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Vorlesung Wissensbasierte Systeme 

Wissensbasierte Systeme 

WS 2003/04 

Abschnitt 1: Einfuehrung 

Organisatorisches, Motivation, Geschichte, 

Herangehensweisen, Beispiele 

Michael Beetz 

Michael Beetz, TU München 1

§ Vorlesungszeit und –ort: 


Organisatorisches 

§ Mo 11:00-12:00, MI 00.04.011 

§ Di 12:00-13:00, MI 00.08.011 

§ Uebung: 

§ Di 13:00-14:00, MI 00.04.01 

§ Dozent: 

§ Michael Beetz 

§ Webseite: 

§ http://wwwradig.in.tum.de/vorlesungen/wibas.WS03/ 



Vorlesungsunterlagen 

o Die Vorlesung basiert auf: 

Artificial Intelligence 

-A Modern Approach 

Stuart Russell and Peter Norvig 

o In der Bibliothek. Im Buchladen ca 45 

Euro. 

o Kopien der Vorlesungsfolien und weitere 

Informationen ueber die WWW-Homepage 


Lernziele der heutigen Vorlesung 

§ Diskussion des Begriffes “Kuenstliche 

Intelligenz” 

§ Gefuehl fuer die Loesbarkeit von Problemen 

§ Beispiele fuer erfolgreiche KI Systeme 



Was ist Kuenstliche Intelligenz? 

Intelligenz 

§ Kuenstliche Intelligenz untersucht 

intelligente Entitaeten 

§ Im Gegensatz zur Philosophie und Psychologie: 

konstruktiv 

§ Untersuchung von intelligenten Systemen als 

Performanzsysteme 

§ Zentrale Fragestellung: Wie kann man mit einem 

kleinen und aeusserst langsamen (elektronischen) 

“Gehirn” eine komplexe Welt wahrnehmen, verstehen 

und in ihr kompetent handeln. 



Systemsthat think like humans. 

Reasoning 

Behavior 

Systems that act like humans. 


Sichtweisen der KI 

(Russell and Norvig) Norvig 

Human Performance Ideal Intelligence 

Systems that think rationally. 

Systems that act rationally. 


Systeme, Systeme, 

die menschlich handeln 

Alan Turing (1950) “Computing machinery and intelligence” 

§ “Can machine think?” -> “can machines behave 

intelligently?” 

§ Durchfuehrbarer Test: Imitationsspiel “Turing Test” 

§ Sah die wichtigsten Argumente gegen KI, die in den 

nachfolgenden 50 Jahren vorgebracht wurden, 

voraus 

§ Schlug folgende Komponenten der KI vor: Wissen, 

Schlussfolgern, Sprachverstehen, Lernen 

Problem: Turing Test ist nicht reproduzierbar, 

konstruktiv, oder zugaenglich mit mathematischen 

Modellen. 




die menschlich denken 

§ Welche kognitiven Faehigkeiten sind 

notwendig, um intelligente Leistungen zu 

erbringen?“ 

§ Information processing psychology, cognitive 

science, cognitive neuro science 

§ Erfordert wissenschaftliche Theorien der 

internen Gehirnaktivitaeten 

§ Was ist die richtige Abstraktionsstufe? 

Wissen oder Neuronen 

§ Wie kann man die Theorien validieren? 

§ Vorhersage und Analyse des Verhaltens von Testpersonen 

§ Analyse neurologischer Daten 




die rational denken 

§ Wertend (oder vorschreibend) anstatt beschreibend 

§ Aristoteles: was sind die korrekten Gesetze des 

Denkens 

§ Einige griechische Philosophieschulen entwickeln 

Logiken: Schreibweisen und Ableitungsregeln des 

Schlussfolgerns 

§ Probleme: 

§ Nur ein Teil des intelligenten Verhaltens beruht auf 

korrekter logischer Ueberlegung 

§ Was ist der Zweck des Denkens? 




die rational handeln 

§ Rationale Agenten (oder rationale Akteure) 

§ Ein rationaler Agent agiert so, dass er seine 

gegebenen Ziele erreicht unter der Voraussetzung, 

dass seine Eindruecke von der Welt und seine 

Ueberzeugungen richtig sind. 

