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Introduction to AI in Computer Games Hojjat Jafary Fanafzar Game Studio November 2012 1 Fanafzar Game Studio

Transcript of 1Fanafzar Game Studio. How do you think about AI? What is AI 2Fanafzar Game Studio.

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Introduction to AI in Computer Games

Hojjat JafaryFanafzar Game Studio

November 2012

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• How do you think about AI?

What is AI

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• 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

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• The Imitation Game (1950)

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

Turing Test

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• The Imitation Game

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

Turing Test

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• 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

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• 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

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Chinese room

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• 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

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• 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

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• According to Weak AI, the correct simulation is a model of 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

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• 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

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• “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

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• Classification

Machine Learning - Supervised

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• 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

… …

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• Clustering

Machine Learning - Unsupervised

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• 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

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• 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

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• Individual = Chromosomes

• Mutation, Selection, and Crossover.

• Operating on dynamic data sets is difficult• Tendency to converge towards local optima• Randomness

Genetic Algorithm

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• Simplified models of neural processing in the brain

Neural Networks

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• Multilayer Perceptrons

Neural Networks

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• 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

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• A good book aboutGA and NN in games

Neural Networks

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Do we really need AI in computer games?

What we expect from game AI?

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• 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)

What we expect from game AI?

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• 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

What we expect from game AI?

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• Agent cycle

Agents as NPCs

THINKSENSE ACT

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• Agent cycle

Agents as NPCs

MAP

GEOMETRY

ENTITIES

. . .

THINK ACT

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• Agent cycle

Agents as NPCs

REMEMBERREASO

N

BEHAVESENSE ACT

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• Agent cycle

Agents as NPCs

ANIMATE

NAVIGATE

. . .

THINKSENSE

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• 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

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• 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

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• Everything in game world is triangle

• In door / Out door

• Path finding still is a problem– Some pathfinding bugs(Video)

Introduction to Path planning

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• 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

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• Near-Optimal Hierarchical Pathfinding,– A. Botea, M. Muller, and J. Schaeffer, Journal of Game

Development, Volume 1

Hierarchical Pathfinding

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• Grid/Tile Base– Fast– Easy to develop– Memory Inefficient– 2D and strategy games

Reviewing some pathfinding methods

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• Waypoint graphs– Manual

Reviewing some pathfinding methods

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• Waypoint graphs– Automated : Point of visibility

Reviewing some pathfinding methods

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• 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

Reviewing some pathfinding methods

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Reviewing some pathfinding methods

Inefficient

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• Convex Polygons– Manual/Automated

Reviewing some pathfinding methods

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• Shortest path - Optimal• Smaller Search Space

Reviewing some pathfinding methods

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Reviewing some pathfinding methods

OK, But how to

implement?

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Reviewing some pathfinding methods• Mesh simplification

• Rendering techniques

• Flood filling with AABBs (UDK)

• Voxelization

• Check Mikko Monone’s work, RecastNavigation– http://digestingduck.blogspot.com

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• The general process is as follows:

1. Voxelization2. Generate Regions3. Generate Contours4. Generate Polygon Mesh5. Generate Detailed Mesh

Navigation mesh generation process

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1. Voxelization

Navigation mesh generation process

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1. Voxelization

Navigation mesh generation process

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1. Voxelization

Navigation mesh generation process

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1. Voxelization

Navigation mesh generation process

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1. Voxelization

Navigation mesh generation process

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1. Voxelization

Navigation mesh generation process

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2. Generate Regions

Navigation mesh generation process

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2. Generate Regions

Navigation mesh generation process

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2. Generate Regions

Navigation mesh generation process

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3. Generate Contours

Navigation mesh generation process

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3. Generate Contours

Navigation mesh generation process

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3. Generate ContoursDouglas-Peucker simplification

Navigation mesh generation process

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3. Generate ContoursDouglas-Peucker simplification

Navigation mesh generation process

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3. Generate ContoursDouglas-Peucker simplification

Navigation mesh generation process

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4. Generate Polygon Mesh– Triangulation– Merge to Convex Polygon– Benefits of Convex Polygon

Navigation mesh generation process

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5. Generate Detailed Mesh

Navigation mesh generation process

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5. Generate Detailed Mesh

Navigation mesh generation process

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• Path find with A*

– Graph nodes are convex polygons

– Corridor map

– A* is not complex to implement

– There are many optimization techniques

Navigation mesh generation process

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• 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

Navigation mesh generation process

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• Velocity obstacle

• There are some variations – RVO, NLVO, FVO, HRVO, NHRVO, PVO

• Mikko uses RVO(Reciprocal Velocity Obstacles)

Navigation mesh generation process

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

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

Navigation mesh generation process

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Navigation mesh, Zorvan Integration

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• Generation Types– Solo– Tiled Navmesh

• Dynamic loading• Deal with dynamic obstacles

• Off-Mesh Connections• Convex polygons

Navigation mesh, Zorvan Integration

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

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

Steering Behaviors

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• Simple Vehicle Model– Mass scalar– Position vector– Velocity vector– max_force scalar– max_speed scalar

Steering Behaviors

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• Seek• Flee• Flocking• Pursuit• Arrival• Obstacle avoidance• Path follow• Leader follow• Hide

Steering Behaviors

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• Seek Vs Flee

Steering Behaviors

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• 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

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• Obstacle avoidance– Only circles

Steering Behaviors

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• Combining Steering Behaviors– Weighted Truncated Sum– Weighted Truncated Running Sum with Prioritization– Prioritized Dithering

• Open Steer

Steering Behaviors

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Advantages– Simplicity– Reliability– Predictability– Efficiency

Disadvantages– Local traps

• Oscillation

– Realism• Jagged paths

– Scalability

Steering Behaviors

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• Pure scripting!

• Structured – FSM– HFSM– Behavior Tree

Decision Making

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• 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

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• 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

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• Hard Coded– Switch Statement– Function pointers– Polymorphism (State Pattern)

• Interpreted– Data Driven– Scripted

• Compiled– machine code– Generating source code

FSM

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• 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

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

• Original Paper – Statecharts: A Visual Formalism for Complex Systems D. Harel

Science of Computer Programming 8, 1987

• Clustering states (XOR)• Concurrency (AND)

HFSM

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• Example

HFSM

Patrol (idle)

Return to post

PursuePlayer Seen

Near PlayerFar from post

Reach post

Combat

Attack 1 Attack 2

Success

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

HFSM

A

C

B

α

β

δ

γ(cond)

D

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• Parallel (AND combination)

HFSM

A

C

α β

E

G

F

α

δ

γ μ

A DY

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Iranvij AI Editor

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Iranvij AI Editor

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• 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

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• Selector

Behavior tree

terminate

keep trying

?

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• Sequence

Behavior tree

keep going

bail out

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• Example

Behavior tree

?

DoorOpen?

Move(into room)

Move(to door)

Opendoor

Move(into room)

Condition Action

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• Refactored tree

Behavior tree

?

DoorOpen?

Move(to door)

Opendoor

Move(into room)

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• Decorators (such as Invertor)

• Random Selector

• Random Sequence

• Parallel

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• Procedural Animation

• Procedural level generation

• Dynamic game difficulty balancing

Next Generation AI for game

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• 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

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• http://digestingduck.blogspot.com/

References

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Thank You

[email protected]