Introduction to AI in computer games

1Fanafzar Game Studio

• How do you think about AI?

What is AI


• The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956

• “The science and engineering of making intelligent machines” -- John McCarthy 1956

• The study and design of intelligent agents -- Russell &

Norvig

What is AI


• The Imitation Game (1950)

• A man (A), a woman (B), and an interrogator (C) who may be of either sex.

Turing Test


• The Imitation Game

• We now ask the question, What will happen when a machine takes the part of A in this game?

Turing Test


• Turing conjectured that, by the year 2000, a computer with a storage of 10^9 units could be programmed well enough to pass the test.

• The Turing test does not directly test whether the computer behaves intelligently

– Some human behavior is unintelligent

– Some intelligent behavior is inhuman

• Real intelligence vs. simulated intelligence

Turing Test


• The Chinese room is a thought experiment by John Searle which first appeared in his paper "Minds, Brains, and Programs", published in Behavioral and Brain Sciences in 1980.

Chinese room


Chinese room


• Searle argued that software could pass the Turing Test simply by manipulating symbols of which they had no understanding.

• Searle concludes—the Turing Test cannot prove that a machine can think.

Chinese room


• According to Strong AI, the correct simulation really is a mind.

• In 1931, Kurt Gödel proved that it is always possible to create statements that a formal system (such as an AI program) could not prove.

Strong AI


• According to Weak AI, the correct simulation is a modelof the mind.

• Can machines think?

– boats and submarines do move through the water but we do not call that swimming.

• Stuart Russell and Peter Norvig write: "AI researchers have devoted little attention to passing the Turing test."

Weak AI


• The way computers "think" is vastly different from the way a human thinks. --James Martin

• AI is faster and has a larger capacity for storage and memory than any human.

• The largest nerves in the brain can transmit impulses at around 90 meters per second, whereas a fiber optics connection can transmit impulses at 300 million meters per second, more than 3 million times faster.

Alien intelligence


• “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” --Tom M. Mitchell

• REcognition, classification

• Online and Offline learning

• Supervised, Unsupervised, Reinforcement

Machine Learning


• Classification

Machine Learning - Supervised


• Clustering

– We don’t know number of classes

Machine Learning - Unsupervised


X Y

12.37 15.64

22.8 7.8

34 17

91 50

11.9 17

44 19

80 45

21 9

33.31 16.5

79 39

… …

• Clustering

Machine Learning - Unsupervised


• The goal of a reinforcement learning agent is to collect as much reward as possible.

• Highly related to dynamic programming techniques

• Most famous technique is Q-learning

• Reinforcement Learning in First Person Shooter Games– IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES,

VOL. 3, NO. 1, MARCH 2011

• High-level Reinforcement Learning in Strategy Games– International Conference on Autonomous Agents and Multiagent Systems

Machine Learning - Reinforcement


• A simple idea: use the theory of evolution as an algorithm.

• A Population of Individuals

• Swarm intelligence

– Ant colony optimization

– Particle swarm optimization

– Bees algorithm

– Cuckoo search

Evolutionary Computing


• Individual = Chromosomes

• Mutation, Selection, and Crossover.

• Operating on dynamic data sets is difficult

• Tendency to converge towards local optima

• Randomness

Genetic Algorithm


• Simplified models of neural processing in the brain

Neural Networks


• Multilayer Perceptrons

Neural Networks


• How to train

• Black box

• Over fitting

• Computationally expensive

• Evolving Neural Controllers using GA for Warcraft 3-Real Time Strategy Game – 2011 Sixth International Conference on Bio-Inspired Computing

Neural Networks


• A good book about

GA and NN in games

Neural Networks


Do we really need AI in computer games?

What we expect from game AI?


• Efficiency

• Ease of Debugging (randomness)

• We don’t need general problem solver

• Believability

– We don’t need human level intelligence

– It doesn't really matter how NPC intelligence is

achieved, as long as the creatures in the game appear

believable.(weak AI)



• Video games provide a rich test bed for artificial intelligence methods

• Designers need to control the behavior of NPCs

– Explicit control

– Implicit control

• It is very genre specific

• Avoid artificial stupidity



• Agent cycle

Agents as NPCs


THINKSENSE ACT

• Agent cycle

Agents as NPCs


MAP

GEOMETRY

ENTITIES

. . .

