Artificial Intelligence in Computer Games by MikeJenny


									Artificial Intelligence
in Computer Games
                     Knut Vidar Siem
  City University of New York, College of Staten Island
           Department of Computer Science
      Seminar in Computer Science (CSC490-6220)
                       Spring 2006
              Covered Topics
   AI techniques used in games
   Some game genres and how they use AI
   Quick case study: Quake III: Arena
   AI research and game developing
   Effects of AI in games
   (AI for education and tutoring)
    AI in a Computer Game Setting
   The “look and feel” of the AI is far more
    important than it’s inner workings
   The player is supposed to win
   The overall goal is to create a fun and
    entertaining game
   With this in mind (commercial) game developers
    can include AI in their games
AI Techniques in Computer Games
   Finite state machines (FSM)
   Artificial neural networks (ANN)
   Fuzzy logic
   Expert systems
   Genetic algorithms
             Finite State Machine
   A finite state machine is a model of behavior
    that can be organized in states
   Strengths:
       As complex as the desired
        complexity of the subject
        being modeled
   Weaknesses:
       Not very accurate for
        modeling human behavior
         Artificial Neural Network
   Models a network of biological neurons
   Each node has input and output
   Strengths:
     Generalization
     Human-like solutions

   Weaknesses:
       Training takes time
                  Fuzzy Logic
   Derived from fuzzy set
   Elements have membership
    values in the interval [0, 1]
   Introduces the possibility
    of imprecision
                  Expert System
   Components:
     Knowledge base
     Set of production rules
     Inference engine for reasoning over the knowledge

   Strengths:
       Can solve problems requiring expert knowledge
   Weaknesses:
     Narrow domain of knowledge
     Inherently just decision logic
            Genetic Algorithms
   Principles from genetics and natural selection
   Evolutionary techniques are used to optimize
    the solution to a problem
   A population of solutions evolve over
    generations by combining properties (often with
    the possibility of mutation)
The Most Popular Game Genres
   There are a wide variety of genres; the following
    are the most popular of the major ones:
     First-person shooters (FPS)
     Real-time strategy games (RTS)

     Role-playing games (RPG)

   Among others are simulator, sports and fighting
    The AI requirements varies with the genre
             First-Person Shooters
   Animation: control the body
    and make it blend with the
    environment e.g. using a
    decision system
   Movement: how the entity
    moves in the world with path
    finding                                   Quake III: Arena, 1999

   Combat: should do spatial reasoning, tactics,
    perceptions and e.g. using FSMs
   Behavior: control the entity’s “state of mind” using
    FSMs and fuzzy logic
              Real-Time Strategy
   Analysis: define goals and
    prioritize through map analysis
    and area labeling
   Resource allocation: match
    means with goals with the
                                                  StarCraft, 1998
    help of network flow algorithms
   High-level AI: switch between AI modes using
    an FSM, a rule-based system or a fuzzy logic system.
             Role-Playing Games
   Historically not a genre
    with a lot of AI
   Random encounters are
    more common
   How much AI is used
    depends on how rigid the            Final Fantasy XII, 2006
    story line is
   The AI techniques used depend greatly on the
    kind of intelligence being modeled.
                 Case Study:
               Quake III: Arena
   “First-person shooter” (FPS) released by Id
    Software in 1999.
   The player moves around in a 3D environment
    fighting enemies and assisting teammates
   In this virtual world lies items such as weapons
    and power-ups
   The players can be either human or computer
    controlled bots
              Quake III: Arena
   "Half way through the project, I think
    everyone said, 'The bots suck. We have to
    get our act together.'"
    - Christian Antkow, Level Designer
   Their rescue was Jean-Paul van Waveren, a 22-
    year-old Master student from the Netherlands
          Quake III: Arena Bots
   are autonomous; self-controlled
   use a cognitive model of the world,
    eliminating the need for waypoints
   use an FSM-like structure to think
   have some “wired” domain
   can issue and react to team           “Crash”

          Quake III: Arena Bots
   use an interface of commands similar to the one
    presented to the player
   depend on fuzzy relations that specifies how
    much they want to do, have or use something (in a
   have goals and sub-goals on a stack
   don’t do much planning
   do not cheat by accessing more information
    than they should
         Quake III: Arena Bots
   Video from a human viewpoint
   Video from a bot’s viewpoint
           Common AI Problems
   Cheating, i.e. peeking into
    “secret” information
   Super-powers, more health,
    resources etc.
   Unless an AI entity is made a bit
    dumb it appears to be a super-
   Unhandled mistakes look bad
    (and they always happen)
           Expected AI Qualities
    These qualities are parts of “the ultimate goal”
    in AI:
   Predictability vs. unpredictability
   Creativity in problem solving
   Personality                               “HAL 9000”

   Intension and autonomous acting
   Improvising and planning
   Learning
    Why Computer Games are suitable
           for AI research
   Technical reasons:
     The virtual environment of a game is not a
      simulation of the problem domain, it is the problem
     Games free AI researchers from building the
     They often come with good APIs and modification
    Why Computer Games are suitable
           for AI research
   Non-technical reasons:
     Games are cheap, often below $50
     Game development is a big, high-paced industry

     As games continue to improve, AI is expected to be
      the next discriminant (as graphics was a few years
          Effects of AI in Games
   A good AI doesn’t necessarily make the game
   More interesting behavior
     Better adaptability and improvisation
     More human-like flaws (if implemented)

   Further blurring of the border between reality
    and unreality
   What are the ethical issues with AI entities?
          AI in Educational Tools
   Can reduce cost of expensive training
   Can simplify training operations that are difficult to
   An AI entity can adapt to the progress and attend to
    the specific needs of a student.
   Adds personality, empathy and emotion to the tool
   Programs exist e.g. to teach programmers to detect
    bugs (PROUST) and to prevent bullying in schools
   Human-Level AI's Killer Application; Interactive Computer
    Games, John E. Laird; Michael van Lent, 2001
   Agent design to pass computer games, Astrid Glende, 2004
   The Quake III Arena Bot, J.M.P. van Waveren, 2001
   Strategies for Strategy Game AI, Ian Lane Davis, 1999
   Learning by Feeling: Evoking Empathy with Synthetic,
    Characters, Ana Paiva, 2005
   Links: artificial intelligence and interactive entertainment,
   AI in First-Person Shooter Games, John McCloskey, Jeffrey
    Miller, Amish Prasad & Lars Linden

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