§ Das Richtige tun!!! 

§ Rationales Denken ist eine Voraussetzung fuer 

rationales Handeln, allerdings keine notwendige 

Voraussetzung. 

§ was z.B., wenn nicht genuegend Information 

vorliegt, um die richtige Entscheidung zu treffen? 




Diese Vorlesung… 

Vorlesung 

Systeme die rational agieren 

oder 

das Richtige tun! 

tun 


Kuenstliche Intelligenz: Intelligenz: 

Was ist 


machbar? machbar 

Kann derzeit … 

§ Ein autonomes Fahrzeug auf einer kurvigen 

Gebirgsstrasse oder mit 120km/h auf der Autobahn 

fahren? 

§ Ein Auto durch die Stadtmitte von Muenchen 

fahren? 

§ Ein Software-Agent Lebensmittel im WWW 

einkaufen? 

§ Ein autonomer Roboter im Supermarkt einkaufen? 

§ Ein Roboter akzeptables Tischtennis spielen? 

§ Ein Computerprogramm den Schachweltmeister 

schlagen? Weltklasse Backgammon spielen? 

§ Ein Computerprogramm in Echtzeit 

Deutsch/Japanisch uebersetzen? 


Kuenstliche Intelligenz: Intelligenz: 

Was ist 


machbar? machbar 

Kann derzeit … 

§ Ein Kontrollsystem eine Raumsonde steuern und 

wissenschaftliche Experimente durchfuehren? 

§ Ein Computerprogramm interessante mathematische 

Theoreme finden und beweisen? 

§ Ein Computerprogramm in spezialisierten 

Rechtsbereichen kompetent beraten? In 

medizinischen Bereichen mit Expertenkompetenz 

diagnostizieren? 

§ Ein Computerprogramm Satellitenbilder automatisch 

klassifizieren um interessante 

Wetterkonstellationen zu finden? 

§ Automatisches Sprachverstehen fuer einen 

Reiseberater 


Ueberraschungen in der KI Forschung 

Aufgaben, die fuer Menschen schwierig sind, 

haben sich als leicht herausgestellt 

§ Schach 

§ Dame, Othello 

§ Missionsplanung, Ablaufplanung fuer Space 

Shuttle Countdown 

§ Automatisches Beweisen 

§ Post sortieren 



Ueberraschungen in der KI Forschung 

Aufgaben, die fuer Menschen leicht sind, haben 

sich als schwierig herausgestellt 

§ Spracherkennung 

§ Gesichtserkennung 

§ Autonome Navigation 

§ Motorik 

§ Sprachverstehen 

§ Alltagsschliessen 




Artificial Intelligence 

addresses: 

§ The construction of intelligent machines 

(whether or not they act like humans) 

§ The formalization of knowledge and 

mechanization of reasoning (both common 

sense and expert) 

§ The use of computational models to 

understand the psychology and behavior of 

people, animals, and artificial agents 

§ Making working with computers as easy and 

helpful as with human experts 

From: Doyle,Dean, et al. 

Strategical Directions in AI 



What AI has done 

§ Spawned subfields 

§ Data mining, virtual agents, logic programming, 

program specification and verification, program 

synthesis, software development environments, 

deductive database systems, … 

§ Programming styles 

§ Functional programming, logical programming, 

object-oriented programming, constraint 

programming, … 

§ Systems 

§ Space shuttle scheduling, autonomous space craft, 

museum tourguide robots, computer chess, medical 

diagnosis systems, google, Microsoft Wizard, … 


Artificial Intelligence: Where are we 


now? 

§ The environment has changed: 

§ Computational power, embedded computing 

devices, 

§ Cheap sensors, multi media, 

§ Computer networks, World Wide Web 

§ Need for “intelligent” computer systems 

§ Information retrieval from the WWW, shopping 

agents, computer games, supply chain control and 

logistics, flood of data, … 

§ Many AI methods are usable 

§ Learning, data mining, probabilistic reasoning, 

description logics, probabilistic state estimation, 

… 


Artificial Intelligence: what lies ahead? 