THINK ACT

• Agent cycle

Agents as NPCs


REMEMBERR

EASO

N

BEH

AV

ESENSE ACT

• Agent cycle

Agents as NPCs


ANIMATE

NAVIGATE

. . .

THINKSENSE

• Rule : if (condition) then action

• Production Rule System comprised of a database of rules, each rule consists of an arbitrarily complex conditional statements.

• They are fairly uncommon approach.

Rule Based


• Search Methods, discovering a sequence of actions or states within a search space that satisfy some goal

• Goal-oriented behavior is still fairly rare in games.

Goal Oriented


• Everything in game world is triangle

• In door / Out door

• Path finding still is a problem

– Some pathfinding bugs(Video)

Introduction to Path planning


• Graph Theory – shortest path

• Single Source shortest path

• All pairs shortest path – Floyd

– Store the result

• Heuristic F(n) = D(n) + H(n)

A*/Dijkstra


• Near-Optimal Hierarchical Pathfinding,

– A. Botea, M. Muller, and J. Schaeffer, Journal of Game Development, Volume 1

Hierarchical Pathfinding


• Grid/Tile Base

– Fast

– Easy to develop

– Memory Inefficient

– 2D and strategy games

Reviewing some pathfinding methods


• Waypoint graphs

– Manual



• Waypoint graphs

– Automated : Point of visibility



• Worlds require a ridiculous number of waypoints

• Difficult dynamic obstacle avoidance, if not impossible

• Is not shortest path – not optimal

• Impossible to do path-smoothing

• Zig Zag path



Fanafzar Game Studio 40


Inefficient

• Convex Polygons

– Manual/Automated



• Shortest path - Optimal

• Smaller Search Space





OK, But how to

implement?



• Mesh simplification

• Rendering techniques

• Flood filling with AABBs (UDK)

• Voxelization

• Check Mikko Monone’s work, RecastNavigation

– http://digestingduck.blogspot.com

• The general process is as follows:

1. Voxelization

2. Generate Regions

3. Generate Contours

4. Generate Polygon Mesh

5. Generate Detailed Mesh

Navigation mesh generation process


1. Voxelization



2. Generate Regions



3. Generate ContoursDouglas-Peucker simplification



4. Generate Polygon Mesh

– Triangulation

– Merge to Convex Polygon

– Benefits of Convex Polygon



• Path find with A*

– Graph nodes are convex polygons

– Corridor map

– A* is not complex to implement

– There are many optimization techniques



• Pathfinding is Not A Star, AUTODESK® KYNAPSE ® MIDDLEWARE WHITE PAPER

– Path smoothing

– Path following

– Deal with other NPCs

– Deal with dynamic evolutions of game world



• Velocity obstacle

• There are some variations

– RVO, NLVO, FVO, HRVO, NHRVO, PVO

• Mikko uses RVO(Reciprocal Velocity Obstacles)



• Voxelization also used in Cover selection, Jumps, Camera movement

– Automatic annotations in Killzone 3 --Mikko Mononen, Paris Game AI Conference 2011



Navigation mesh, Zorvan Integration


• Generation Types

– Solo

– Tiled Navmesh

• Dynamic loading

• Deal with dynamic obstacles

• Off-Mesh Connections

• Convex polygons

Navigation mesh, Zorvan Integration


• How to simulate movement of intelligent objects like bird, animals, cars, etc. --Craig Reynols

• Stanley and Stella in: Breaking the Ice (1987)

Steering Behaviors


• Simple Vehicle Model

– Mass scalar

– Position vector

– Velocity vector

– max_force scalar

– max_speed scalar

Steering Behaviors


• Seek

• Flee

• Flocking

• Pursuit

• Arrival

• Obstacle avoidance

• Path follow

• Leader follow

• Hide

Steering Behaviors


• Seek Vs Flee

Steering Behaviors


• Seek

Vector2D SteeringBehaviors::Seek(Vector2D TargetPos)

