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CSCE 590E Spring 2007

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CSCE 590E Spring 2007 Powered By Docstoc
					CSCE 590E Spring 2007
AI/Audio By Jijun Tang

Announcements


April 30th: demos
 

Show progress/difficulties Risk analysis No extension is possible Make sure you equipment works on site
Anyone can contact their high school admin to arrange direct talks to students Of course, in SC only



Final Demo: May 8th, 5:30pm, Amoco Hall
 



High school outreach
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Homework
 



Page 591 question 1 5 points, due on Monday Hardcopy should be turned in before the class

Artificial Intelligence






Intelligence embodied in a man-made device Human level AI still unobtainable The difficulty is comprehension

Goals of an AI Game Programmer
Different than academic or defense industry 1. AI must be intelligent, yet purposely flawed 2. AI must have no unintended weaknesses 3. AI must perform within the constraints 4. AI must be configurable by game designers or players 5. AI must not keep the game from shipping

Game Agents


May act as an
  

Opponent Ally Neutral character



Continually loops through the Sense-Think-Act cycle


Optional learning or remembering step

Sense-Think-Act Cycle: Sensing


Agent can have access to perfect information of the game world


May be expensive/difficult to tease out useful info
Complete terrain layout Location and state of every game object Location and state of player



Game World Information
  



But isn‟t this cheating???

Sense-Think-Act Cycle: Thinking
 



Sensed information gathered Must process sensed information Two primary methods




Process using pre-coded expert knowledge Use search to find an optimal solution

Sense-Think-Act Cycle: Acting


 

Sensing and thinking steps invisible to player Acting is how player witnesses intelligence Numerous agent actions, for example:
   
 

Change locations Pick up object Play animation Play sound effect Converse with player Fire weapon

Extra Step in Cycle: Learning and Remembering
 

Optional 4th step Not necessary in many games




Agents don‟t live long enough Game design might not desire it

Making Agents Stupid


Sometimes very easy to trounce player


Make agents faster, stronger, more accurate



Sometimes necessary to dumb down agents, for example:
   

Make shooting less accurate Make longer reaction times Engage player only one at a time Change locations to make self more vulnerable

Agent Cheating


Players don‟t like agent cheating


When agent given unfair advantage in speed, strength, or knowledge



Sometimes necessary
  

For highest difficultly levels For CPU computation reasons For development time reasons
Consider letting the player know upfront



Don‟t let the player catch you cheating!


Common Game AI Techniques
    

  

A* Pathfinding Command Hierarchy Dead Reckoning Emergent Behavior Flocking Formations Influence Mapping …

A* Pathfinding






Directed search algorithm used for finding an optimal path through the game world Used knowledge about the destination to direct the search A* is regarded as the best
  

Guaranteed to find a path if one exists Will find the optimal path Very efficient and fast

Command Hierarchy


Strategy for dealing with decisions at different levels


From the general down to the foot soldier General directs high-level strategy Foot soldier concentrates on combat



Modeled after military hierarchies
 

Dead Reckoning




 

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




Behavior that wasn‟t explicitly programmed Emerges from the interaction of simpler behaviors or rules
 

Rules: seek food, avoid walls Can result in unanticipated individual or group behavior

Flocking
 

Example of emergent behavior


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


Group movement technique


Mimics military formations





Similar to flocking, but actually distinct Each unit guided toward formation position


Flocking doesn‟t dictate goal positions

Influence Mapping


 


Method for viewing/abstracting distribution of power within game world Typically 2D grid superimposed on land Unit influence is summed into each grid cell


Unit influences neighboring cells with falloff
Can identify the “front” of the battle Can identify unguarded areas Plan attacks Sim-city: influence of police around the city

Facilitates decisions
 

 

Level-of-Detail AI


 

Optimization technique like graphical LOD Only perform AI computations if player will notice For example
 

Only compute detailed paths for visible agents Off-screen agents don‟t think as often

Manager Task Assignment


Manager organizes cooperation between agents




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

Terrain Analysis
 

Analyzes world terrain to identify strategic locations Identify
    

Resources Choke points Ambush points Sniper points Cover points

Trigger System
  

Highly specialized scripting system Uses if/then rules


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
 

Show potential for future Generally not used for games


  


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
 

Performs humanlike reasoning when faced with uncertainty Potential for modeling what an AI should know about the player


Alternative to cheating



RTS Example


AI can infer existence or nonexistence of player build units

Blackboard Architecture


Complex problem is posted on a shared communication space
 



Agents propose solutions Solutions scored and selected Continues until problem is solved



