Transcript document

CSCE 552 Spring 2011
AI II
By Jijun Tang
Announcements
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Second presentation: April 11th and 13th
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In class
Each group 12 minutes
What to show:
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Detailed designs of layers/modules/classes
Progress report with demo
3D/2D Models, graphics, etc
Schedules and milestones
Problems and planned changes
Two Bone IK
Disallowed
elbow
position
Shoulder
Wrist
Allowed
elbow
position
Game Agents
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May act as an
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Opponent
Ally
Neutral character
Continually loops through the
Sense-Think-Act cycle
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Optional learning or remembering step
Sense-Think-Act Cycle:
Sensing
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Agent can have access to perfect information
of the game world
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Game World Information
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May be expensive/difficult to tease out useful info
Players cannot
Complete terrain layout
Location and state of every game object
Location and state of player
But isn’t this cheating???
Sense-Think-Act Cycle:
Thinking
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Sensed information gathered
Must process sensed information
Two primary methods
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Process using pre-coded expert
knowledge
Use search to find an optimal solution
Sense-Think-Act Cycle:
Acting
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Sensing and thinking steps invisible to
player
Acting is how player witnesses intelligence
Numerous agent actions, for example:
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Change locations
Pick up object
Play animation
Play sound effect
Converse with player
Fire weapon
Command Hierarchy
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Strategy for dealing with decisions at
different levels
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From the general down to the foot soldier
Modeled after military hierarchies
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General directs high-level strategy
Foot soldier concentrates on combat
US Military Chain of
Command
Dead Reckoning
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Method for predicting object’s future position
based on current position, velocity and
acceleration
Works well since movement is generally
close to a straight line over short time
periods
Can also give guidance to how far object
could have moved
Example: shooting game to estimate the
leading distance
Emergent Behavior
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Behavior that wasn’t explicitly
programmed
Emerges from the interaction of
simpler behaviors or rules
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Rules: seek food, avoid walls
Can result in unanticipated individual or
group behavior
Flocking
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Example of emergent behavior
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Simulates flocking birds, schooling fish
Developed by Craig Reynolds: 1987
SIGGRAPH paper
Three classic rules
1. Separation – avoid local flockmates
2. Alignment – steer toward average
heading
3. Cohesion – steer toward average position
Formations
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Group movement technique
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Mimics military formations
Similar to flocking, but actually distinct
Each unit guided toward formation
position
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Flocking doesn’t dictate goal positions
Need a leader
Flocking/Formation
Influence Mapping
Level-of-Detail AI
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Optimization technique like graphical LOD
Only perform AI computations if player will
notice
For example
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Only compute detailed paths for visible agents
Off-screen agents don’t think as often
Manager Task Assignment
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Manager organizes cooperation between
agents
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Manager may be invisible in game
Avoids complicated negotiation and
communication between agents
Manager identifies important tasks and
assigns them to agents
For example, a coach in an AI football team
Obstacle Avoidance
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Paths generated from pathfinding
algorithm consider only static terrain,
not moving obstacles
Given a path, agent must still avoid
moving obstacles
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Requires trajectory prediction
Requires various steering behaviors
Scripting
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Scripting specifies game data or logic
outside of the game’s source language
Scripting influence spectrum
Level 0: Everything hardcoded
Level 1: Data in files specify stats/locations
Level 2: Scripted cut-scenes (non-interactive)
Level 3: Lightweight logic, like trigger system
Level 4: Heavy logic in scripts
Level 5: Everything coded in scripts
Example
Amit [to Steve]: Hello, friend! Steve
[nods to Bryan]: Welcome to CGDC.
[Amit exits left.]
