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GA -CIG Competition PacMan- Yonsei Univ Soft Computing Lab

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1/34 NOT YOUR FAULT!

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2/34 7 GA ?

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3/34 1. ! 2. !

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4/34 DO YOU BELIEVE IN GA?

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5/34 ?

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6/34

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( ) 7/34 Time(1.5 sec) Joint Degree(38) Angular motor . Motor 5 (-2, -1, 0, 1, 2 ) . (-) .

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8/34 01101110000100010011 64 361 169 576 ?

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9/34

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10/34 !

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11/34 !

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CIG IEEE Symposium on Computational Intelligence and Games Tutorial Session Presentation Competition http://www.ieee-cig.org/competitions/ Competitions Chair: Simon M. Lucas 12/34

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CIG Competition 13/34

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Unreal Tournament Sponsored by 2K Australia2K Australia The competition task is to create a computer game bot Indistinguishable from a human player major prize of A$7,000 cash Trip to 2K Australia's studio DeathMatch game type for the FPS Chatting will be disabled. Some aspects of the game play will be modified in a way not known to competitors. 14/34

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TORCS Car Racing Daniele Loiacono, Julian Togelius and Pier Luca Lanzi Learn (or otherwise develop) a controller The distance raced in a fixed amount of time Best few controllers will race against each other Mac OS X, Windows and Linux, using Java and C++ Neural networks, Fuzzy logic, Force fields, Expression trees 15/34

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Ms Pac-Man Non-deterministic game The world record for a human player currently stands at 921,360 Can anyone develop a software agent to beat that? the best software controller for the game of Ms Pac-Man Screen-capture to get the current state of the game 16/34

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AI for CS different than AI for Games 17/34 Must be smart, but purposely flawed Loose in a fun, challenging way No unintended weaknesses No golden path to defeat Must not look dumb Must perform in real time (CPU) Configurable by designers Not hard coded by programmer Amount and type of AI for game can vary RTS needs global strategy, FPS needs modeling of individual units at footstep level RTS most demanding: 3 full-time AI programmers Puzzle, street fighting: 1 part-time AI programmer

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Goals Games are entertainment Important that things behave naturally Not necessarily perfect things are not always creature Follow(the games) natural laws And avoid cheating Characters should be aware 18/34

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Game Agents 19/34 Most AI focuses around game agent think of agent as NPC, enemy, ally or neutral Loops through: sense-think-act cycle Acting is event specific, so talk about sense and think first, then a bit on act SenseThink Act

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Game Agents Sensing (1 of 2) Gather current world state: barriers, opponents, objects, Needs limitations: avoid cheating by looking at game data Typically, same constraints as player (vision, hearing range, etc.) Vision Can be quite complicated (CPU intensive) to test visibility (ie- if only part of an object visible) Compute vector to each object Check magnitude (ie- is it too far away?) Check angle (dot product) (ie- within 120 viewing angle?) Check if obscured. Most expensive, so do last.

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Game Agents Sensing (2 of 2) Hearing Ex- tip-toe past, enemy doesnt hear, but if run past, enemy hears (stealth games, like Thief) Implement as event-driven When player performs action, notify agents within range Rather than sound reflection (complicated) usually distance within bounded area Can enhance with listen attributes by agent (if agent is keen eared or paying attention) Communication Model sensing data from other agents Can be instant (ie- connected by radio) Or via hearing (ie- shout) Reaction times Sensing may take some time (ie- dont have agent react to alarm instantly, seems unrealistic) Build in delay. Implement with simple timer.

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Game Agents Thinking (1 of 3) Evaluate information and make decision As simple or elaborate as required Generally, two ways: Pre-coded expert knowledge Typically hand-crafted if-then rules + randomness to make unpredictable Search algorithm for best (optimal) solution Ex- MinMax 22/34

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Game Agents Thinking (2 of 3) 23/34 Expert Knowledge Finite State Machines, decision trees, (FSM most popular, details next) Appealing since simple, natural, embodies common sense and knowledge of domain Ex: See enemy weaker than you? Attack. See enemy stronger? Go get help Trouble is, often does not scale Complex situations have many factors Add more rules, becomes brittle Still, often quite adequate for many AI tasks Many agents have quite narrow domain, so doesnt matter

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Game Agents Thinking (3 of 3) 24/34 Search Look ahead and see what move to do next Ex: piece on game board (MinMax), pathfinding (A*) Works well with known information (ie- can see obstacles, pieces on board) Machine learning Evaluate past actions, use for future action Techniques show promise, but typically too slow

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Game Agents Acting (1 of 2) 25/34 Learning and Remembering May not be important in many games where agent short-lived (ie- enemy drone) But if alive for 30+ seconds, can be helpful ie- player attacks from right, so shield right Implementation - too avoid too much information, can have fade from memory (by time or by queue that becomes full)

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Game Agents Acting (2 of 2) 26/34 Making agents stupid Many cases, easy to make agents dominate Ex: FPS bot always makes head-shot Dumb down by giving human conditions, longer reaction times, make unnecessarily vulnerable, have make mistakes Agent cheating Ideally, dont have unfair advantage (such as more attributes or more knowledge) But sometimes might cheat to make a challenge Remember, thats the goal, AI lose in challenging way Best to let player know

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Game Agents AI Deterministic AI Preplanned, predictable events Set by rules Walk forward ten steps then backwards If player is within 5 squares move closer Go to town take the shortest path using shortest path algorithm Attack player with least health/most damage potential/etc Non deterministic AI Unpredictable movement Random selection of directions React to environment to learn movement Player nearby, go slower Got shot going around corner last time, then take a wider path this time 27/34

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Base Environment http://cswww.essex.ac.uk/staff/sml/pacman/PacManContest.html Java Image Detector, Simulator, Game http://sclab.yonsei.ac.kr/courses/09AgentGame/Reference_Code.zip

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29/34 IF then If then. If then SensingThinkingActing

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30/34 1. 2. 3. ? 4. !

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31/34 ? 256

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32/34 Evaluating! ! !

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33/34 Expert Rule Based System(High Priority) GA Rule(Middle Priority) Expert Rule Based System(Low Priority)

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34/34 1. GA 2. CIG Competition 3. AI 4. Pac_man AI 5. Pac_man AI GA