Alternatively, use concept to facilitate communication and cooperation

Decision Tree Learning




Constructs a decision tree based on observed measurements from game world Best known game use: Black & White
 

Creature would learn and form “opinions” Learned what to eat in the world based on feedback from the player and world

Filtered Randomness


  

Filters randomness so that it appears random to players over short term Removes undesirable events


Like coin coming up heads 8 times in a row

Statistical randomness is largely preserved without gross peculiarities Example:


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




 

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:


Game may have many settings for the AI, but interaction between settings makes it hard to find an optimal combination

N-Gram Statistical Prediction






Technique to predict next value in a sequence In the sequence 18181810181, it would predict 8 as being the next value Example


In street fighting game, player just did Low Kick followed by Low Punch


Predict their next move and expect it

Neural Networks




Complex non-linear functions that relate one or more inputs to an output Must be trained with numerous examples




Training is computationally expensive making them unsuited for in-game learning Training can take place before game ships


Once fixed, extremely cheap to compute

Planning


 

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

 

Good planning algorithm Good world representation Appropriate set of actions

Player Modeling


Build a profile of the player‟s behavior
 

Continuously refine during gameplay Accumulate statistics and events 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



Player model then used to adapt the AI




Production (Expert) Systems


Formal rule-based system
  

Database of rules Database of facts Inference engine to decide which rules trigger – resolves conflicts between rules



Example
 

Soar used experiment with Quake 2 bots Upwards of 800 rules for competent opponent

Reputation System


 

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
 

Might be friendly toward player Might be hostile toward player “Play nice OR make sure there are no witnesses”



Affords new gameplay opportunities


Smart Terrain


 

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


Allows for expansion packs or user created objects, like in The Sims Objects by their very design afford a very specific type of interaction



Enlightened by Affordance Theory


Speech Recognition






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
  

Turns ordinary text into synthesized speech Cheaper than hiring voice actors Quality of speech is still a problem
  

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


No need to resort to worse sounding solution

Artificial Intelligence: Pathfinding

Introduction




 

Almost every game requires pathfinding Agents must be able to find their way around the game world Pathfinding is not a trivial problem The fastest and most efficient pathfinding techniques tend to consume a great deal of resources

Representing the Search Space




Agents need to know where they can move Search space should represent either
 

Clear routes that can be traversed Or the entire walkable surface Small obstacles or moving objects
Grids Waypoint graphs Navigation meshes

 

Search space typically doesn‟t represent:


Most common search space representations:
 



Grids


2D grids – intuitive world representation


Works well for many games including some 3D games such as Warcraft III Passable or impassable



Each cell is flagged




Each object in the world can occupy one or more cells

Characteristics of Grids
  

Fast look-up Easy access to neighboring cells Complete representation of the level

Waypoint Graph




A waypoint graph specifies lines/routes that are “safe” for traversing Each line (or link) connects exactly two waypoints

Characteristics of Waypoint Graphs






Waypoint node can be connected to any number of other waypoint nodes Waypoint graph can easily represent arbitrary 3D levels Can incorporate auxiliary information



Such as ladders and jump pads Radius of the path

Navigation Meshes




Combination of grids and waypoint graphs Every node of a navigation mesh represents a convex polygon (or area)


As opposed to a single position in a waypoint node Any two points inside can be connected without crossing an edge of the polygon



Advantage of convex polygon




Navigation mesh can be thought of as a walkable surface

Navigation Meshes (continued)

Characteristics of Navigation Meshes
 



Complete representation of the level Ties pathfinding and collision detection together Can easily be used for 2D and 3D games

Searching for a Path


 

A path is a list of cells, points, or nodes that an agent must traverse A pathfinding algorithm finds a path


From a start position to a goal position

The following pathfinding algorithms can be used on
 


Grids Waypoint graphs Navigation meshes

Criteria for Evaluating Pathfinding Algorithms
 

Quality of final path Resource consumption during search


CPU and memory A complete algorithm guarantees to find a path if one exists



Whether it is a complete algorithm


Random Trace


Simple algorithm
  

Agent moves towards goal If goal reached, then done If obstacle


Trace around the obstacle clockwise or counter-clockwise (pick randomly) until free path towards goal



Repeat procedure until goal reached

Random Trace (continued)


How will Random Trace do on the following maps?

