AI in games
Roger Crawfis/Eric Fosler-Lussier
CSE 786 Game Design
AI vs. AI for games
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AI for games poses a number of unique design
challenges
Traditional AI:
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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:
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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 undefeatible
Injecting AI into a Game
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Friend:
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Foe:
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autonomous, intelligent NPC helpmates
Configurable (scripted) behaviors: different characters
solve a problem in different ways
Player may trade places with NPC: automation
Opponents get better with time
Opponents are less predictable because the
individuals’ behavior is not uniform
Scene Clutter
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Provides a richness to your environment.
Animals grazing, birds flying, people milling about,
automobiles driving, etc.
Goals for your games
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Prepare you for entertainment and ‘serious’ games
You must implement at least one of these features
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Improve over time
Goal-driven action decomposition
A* search
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for path planning, with varying cost functions for different agents
(different agents have different heuristics)
Game tree search
Emergent group behavior (flocking or formations)
“Intelligent” interaction (e.g. speech/language)
Your game AI must be data-driven, i.e. it must tune
itself or otherwise interoperate with an arbitrary
map/level loaded at runtime
Warning: AI is not an afterthought
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AI elements need to be designed in from
the start
Configurable/scripted behavior
Data-driven map/level layouts
AI isn’t time-consuming to create, if coded
from the start
Improving over time
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Agent behavior needs to be composed from
small, modular, context-sensitive actions
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Evade-right if angle >90 and < 180
Reward good performance/punish bad
Each re-spawn can modify control strategy
See good discussion in Buckland online book
You can either perform training and freeze
optimal play, or let the agent adapt to each user
Improving over time
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Example from Buckland (online) Ch 10: brainy aliens
Map sensory input to behavior with a hidden layer
The time it stayed alive is fitness function
When alien dies, replace it with an evolved one
Goal-driven behavior
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Multiple steps required to achieve a desired
effect
Useful in
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Action-adventure type games -puzzles to solve
RPG - task underlings with a multi-step job
Good discussion in Buckland PGAIE ch. 9
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Each ‘goal’ is an instance of a composite class
Many different goals can be created with
minimal coding
Goal-driven agents
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Different classes of agents solve problem
in different ways, based on their abilities
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“Block door”:
Strong trolls move boulders in the way
 Small hobbits shovel sand into the opening
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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
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In more complex games, might have goal queue
A* path planning
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Smart path planning/locomotion using
waypoints/grabpoints built into the world
Board/card games like chess/poker are
impossible to completely search, heuristics
are required and search cutoffs
flocking/formation
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Agents have small goals to balance that
result in emergent behavior
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Flocking: stay close, navigate toward centroid
of flock, avoid collision
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Results in a ‘crowd’ behavior look
Formations: flanking/center/right wing, etc.
Individuals stay evenly spaced within their group
 Each agent chooses a position within the formation
 One agent or central planner selects formation
 Results in a more disciplined/organized look (Orcs)
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Depends on how organized you want the
agents to appear
Flocking demos
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See Craig Reynold’s Steering behaviors:
http://www.red3d.com/cwr/steer/
“Intelligent” interaction
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NPCs might communicate state via
language
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E.g. Wii Fit, “All your bases belong to us”
Complete job is WAAAAY too hard
Input: probably beyond scope of project
 Output: May be able to do simple state-conditional
things (mocking, encouragement)
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Scripting languages can be used for rapid
deployment
Other AI principles: observability
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Don’t let the agents have perfect
knowledge: they have to operate in the
environment like the players do
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Sense and remember events in their sensory
horizon, memories can have a TTL
Perhaps in more advanced levels, they can
communicate with each other about what
they know
See article on “adding stupidity to AI”