Amit.turns_towards(Steve);
Amit.walks_within(3);
Amit.says_to(Steve, "Hello, friend!");
Amit.waits(1);
Steve.turns_towards(Bryan);
Steve.walks_within(5);
Steve.nods_to(Bryan);
Steve.waits(1);
Steve.says_to(Bryan, "Welcome to CGDC.");
Amit.waits(3);
Amit.face_direction(DIR_LEFT);
Amit.exits();
Scripting Pros and Cons
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Pros
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Scripts changed without recompiling
game
Designers empowered
Players can tinker with scripts
Cons
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More difficult to debug
Nonprogrammers required to program
Time commitment for tools
State Machine
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Most common game AI software pattern
Set of states and transitions, with only one
state active at a time
Easy to program, debug, understand
Stack-Based State Machine
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Also referred to as push-down
automata
Remembers past states
Allows for diversions, later returning to
previous behaviors
Example
Player escapes in combat, pop Combat off, goes to
search; if not find the player, pop Search off, goes
to patrol, …
Subsumption Architecture
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Popularized by the work of Rodney Brooks
Separates behaviors into concurrently running
finite-state machines
Well suited for character-based games where
moving and sensing co-exist
Lower layers
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Higher layers
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Rudimentary behaviors (like obstacle avoidance)
Goal determination and goal seeking
Lower layers have priority
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System stays robust
Example
Terrain Analysis
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Analyzes world terrain to identify
strategic locations
Identify
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Resources
Choke points
Ambush points
Sniper points
Cover points
Terrain:
Height Field Landscape
T op-Down View
T op-Down View (heights added)
Perspective View
Perspective View (heights added)
Locate Triangle on Height
Field
Essentially a 2D problem
z
Q
Q
R
R
x
Qz > Qx
Rz > 1 - Rx
Qz <= Qx
Rz <= 1 - Rx
Locate Point on Triangle
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Plane equation: Ax  By  Cz  D  0
A, B, C are the x, y, z components of
the triangle plane’s normal vector
Where D  N  P0
with one of the triangles
vertices being P0
Giving: N x x   N y  y   N z z    N  P0   0
Locate Point on Triangle-cont’d
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The normal can be constructed by taking the
cross product of two sides:
N  P1  P0   P2  P0 
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Solve for y and insert the x and z
components of Q, giving the final equation
for point within triangle:
 N x Q x  N z Q z  N  P0 
Qy 
Ny
Treating Nonuniform Polygon
Mesh
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Hard to detect the triangle where the
point lies in
Using Triangulated Irregular Networks
(TINs)
Barycentric Coordinates
Barycentric Coordinates
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Even with complex data structure, we
still have to test each triangle (in a sub
region) to see if the point lies in it
Using Barycentric Coordinates to do
the test
P2
Point = w0P0 + w1P1 + w2P2
Q
R
P1
P0
Q = (0)P0 + (0.5)P1 + (0.5)P2
R = (0.33)P0 + (0.33)P1 + (0.33)P2
Locate Point on Triangle Using
Barycentric Coordinates
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Calculate barycentric coordinates for point
Q in a triangle’s plane
 w1 
1
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w 
2
2 2
 2  V1 V2  V1  V2 
 V22
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 V1  V2
 V1  V2   S  V1 
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V12  S  V2 
S  Q  P0
V1  P1  P0
V2  P2  P0
w0  1  w1  w2
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If any of the weights (w0, w1, w2) are
negative, then the point Q does not lie in
the triangle
Trigger System
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Highly specialized scripting system
Uses if/then rules
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If condition, then response
Simple for designers/players to
understand and create
More robust than general scripting
Tool development simpler than general
scripting
Promising AI Techniques
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Show potential for future
Generally not used for games
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May not be well known
May be hard to understand
May have limited use
May require too much development time
May require too many resources
Bayesian Networks
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Performs humanlike reasoning when
faced with uncertainty
Potential for modeling what an AI
should know about the player
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Alternative to cheating
RTS Example
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AI can infer existence or nonexistence of
player build units
Example
Blackboard Architecture
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Complex problem is posted on a
shared communication space
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Agents propose solutions
Solutions scored and selected
Continues until problem is solved
Alternatively, use concept to facilitate
communication and cooperation
Decision Tree Learning
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Constructs a decision tree based on
observed measurements from game
world
Best known game use: Black & White
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Creature would learn and form “opinions”
Learned what to eat in the world based
on feedback from the player and world
Filtered Randomness
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Filters randomness so that it appears
random to players over short term
Removes undesirable events
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Like coin coming up heads 8 times in a row
Statistical randomness is largely preserved
without gross peculiarities
Example:
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In an FPS, opponents should randomly spawn
from different locations (and never spawn from
the same location more than 2 times in a row).