Random Trace Characteristics
 



Not a complete algorithm Found paths are unlikely to be optimal Consumes very little memory

Understanding A*


To understand A*


First understand Breadth-First, Best-First, and Dijkstra algorithms



These algorithms use nodes to represent candidate paths

Understanding A*
class PlannerNode { public: PlannerNode *m_pParent; int m_cellX, m_cellY; ... };


The m_pParent member is used to chain nodes sequentially together to represent a path

Understanding A*


All of the following algorithms use two lists
 

The open list The closed list

 

Open list keeps track of promising nodes When a node is examined from open list


Taken off open list and checked to see whether it has reached the goal Used to create additional nodes Then placed on the closed list



If it has not reached the goal
 

Overall Structure of the Algorithms
1. Create start point node – push onto open list 2. While open list is not empty
A. Pop node from open list (call it currentNode) B. If currentNode corresponds to goal, break from step 2 C. Create new nodes (successors nodes) for cells around currentNode and push them onto open list D. Put currentNode onto closed list

Breadth-First


Finds a path from the start to the goal by examining the search space ply-by-ply

Breadth-First Characteristics
  

Exhaustive search


Systematic, but not clever

Consumes substantial amount of CPU and memory Guarantees to find paths that have fewest number of nodes in them


Not necessarily the shortest distance!



Complete algorithm

Best-First






Uses problem specific knowledge to speed up the search process Head straight for the goal Computes the distance of every node to the goal


Uses the distance (or heuristic cost) as a priority value to determine the next node that should be brought out of the open list

Best-First (continued)

Best-First (continued)


Situation where Best-First finds a suboptimal path

Best-First Characteristics
 



Heuristic search Uses fewer resources than BreadthFirst Tends to find good paths


No guarantee to find most optimal path



Complete algorithm

Dijkstra


Disregards distance to goal
 

Keeps track of the cost of every path No guessing



Computes accumulated cost paid to reach a node from the start


Uses the cost (called the given cost) as a priority value to determine the next node that should be brought out of the open list

Dijkstra Characteristics
 

 

Exhaustive search At least as resource intensive as Breadth-First Always finds the most optimal path Complete algorithm

A*


Uses both heuristic cost and given cost to order the open list
Final Cost = Given Cost + (Heuristic Cost * Heuristic Weight)



A* (continued)


Avoids Best-First trap!

A* Characteristics
 





Heuristic search On average, uses fewer resources than Dijkstra and Breadth-First Admissible heuristic guarantees it will find the most optimal path Complete algorithm

Summary


Two key aspects of pathfinding:
 

Representing the search space Searching for a path

Audio Programming

Audio Programming
Audio in games is more important than ever before

Programming Basic Audio


Most gaming hardware has similar capabilities (on similar platforms)


Mostly programming interfaces differ



Learning fundamental concepts of audio programming is important

API Choices
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DirectSound (part of DirectX API)
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Only available on Windows platforms Newer API Available on multiple platforms Typically available on consoles Can offer broad cross-platform solutions



OpenAL
 

 

Proprietary APIs
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3rd Party Licensable APIs


Analog Sound Wave

Basic Audio Terminology and Physics
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Amplitude
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Measurement of a sound wave‟s pressure Measurement of the interval between wave cycles, typically measured in Hertz The perception of frequency
Musical distribution of frequencies over keys Measures sound amplitude



Frequency
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
 

Pitch
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Tuning


Decibel
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Digital Representation of a Sound Wave

Digital Representation of a Sound Wave


Most common technique known as sampling










Sampling involves measuring the amplitude of the analog wave file at discrete intervals The frequency of sampling is known as sampling rate Each sample is typically stored in a value ranging from 4 to 24 bits in size The size of the sample value in bits is known as the „bit depth‟ Music CDs have a sample rate and bit depth of 44.1 kHz (samples/sec) and 16 bits (sample size)

Quantization Error in Sampling

Bit Depth and Signal Noise


Bit depth of sample data affects signal noise






Signal to noise ratio = number of available bits / 1 For example, 8-bit samples have a 256:1 SNR (~48 dB), and 16-bit samples have a 65,536:1 SNR (~96 dB) Decibel ratio is calculated using 10 x log10 (ratio) or 8.685890 x log e (ratio)

Sampling Frequency and Frequency Reproduction
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

Sampling frequency affects range and quality of high-frequency reproduction Nyquist Limit
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Frequencies up to one-half the sampling rate can be reproduced Audio quality degrades as frequency approaches this limit

Sampling Errors vs. Sampling Frequency

Modern Audio Hardware


Samples are piped into sound “channels”


Often a hardware pipeline from this point

 

Various operations, such as volume, pan, and pitch may be applied 3D sounds may apply HRTF algorithms and/or mix the sound into final output buffers.