{

Vector2D diff = TargetPos - m_pVehicle->Pos();

Vector2D DesiredVelocity =

Vec2DNormalize(diff) * m_pVehicle->MaxSpeed();

return (DesiredVelocity - m_pVehicle->Velocity());

}

Steering Behaviors


• Obstacle avoidance

– Only circles

Steering Behaviors


• Combining Steering Behaviors

– Weighted Truncated Sum

– Weighted Truncated Running Sum with Prioritization

– Prioritized Dithering

• Open Steer

Steering Behaviors


Advantages

– Simplicity

– Reliability

– Predictability

– Efficiency

Disadvantages

– Local traps• Oscillation

– Realism• Jagged paths

– Scalability

Steering Behaviors


• Pure scripting!

• Structured

– FSM

– HFSM

– Behavior Tree

Decision Making


• Theory (Simplified)

– A set states, S

– An input vocabulary, e

– Transition function, T(s, e)

• Map a state and an input to another state

FSM


Patrol (idle)

Combat

Return to post

PursuePlayer Seen

Near PlayerFar from postReach post

• Finite State Machine (FSM) is the Most Commonly

used Game AI Technology Today

– Simple

– Efficient

– Easily extensible

– Powerful enough to handle a wide variety of situations

• Decisions only depend on current state

FSM


• Hard Coded

– Switch Statement

– Function pointers

– Polymorphism (State Pattern)

• Interpreted

– Data Driven

– Scripted

• Compiled

– machine code

– Generating source code

FSM


• Function Pointer-Based, Embedded Finite-State Machines

– Chapter 3.1, Game programming gems 1

• A Finite-State Machine Class

– Chapter 3.3 Game Programming Gems 3

FSM


• Each state can be a complete state machine in its own right

• Original Paper

– Statecharts: A Visual Formalism for Complex Systems D. HarelScience of Computer Programming 8, 1987

• Clustering states (XOR)

• Concurrency (AND)

HFSM


• Example

HFSM


Patrol (idle)

Return to post

PursuePlayer Seen

Near PlayerFar from post

Reach post

Combat

Attack 1 Attack 2

Success

• Cluster states

• The semantic of D is exclusive-or (XOR) of A and C

HFSM


A

C

B

α

β

δ

γ(cond)

D

• Parallel (AND combination)

HFSM


A

C

α β

E

G

F

α

δ

γ μ

A DY


Iranvij AI Editor

• Halo 2 [Bungie Software, 2004] was one of the first high-profile games for which the use of behavior trees.

• Instead of a state, the main building block of a behavior tree is a Task

• Conditions, Actions, and Composites

Behavior tree


• Selector

Behavior tree


terminate

keep trying

?

• Sequence

Behavior tree


keep going

bail out

• Example

Behavior tree


?

DoorOpen?

Move(into room)

Move(to door)

Opendoor

Move(into room)

Condition Action

• Refactored tree

Behavior tree


?

DoorOpen?

Move(to door)

Opendoor

Move(into room)

• Decorators (such as Invertor)

• Random Selector

• Random Sequence

• Parallel


• Procedural Animation

• Procedural level generation

• Dynamic game difficulty balancing

Next Generation AI for game


• Artificial Intelligence For Games, Second Edition, Ian Millington, John Funge

• Programming Game AI by Example, Mat Buckland

• Game programming gems series

• AI Game Programming Wisdom series

• AI Game Development: Synthetic Creatures with Learning and Reactive Behaviors, Alex J. Champandard

• http://aigamedev.com/

• http://www.ai-blog.net/archives/000183.html

• http://www.critterai.org/nmgen_study

References


http://aigamedev.com/

http://www.ai-blog.net/archives/000183.html



http://www.critterai.org/nmgen_study

• http://digestingduck.blogspot.com/

References



Thank You

[email protected]

Introduction to AI in computer games

Technology

Transcript of Introduction to AI in computer games