Genetic Algorithms
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Technique for search and optimization that
uses evolutionary principles
Good at finding a solution in complex or
poorly understood search spaces
Typically done offline before game ships
Example:
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Game may have many settings for the AI, but
interaction between settings makes it hard to
find an optimal combination
Flowchat
N-Gram Statistical Prediction
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Technique to predict next value in a
sequence
In the sequence 18181810181, it
would predict 8 as being the next value
Example
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In street fighting game, player just did
Low Kick followed by Low Punch
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Predict their next move and expect it
Neural Networks
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Complex non-linear functions that relate one
or more inputs to an output
Must be trained with numerous examples
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Training is computationally expensive making
them unsuited for in-game learning
Training can take place before game ships
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Once fixed, extremely cheap to compute
Example
Planning
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Planning is a search to find a series of
actions that change the current world state
into a desired world state
Increasingly desirable as game worlds
become more rich and complex
Requires
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Good planning algorithm
Good world representation
Appropriate set of actions
Player Modeling
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Build a profile of the player’s behavior
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Continuously refine during gameplay
Accumulate statistics and events
Player model then used to adapt the AI
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Make the game easier: player is not good at
handling some weapons, then avoid
Make the game harder: player is not good at
handling some weapons, exploit this weakness
Production (Expert) Systems
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Formal rule-based system
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Database of rules
Database of facts
Inference engine to decide which rules trigger –
resolves conflicts between rules
Example
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Soar used experiment with Quake 2 bots
Upwards of 800 rules for competent opponent
Reinforcement Learning
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Machine learning technique
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Discovers solutions through trial and
error
Must reward and punish at appropriate
times
Can solve difficult or complex problems
like physical control problems
Useful when AI’s effects are uncertain
or delayed
Reputation System
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Models player’s reputation within the game
world
Agents learn new facts by watching player
or from gossip from other agents
Based on what an agent knows
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Might be friendly toward player
Might be hostile toward player
Affords new gameplay opportunities
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“Play nice OR make sure there are no
witnesses”
Smart Terrain
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Put intelligence into inanimate objects
Agent asks object how to use it: how to
open the door, how to set clock, etc
Agents can use objects for which they
weren’t originally programmed for
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Allows for expansion packs or user created
objects, like in The Sims
Enlightened by Affordance Theory
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Objects by their very design afford a very
specific type of interaction
Speech Recognition
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Players can speak into microphone to
control some aspect of gameplay
Limited recognition means only simple
commands possible
Problems with different accents,
different genders, different ages (child
vs adult)
Text-to-Speech
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Turns ordinary text into synthesized speech
Cheaper than hiring voice actors
Quality of speech is still a problem
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Not particularly natural sounding
Intonation problems
Algorithms not good at “voice acting”: the mouth
needs to be animated based on the text
Large disc capacities make recording human
voices not that big a problem
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No need to resort to worse sounding solution
Weakness Modification
Learning
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General strategy to keep the AI from losing
to the player in the same way every time
Two main steps
1. Record a key gameplay state that precedes a
failure
2. Recognize that state in the future and change
something about the AI behavior
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AI might not win more often or act more intelligently,
but won’t lose in the same way every time
Keeps “history from repeating itself”
Artificial Intelligence: Pathfinding
PathPlannerApp Demo
Representing
the Search Space
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Agents need to know where they can move
Search space should represent either
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Search space typically doesn’t represent:
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Clear routes that can be traversed
Or the entire walkable surface
Small obstacles or moving objects
Most common search space representations:
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Grids
Waypoint graphs
Navigation meshes
Grids
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2D grids – intuitive world
representation
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Each cell is flagged
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Works well for many games including
some 3D games such as Warcraft III
Passable or impassable
Each object in the world can occupy
one or more cells
Characteristics of Grids
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Fast look-up
Easy access to neighboring cells
Complete representation of the level
Waypoint Graph
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A waypoint graph specifies lines/routes that
are “safe” for traversing
Each line (or link) connects exactly two
waypoints
Characteristics
of Waypoint Graphs
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Waypoint node can be connected to
any number of other waypoint nodes
Waypoint graph can easily represent
arbitrary 3D levels
Can incorporate auxiliary information
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Such as ladders and jump pads
Radius of the path
Navigation Meshes
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Combination of grids and waypoint graphs
Every node of a navigation mesh represents
a convex polygon (or area)
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Advantage of convex polygon
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As opposed to a single position in a waypoint
node
Any two points inside can be connected without
crossing an edge of the polygon
Navigation mesh can be thought of as a
walkable surface
Navigation Meshes (continued)
Computational Geometry
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CGAL (Computational Geometry
Algorithm Library)
Find the closest phone
Find the route from point A to B
Convex hull
Example—No Rotation
Space Split
Resulted Path
Improvement
Example 2—With Rotation
Example 3—Visibility Graph