Sound Playback Techniques


Two basic playback methods:
1. Play sample entirely from memory buffer 2. Stream data in real-time from storage medium




Streaming is more memory efficient for very large audio files, such as music tracks, dialogue, etc Streaming systems use either a circular buffer with read-write pointers, or a doublebuffering algorithm

Playback

Sample Playback and Manipulation


Three basic operations you should know


Panning is the attenuation of left and right channels of a mixed sound


Results in spatial positioning within the aural stereo field





Pitch allows the adjustment of a sample‟s playback frequency in real-time Volume control typically attenuates the volume of a sound


Amplification is generally never supported

Compressed Audio Format


Compressed audio formats allow sound and music to be stored more compactly


Bit reduction codecs generally are lightweight


ADPCM compression is implemented in hardware on all the major current video game console systems



Psycho-acoustic codecs often have better compression



Discard sounds our ears would not typically be able to hear Require substantially more computational horsepower to decode

MP3, Ogg Vorbis, Licensing & Patent Issues


The MP3 format is patented


Any commercial game is subject to licensing terms as determined by Fraunhofer & Thompson Multimedia, the holders of the patents Open source and patent-free (royalty-free)



Ogg Vorbis is similar to MP3 in many ways




Be aware of patent and license restrictions when using 3rd party software

3D Audio


Two sets of data required when working in world coordinates:


Listener Data


Composed of world position and orientation (virtual microphone in the world) Composed of sound position, orientation, velocity, etc (virtual sound source in the world)



Source Data


Listener/Source

Environmental Effects




Environmental effects nearly always implemented in hardware Sound transmission is categorized in three ways
  

Direct transmission Early reflections (echo) Late reflections (reverberation)

Sound Transmission Categories

Environmental Effects Standards


EAX 2.0 and beyond




EAX 2.0 developed by Creative Labs and released as an open standard EAX 3.0 and 4.0 remain proprietary Creative Labs standards



I3DL2


Open standard developed by IA-SIG, similar to EAX 2.0 in functionality

Programming Music Systems


Two common music systems


MIDI-based systems


(Musical Instrument Digital Interface) (CD audio, MP3 playback, etc)



Digital audio streaming systems


Advantages and Disadvantages of MIDI
 





Actual music data size is negligible Easy to control, alter, and even generate in real-time High quality music is more difficult to compose and program Only effective if you can guarantee playback of a common instrument set

Example

Other MIDI-based technologies to be aware of


DLS (DownLoadable Sound) Format


A standardized format for instrument definition files



iXMF (Interactive eXtensible Music Format)


New proposed standard for a container format for interactive music

Advantages / Disadvantages of Digital Audio Streams








Superb musical reproduction is guaranteed Allows composers to work with any compositional techniques Some potential interactivity is sacrificed for expediency and musical quality Generally high storage requirements

A Conceptual Interactive Music Playback System
 



Divide music into small two to eightbar chunks that we‟ll call segments. A network of transitions from segment to segment (including loops and branches) is called a theme. Playing music is now as simple as choosing a theme to play. The transition map tracks the details.

Advanced Audio Programming




 


3D Audio Environmental Effects Integration Audio Scripting and Engine Integration Lip-sync Technology Advanced Voice Playback Voice Recognition

3D Audio Environmental Effects Integration


Environmental effects should be driven by a room‟s shape and material composition.




Can determining the optimal effect settings be done automatically? This may be important as game worlds become larger and more complex

3D Audio Environmental Effects Integration (cont)


Sound occlusion and damping is a particularly difficult problem to solve






This is essentially a pathfinding problem for audio. Doors can dynamically affect a sound‟s properties Very few titles have even attempted a robust, general-purpose, and automated solution to these problems.

Room Acoustics

Dynamic Occlusion

Audio Scripting and Engine Integration








Very little audio programming should be done by general game programmers Game Engine should offer robust support for audio triggers and scripts Engine should deal with audio scripts, not “sound files” Why is this so important?

Audio Scripting


Many situations require much more information than can be embedded in a linear audio file


  

Sound Variation Sound Repetition Complex Sound Looping Background Ambience

Lip-sync Technology


Lip-sync technology is a blending of audio and visual techniques to create realisticlooking speech by in-game actors.


 

Simple techniques such as waveform amplitude measurement has worked previously, but… In future titles, it will be considered inadequate. Much work can still be done in this field.

Advanced Voice Playback






Real-time spoken feedback is especially important in sports titles (simulated announcers) Game are reaching the limits of what current techniques (canned, prerecorded phrases combined in series) can provide. Again, this is an opportunity for future groundbreaking audio work.

Voice Recognition






Spoken commands are much easier to deliver in certain situations. A great example of this? Squad-based tactical shooters. Current generation systems are still very error prone. A great opportunity for breakout audio technology.


				
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