Integrated intelligent systems 




Characteristics of 

Integrated Intelligent Systems 

§ Complete systems 

§ Extended existence 

§ Interacting with the environment 

§ Naturalistic tasks and everyday activity 

§ “AI completeness” 



The Concept of an Agent 

Working definition: An agent is anything that can be 

viewed as perceiving its environment through its 

sensors and acting upon it through its effectors. 

percepts 

Agent Environment 

actions 



Is a Thermostat an Agent? 

•... Yes, it perceives and acts! 

measured temperature 

open valve 

close valve 

no-op 

•… but not a very interesting one 


Agents: The AGILO RoboCuppers at 


percepts 

Intelligence = 

actions 

Play 

§Performing appropriate goal-directed action 

§Given uncertain and incomplete information 

§Using limited computational resources 


Agents: The AGILO RoboCuppers at 


Play 



Rational Agents 

An ideal rational agent performs actions that are expected to 

maximize its performance measure based on the evidence 

provided by the percept sequence and the built-in knowledge 

percepts 

Agent Environment 

actions 

State 3 

State 2 

State 1 

State t 


Anwendungsfelder 

§ Sprachverstehende und 

–generierende Systeme 

§ Bildverstehende 

Systeme 

§ Robotik 

§ Assistenzsysteme 


Die KI Landschaft 

Methoden 

§ Problemloesen und Suche 

§ Wissensrepraesentation 

und –verarbeitung 

§ Handlungsplanung 

§ Maschinelles Lernen 

§ Verarbeitung von 

unsicherem Wissen 


§ Einfuehrung 

§ Rationale Agenten 


Inhalt der Vorlesung: Vorlesung: 

§ Zielorientierte Agenten/ 

Problemloesen d. Suche 

methodenorientiert!!! 

methodenorientiert!!! 

§ Heuristische Suche 

§ Constraintbasierte Suche 

§ Schlussfolgernde Agenten 

§ Syntax, Semantik, Kalkuel 

§ Wumpuswelt und ihre 

Axiomatisierung 

§ Situationskalkuel 

§ Planende Agenten 

§ POCL Planung 

§ Planung und Ausfuehrung 

§ Agieren unter 

Unsicherheit 

§ Bayes Netze 

§ Entscheidungsnetze 

§ Markov 

Entscheidungsprozesse 

§ Lernende Agenten 

§ Entscheidungsbaumlernen 

§ Reinforcement Lernen 


Lehrveranstaltungen am Lehrstuhl IX 

Muster- 

erkennung 

Interpr.von 

Bildfolgen 

+ Seminare 

+ Praktika 

+ DAs & SEPs 


Bildver- 

verstehen 

Industr. 

BV 

intelligente 

Autonome 

Roboter 

WIBAS 1 

WIBAS 2 

WBS in 

Industriellen 

Anwendungen 



Anwendungen 

§ Autonome Raumsonden und Rovers 

§ Planungs- und Schedulinganwendungen: 

Space Shuttle scheduling and Golfkrieg- 

Logistik 

§ Spielprogramme: Schach und Backgammon 

§ Computeranimierte Charaktere 

§ Wissensbasierte Systeme (im WWW) 

§ Autonome Roboter: ROBOCUP 

§ Data Mining, Machine Learning 

§ Bayes Nets 


Autonome Raumsonden und Rover 



courtesy JPL

Remote Agent (RA) Architecture 

PS generates plan 

Exec expands & 

executes it 

MIR looks for 

failures. 

Exec can respond 

to fault locally... 

… or request a 

new plan. 


