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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
– In a known world, optimal solutions can be precomputed
– Entertainment criteria: smart enough to pose a challenge,
but not smart enough to be undefeatable
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
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.
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
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
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”
Example Problem: Tic-Tac-Toe
X
X
O
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