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Survey of AI for games

Survey of AI for games. AI vs. AI for games. Traditional AI: Made to handle unseen inputs, large state space Too many options possible to compute an exact optimal solution Engineering criteria: best possible performance Game AI: The game world is known, though it can still be large

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Survey of AI for games

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  1. Survey of AI for games

  2. AI vs. AI for games • Traditional AI: • Made to handle unseen inputs, large state space • Too many options possible to compute an exact optimal solution • Engineering criteria: best possible performance • Game AI: • The game world is known, though it can still be large • In a known world, optimal solutions can be precomputed • Entertainment criteria: smart enough to pose a challenge, but not smart enough to be undefeatable

  3. Game AI related courses • CSE 3902 Project: Design, Development, and Documentation of Interactive Systems • State machines • CSE 3541 Computer Game and Animation Techniques • Agents and 3D spatial movement • CSE 3521 Survey of Artificial Intelligence I: Basic Techniques • Search, logic, knowledge representation • CSE 5522 Survey of Artificial Intelligence II • Probabilities and research topics • CSE 5524 Computer Vision for Human-Computer Interaction • Computing with images as input

  4. Adding AI into a Game • Friend • Autonomous, intelligent NPC helpmates • Configurable (scripted) behaviors: different characters solve a problem in different ways • Player may trade places with NPC: automation • Foe • Opponents get better with time • Opponents are less predictable because the individuals’ behavior is not uniform • Scene Clutter • Provides a richness to your environment. • Animals grazing, birds flying, people milling about, automobiles driving, etc.

  5. Goal-driven behavior • Multiple steps required to achieve a desired effect • Useful in • Action-adventure type games - puzzles to solve • RPG - task underlings with a multi-step job • Good discussion in Programming Game AI by Example Ch. 9 • Each ‘goal’ is an instance of a composite class • Many different goals can be created with minimal coding

  6. Goal-driven agents • Different classes of agents solve problem in different ways, based on their abilities • “Block door”: • Strong trolls move boulders in the way • Small hobbits shovel sand into the opening • Each agent’s response to the goal depends on his abilities, available tools, etc. • Goal object contains alternative recipes • One goal at a time is active for each agent • In more complex games, might have goal queue

  7. Other AI principles: observability • Don’t let the agents have perfect knowledge: they have to operate in the environment like the players do • Sense and remember events in their sensory horizon, memories can have a timestamp • Perhaps in more advanced levels, they can communicate with each other about what they know • See article on “adding stupidity to AI”

  8. Example Problem: Tic-Tac-Toe

  9. Example Problem: Natural language processing • “Time flies like an arrow” • Grammatically valid interpretations: • 1. time passes quickly like an arrow • 2. command: time the flies the way an arrow times the flies • 3. command: only time those flies which are like an arrow • 4. “time-flies” are fond of an arrow

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