games and artificial intelligence prof. drs. dr. l.j.m.rothkrantz

Games and Artificial IntelligenceProf. drs. Dr. L.J.M.Rothkrantz

• Artificial players (bots, Quake)• Route finding• Sharing emotion

April 20, 2023 1

Quake III Arena Bot

J.M.P. van Waveren (id-SOFTWARE)L.J.M. Rothkrantz

Delft University of TechnologyDelft

Contents

• Introduction• Requirements• Bot architecture• Subsystems• Conclusion

All about what makes

my clock tick.

Introduction

Who am I ?

What game do I

play ?

Introduction

• Quake 1 (Omicron bot)• Quake 2 (Gladiator bot)• Quake 3 Arena• Hired by Software


What do we need? Guns, lots of guns!

Requirements

• Hard to distinguish from human player• Same game rules apply to bots and humans• Not allowed to cheat• Different bot characters• Play team games• Resource efficient (CPU & memory)• Commercial quality code• Easy extendable/modifiable architecture and

implementation


Hey now! I’m more than just bones!

Bot architecture

Team leader AI

Misc. AI AI Network Commands

CharacterFuzzy ChatsGoals Navigation

Area Awareness System Basic Actions

4th

3rd

2nd

1st

Bot architecture(information flow)

4th

3rd

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1st

Team leader AI


CharacterFuzzy ChatsGoalsNavigatio

n


Bot architecture(integration with game engine)

Game

Server

Client

Client Game

Renderer

Bot Lib (1st & 2nd layer)

Bot AI (3rd & 4th layer)

networking

Client code providing the IO functionality for human players

3D image

Player input

Sound

IntroductionIntroduction RequirementsRequirements Bot architectureBot architecture SubsystemsSubsystems ConclusionConclusion

Team leader AI



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Basic Actions

Team leader AI



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Area Awareness System

Team leader AI



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Waypoint system

AASMaze


• Quake3 maps much more complex than maze• Multi-level routing algorithm• Areas grouped in clusters.

Navigation

Team leader AI



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Navigation

Bot Characters

Team leader AI



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Bot Characters(characteristics)

Name Name of the bot.

Gender Gender of the bot ( male, female, it – mechanical creature ).

Attack skill How skilled the bot is when attacking.

Weapon weights File with weapon selection fuzzy logic.

View factor Scale factor for difference between current and ideal view angle to view angle change.View max change Maximum view angle change per second.

Reaction time Reaction time in seconds.

Aim accuracy Accuracy when aiming, a value between 0 and 1 for each weapon.

Aim skill Skill when aiming, a value between 0 and 1 for each weapon.

Chats File with individual bot chatter.

Characters per minute How fast the bot types.

Chat tendencies Tendencies to use specific chats when things happen.

Croucher Tendency to crouch.

Jumper Tendency to jump.

Walker Tendency to walk instead of run.

Weapon jumper Tendency to rocket jump.

Item weights File with item goal selection fuzzy logic.

Aggression Aggression of the bot.

Self preservation Self preservation of the bot.

Vengefulness How likely the bot is to take revenge.

Camper Tendency to camp.

Easy fragger Tendency to go for cheap kills.

Alertness How alert the bot is.

Fire throttle Tendency to fire continuously instead of pausing between shots.

Goal Selection

Team leader AI



n


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Goal Selection

?

Fuzzy weight = 0.8

Fuzzy weight = 0.5

Fuzzy weight = 0.7

Communication with chats

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Communication with chats

• Generating chat messages• Interpreting chat messages• Synonyms• Match templates

Communication with chats(match templates)

Grunt get the rocket launcher.

<variable> get the <variable>

Grunt get the rocket launcher.

Grunt rocket launcher.

Communication with chats(Eliza chats)

MrElusive: this game is fun!

Bot: are we having fun?

MrElusive: I like to shoot things.

Bot: I like to shoot things too.

MrElusive: I’m going to shoot you.

Bot: I don’t believe you are going to shoot me.

MrElusive: just wait and see!

Bot: don’t wait.

MrElusive: you are history when I find you

Bot: maybe I’m history when you find me, and maybe not.

MrElusive: be afraid.

Bot: are you afraid of big, mean gooses?

AI Network

Team leader AI



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AI Network

Seek Long TermGoal

Seek Short TermGoal

Seek ActivateEntity

RespawnStand

Battle Fight Battle Chase Battle RetreatBattle Short Term

Goal

AI Network(Seek LTG node example)

• Am I dead? If so go to ‘respawn’ node.• Is there an enemy I want to fight with? If so go to the ‘battle flight’

node.• If I reached my long term goal select new long term goal.• If there is a short goal go to the ‘seek short term goal’ node.• Predict obstacles that need to be handled (doors opened with buttons).• Navigate towards long term goal.• If I am blocked figure out how to deal with the obstacle.