Smart 

Executive 

ESL 

Monitors 

Planner & 

Scheduler 

HSTS / IRS 

Mode ID & 

Reconfiguration 

MIR / Livingstone 

Real-time Execution 

Adaptive Control 

Spacecraft Hardware 

Mission 

Goals 



Rover Agents 



Europa Submersible 

Examples of of Unknowns and Impact on Planning 

–Thickness and composition of of ice-cap 

•energy expended to to penetrate surface 

•data volume and type collected 

•ability to to communicate while below cap 

–Properties of of underground ocean 

•energy and time cost to to move/explore 

•effectiveness of of sensors 


Planungs- Planungs und Schedulinganwendungen 



Beispiele: 

•Schach 

•Backgammon 


Spielprogramme 


“I could feel 

–I could 

smell – a 

new kind of 

intelligence 

across the 

table” 


Computer Schach: Schach: 

Deep Blue 

Saying Deep Blue 

doesn’t really think 

about chess is like 

saying an airplane 

doesn’t really fly 

because it doesn’t 

flap its wings. 

–Drew McDermott 


Backgammon: TD-Backgammon 

TD Backgammon 



Computeranimierte Charaktere 

A Bug’s Life (Pixar/Disney) Toy Story (Pixar/Disney) 


Antz(Dreamworks) 

Tomb Raider 3 (Eidos Interactive) The Legend of Zelda (Nintendo) Final Fantasy VIII (SquareOne) 


Artificial Fish (Terzopoulous 

( Terzopoulous et al.) 



Artificial Fish (Terzopoulous 

( Terzopoulous et al.) 




Das Dressieren von Hunden 



Erwartungen und Emotionen 

§ Emotional Autonomic variable 

§ Surprise (unexpected observation) 

§ Confusion (negated expectation) 

§ Proportional to amount of culled probability 

§ Frustration (consistently negated 

expectations) 

V a riab le V a lu e 

Duncan instructed to approach sheep 

Discovers sheep is not 

in last-observed location 

Confusion Frustration 

Sheep found in 

unexpected location. 

Surprise 

t 

inst 

p −pn 

( 

highest 

pn ( 

Michael Beetz, TU Time München 43 

s 

x = αx + 

βx 

t t−1t inst 

= 

t 

cinst = p =∑ 

culled pn ( ) 

n∈V * 

) 

* 

f = kc 

) 

t t 

inst


Das Dressieren von Hunden 

(1)Movie: Duncan 

(2)Movie: maintaining belief states 



Computer Game Players 

Real-time Strategy Games 

(e.g. Age of Kings (Microsoft Games/Ensemble Studios), … 

Building Competitive AI/Computer Players is challenging 





Wissensrepraesentation 

§ Speech Recognition 

§ “word spotting” feasible today 

§ continuous speech – rapid progress 

§ turns out that “low level” signal not as 

ambiguous as we once thought 

§ Translation / Understanding 

§ very limited progress 

The spirit is willing but the flesh is weak. (English) 

The vodka is good but the meat is rotten. (Russian) 


§ John gave Pete a book. 

§ John gave Pete a hard time. 

§ John gave Pete a black eye. 

§ John gave in. 

§ John gave up. 

§ John’s legs gave out beneath him. 

§ It is 300 miles, give or take 10. 



Syntaktisches, 

Syntaktisches, 

Semantisches, 

Semantisches, 

Analoges 


Wissen 

§ Time flies like an arrow. 

§ Fruit flies like a banana. 

§ Fruit flies like a rock. 


Knowledge 

Base 

Layers 

Arrangement, by Generality 

Upper 

Ontology 

Core 

Theories 

Domain-Specific 

Theories 

Facts 

(Database) 

Upper Ontology: Abstract Concepts 

Core Theories: Space, Time, Causality, … 

Domain-Specific Theories 

Facts: Instances

Knowledge 

Base 

Layers 


EVENT ˝ TEMPORAL-THING ˝ INDIVIDUAL ˝ THING 

Upper 

Ontology 

Core 

Theories 


Theories 

Facts 

(Database) 

Upper Ontology: Abstract Concepts

Knowledge 

Base 

Layers 



Upper 

Ontology 

Core 

Theories 


Theories 

Facts 

(Database) 



For all events a and b, a causes b 

implies a precedes b

Knowledge 

Base 

Layers 



Upper 

Ontology 

Core 

Theories 


Theories 

Facts 

(Database) 




implies a precedes b 


For any mammal m and any anthrax bacteria a, 

m’s being exposed to a causes m to be infected by 

a.

