M. Mora et al.: Evolving the cooperative behaviour in unreal bots. IEEE CIG 2010, pp. 241–248
  39. 39. • Define a FSM based in expert’s knowledge: – Two state levels, Set of rules• Optimize parameters by means of a GA
  40. 40. • Examples of NPCs/Bots/Agents:
  41. 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 ant’s fighting. Google AI Challenge 2011.• Pac-Man: It can be implemented pac-man’s or ghosts intelligence.• Simulated Car Racing: Car races, track generation, mechanical optimization.• Mario AI: Agent, learning, level generation.
  42. 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