Miscellaneous AI

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Fighting behaviour

• Acquiring enemies (visibility/audibility)• Selecting weapons (fuzzy logic)• Aiming at the enemy• Taking position and avoiding projectiles

Obstacles & Puzzles

IntroductionIntroduction RequirementsRequirements Bot architectureBot architecture SubsystemsSubsystems ConclusionConclusion

I rock!

Conclusion

• Fairly good artificial player• Cannot be distinguished from human at first sight• Same game rules apply and the bot does not

cheat• Versatile set of bot characters• Plays team based game types• Communicates with other players (bot/human)• Resource efficient (AAS)• Easy extendable/modifiable architecture and

implementation. Several other games and mods use the bot AI base.

Future directions

• Better anticipation of enemies• Environment analysis• Planning

You’re saying

you can

improve me?

A location based approach to distributed world-knowledge in mobile ad-hoc networks

ManetLoc

L.J.M.Rothkrantz, M. van Velden, D. Datcu

Agenda

• Introduction• Problem description• Implementation: ManetLoc• Demonstration• Future work• Questions

2. Without pre-setup infrastructure

• Unavailable• Wired network • GSM• GPS

• Available• PDA• Mobile Adhoc Networking (MANET)

3. Without pre-knowledge

?

• World model needed to solve problems• Crisis state• Locations

• Individuals• Exits

4. Process location information

• Input• User• Sensors• Other

• Output• World model

5. Fuse location information

• Match• Merge

6. Distributed

• No central server• Ad-hoc communication

• WiFi• Bluetooth

Implementation

• ManetLoc• Simulation• Based on AHS• Rectilinear world

• Intersections• Edges• Exits

Modules

• Exploration• Mapping• Distributing• Matching• Merging• Agent services

Exploration and mapping

• User input• Sensory data• World model• Closing loops

Guidance Take step(s)

Observe & reportUpdate world model

UserAgent

Distributing

A

B

C

B

A

C

• Check if useful• Convert to interpretable string• Broadcast

Matching

• Vertex matching• Growing hypotheses• Combining hypotheses

?

Map A

Map B

Possible Hypotheses

.

...

..

Merging

• Rotate • Shift • Add new vertexes • Connect

+ +

Map A Map B Selected Hypothesis Merged Map

Match

Merge

DeleteHyp?

Agent services

• Guidance• Exploration• Nearest exit

Results

• Tests• Usability• Correctness• Completeness• Performance

• Concept works• Better results for larger worlds• Most gain in pre-explored world

Future work

• Real life system• Input

• NLP• Iconic

• Data distribution• Dynamic data• Levels of detail• Planning• Execution

Questions

?

March 27, 2008

55

Emotion recognition using brain activity

Master thesis presentation

Robert Horlings

Media and Knowledge Engineering

April 20, 2023 56

Emotion

• Important in human communication• Influences decisions and behavior

Brain computing

• Direct information about emotion or brain processes.

• Applications such as• text writing• playing pong• moving mouse pointer

April 20, 2023 57

Emotion recognition

• From speech, text, facial expressions• Focus: brain activity

April 20, 2023 58

Applications

• Psychologists/doctors• Computer behavior depending on the user’s

emotion• Assisting patients with severe muscle diseases

April 20, 2023 59

Goal

Create a system for emotion recognition using brain activity and assess the quality of this emotion recognition in practice

April 20, 2023 60

Outline

• Brain activity• Emotion• Emotion and brain activity• Emotion recognition system• EEG recordings• Results• Demonstration• Conclusions

April 20, 2023 61

Brain activity

• Electroencephalography (EEG)• Measurement of brain activity• Small electric current

• Noise • Artifacts

April 20, 2023 62

Brain activity frequencies

• Splitting into different frequencies• Frequency bands with

specific characteristics

April 20, 2023 63

Emotion models

April 20, 2023 64

Emotion in brain activity

April 20, 2023 65

• Limbic system

• Difference between two hemispheres• Different frequencies

System

April 20, 2023 66

System structure

April 20, 2023 67

Preprocessing

April 20, 2023 68

Feature extraction

April 20, 2023 69

Seconds

Classification

April 20, 2023 70

Classification

April 20, 2023 71

EEG recordings

April 20, 2023 72

Emotion eliciting

April 20, 2023 73

Self assessments

April 20, 2023 74

Experimental design

April 20, 2023 75

Results

April 20, 2023 76

Results

Valence 100%

Arousal 93%

April 20, 2023 77

Results with new samples

April 20, 2023 78

Valence 32%

Arousal 37%

Improvements

April 20, 2023 79

Original

Valence 32%

Arousal 37%

Original Two classes1 – 5

Three classes1,2 – 3 – 4,5

Valence 32% 71% 37%

Arousal 37% 81% 49%

Demonstration

April 20, 2023 80

Conclusions

• Emotions can be recognized from EEG signals• Much background noise

• Extremes on both dimensions work best

April 20, 2023 81

Future work

• Measuring more data• Investigate new techniques

April 20, 2023 82

games and artificial intelligence prof. drs. dr. l.j.m.rothkrantz

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