Knowledge 

Base 

Layers 



Upper 

Ontology 

Core 

Theories 


Theories 

Facts 

(Database) 




implies a precedes b 


For any mammal m and any anthrax bacteria a, 

m’s being exposed to a causes m to be infected 

by a. 

Facts: Instances 

John is a person infected by 

anthrax.


Was das Web sein sollte! sollte 

§ "This is a pity, as in fact documents on the web describe real 

objects and imaginary concepts, and give particular relationships 

between them... For example, a document might describe a person. 

The title document to a house describes a house and also the 

ownership relation with a person. ... This means that machines, as 

well as people operating on the web of information, can do real 

things. For example, a program could search for a house and 

negotiate transfer of ownership of the house to a new owner. The 

land registry guarantees that the title actually represents reality.” 

§ Tim Berners-Lee plenary presentation at WWW Geneva, 1994 


Can’t we just use XML? 

This is what a web-page in natural language 

looks like for a machine

education> 

< work> 

< private> 

XML helps 

XML allows “meaningful tags” to be added to 

parts of the text 

< CV> 

< name>

< education> 

XML „ machine accessible meaning 

But to your machine, 

the tags look like this…. 

< work> 

< private> 

< CV > 

< name >

< education > 

< work> 

< private > 

Schemas take a step in the right direction 

Schemas help…. 

< CV > 

< name > 

 

< education > 

< work> 

< private > 

< CV > 

< name > 

< CV > …by relating 

private 

common terms 

between documents


Web Semantics 

Semantic WebLayerCake (Berners-Lee, 99;Swartz-Hendler, 2001) 



Autonome Roboter 



Research in 

Image Understanding and 

Intelligent Autonomous Systems 

Michael Beetz 

Technische Universitat Munchen 



An Example from the Past 

The Robot Museums Tourguide Robot MINERVA 

•Smithsonian’s National Museum 

of American History 

•>94 hours, 13days 



Project FIPM: 

Football Interaction and Process Model 


§ A research group 


What is FIPM? 

§ Informal, embedded in Lehrstuhl Informatik IX 

§ Goal: integrated intelligent information system 

§ Members: Michael Beetz, Christian Holzer, Donald 

Kossmann, Thomas Stammeier, 

Maximilian Leinweber, ??? 

§ A proposed DFG research project 

§ Headed by: Prof. Radig, Prof. Beetz, 

Prof. Hartmann 

§ 2 researchers (CS) + 1 researcher (Sport) 

+ 2 student researchers 

§ Software application system 

§ Based on the Cairos sensor system 

§ Basic research in Intelligent Systems and Game Analysis 



The FIPM Vision 

§ We will develop an integrated intelligent 

software system that 

§ serves as a testbed for our basic research in 

§ Perception of intentional activity 

§ (probabilistic) sensor data interpretation 

§ Intelligent autonomous systems 

§ Automatic game analysis and assessment 

§ Databases 

§ … 

§ will be publicly demonstrated and evaluated 

§ eg at the World Cup 2006 


FIPM: computational problem 

§ Computational problem: 

§ Input: positional data 

§ Output: game analysis, game summary 



Competent game analysis – an example 

Beim Angriff ueberden rechten Fluegelhat der Spieler X einen 

Fehlpass gespielt, derallerdings zu Lasten des StuermersY geht, da 

dieser sich falsch freigelaufen hat. Durchden Fehlpass konnte die 

Mannschaft B einen schnellen Gegenangriff einleiten. 

§ Recognizing episodes (discretization) 

§ “Spielfaehigkeit” 

§ Interactions and situation context 

§ Concepts and key indices 

§ Expressive game representation 

§ Activity recognition, interpretation, and 

assessment 




Why Soccer is so difficult 

§ Control over ball and passing is technically 

difficult 

§ Game without ball 

§ Large field 

§ Many action options 

§ High failure rates 

§ … 



A Software Workbench for 

Motion Segmentation and Abstraction 



Football Home Cinema 


Assistant Coach’s Workbench 



Retrieving Information from the Web 

BL 

wrapper 

FCK 


wrapper 

Kicker 

wrapper 

DAML+OIL/XML 

Data base 



Project TUMBOT 

including Campus Guide 

Connected to the WWW 

using software agents 



Mapping in Orleanstrasse 


Object-oriented Object oriented Environment Maps: 

Regions & Gateways 

•Metric Map of 2D line segments 

•Feature vector (recognition/localization) 

-bounding rectangle 

-moments of (laser) point cloud 

-main axes, free space ..... 

•Rectangles in 2D & 3D, cubes 

•Measureof accuracy/completeness 

•Connected gateways 


•Gateway points ( ) 

•Crossing points (x) 

& traversal directions 

•Left/Right turn, X/Tjunction, 

narrow passage, 

open/close transition 

•Adjacent regions 


Vision-Based 

Vision Based 

3D 

Landmarks 

•rectangles, cubes, lines 



Vision-Based 

Vision Based 

3D 

Landmarks 

•rectangles, cubes, lines 


Door??? 

Table ??? 

Poster ??? 



Project CCD 

The CCD Algorithm 

§DAGM 2002: Best Paper Award 

§RoboCup Engineering Award (2002) 

§CVPR Conference Paper 

§IJCV Article accepted 

§Implementation State 



Project: AGILO 

Semi-automatic Semi automatic Acquisition of 

Cooperative Visuo-motoric 

Visuo motoric Plans 


The AGILO Soccer Robots 

§ Pioneer 1 robots 

§ Off-the-shelf CCD 

cameras 

§ Simple differential drive 

§ No laser range finders 

§ No omni-directional vision 


Challenges: 

1. Restricted camera view 

Ł cooperation 

2. Vibrations,spot light,shadow 

Ł inaccuracies, uncertainty 

• Vibrations of 1-2 pixels may 

yield more than 0.5m 

inaccuracy in depth est. 

3. Opponents move quickly and 

unpredictably 

Ł iterate with high frequency 

4. Simple drive 

Ł difficult steering 


The AGILO RoboCuppers at Play 



Snapshots from the Episode (1) 



Snapshots from the Episode (2) 




The Project Title Revisited 

Semi-automatic Acquisition of 

Visuo-motoric 

Cooperative Plans 

§ Integration of programing and learning 

§ Programable hierarchical robot learning 

§ Robocup without color labeling 

§ Vision based probabilistic state estimation 

§ Robot control programs that can not only be 

executed but also reasoned about and manipulated 


Given: Given 

RoboCup: RoboCup: 

Game State Estimation 

§Fully implemented and empirically evaluated 

§Self localization transferred to Sony dogs 

§AAAI 2002 paper & plenary oral presentation 

§IEEE Journal article 

§etc 


Estimate: Estimate 



Robot Self-localization 

Self localization 

Given: 

Compute: 





Opponent Tracking 



Experience-based Experience based Learning 

not 


learn to play soccer 

but learn to solve critical reasoning tasks 

Robot becomes capable of inferring 

§ what happens if I set the voltage of the left motor to 2 volt? 

§ which control signal should i issue to arrive at (-2.0,1.0) with 

an orientation of 96° and a velocity of 0.6 m/sec ? 

§ how fast can I get the ball? 

§ how promising is it to dribble the ball into the goal? 

§ etc. 

•Full paper at AAMAS 

• Journal article submitted 

• Library for radial basis functions 

Robot controllers can call inference tasks as 

subroutines to select appropriate actions 

• Individual learning tasks at IROS, RoboCup 

Symposium, 

DARS, … 



Example Learning Task: 

the model of the robot´s dynamics 

Learning: 

1. record data: states ζ and commands ξ 

2. neural learning of the mapping ζ,ξ -> ζ 

< 2 hours of data 

acquisition 

< 2 hours of data 

acquisition 

> 330 hours of data 

acquisition 

training data 

10000 experiments 

in complex state space 


Project Camera-equipped Camera equipped Rooms

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