www.ima-zlw-ifu.rwth-aachen.de

The Internet of Things in Production Technology:

Heterogeneous Agent Systems for Dezentralized Production Paradigms

6. Expertenforum „Agenten im Umfeld von Industrie 4.0“ Munich, Germany, May 07th, 2014

Univ.-Prof. Dr. rer. nat. Sabina Jeschke

IMA/ZLW & IfU Faculty of Mechanical Engineering RWTH Aachen University

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Outline

I. Cyber-Physical Systems alias the Internet of Things The term, its predecessors, its definition and its “relatives”...

… leading to the 4th industrial (r)evolution...

… towards a networked world

II. Heterogeneous Agents - Stabilization by Local Autonomy The rise of agent systems …

… its visions and views, …

… its scientific challenges

III. Towards an Autopoietic Production: 1. Decentralized production planning

2. Virtual Production Intelligence

3. Self-optimizing in socio-technical assembly systems

4. MAS in (Intra-)Logistics

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“When wireless is perfectly applied the whole earth will be converted into a huge brain, which in fact it is, […] and the instruments through which we shall be able to do this will be amazingly simple compared with our present telephone. A man will be able to carry one in his vest pocket.”

The term and its predecessors… Teleautomation…

1926 Nikola Tesla “Teleautomation”

Nikola Tesla, „Teleautomation“, USA

Wardenclyffe Tower (built for broadcast

energy experiments)

remote-controlled unmanned aerial vehicle (unmanned boat) by Tesla, 1898

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Google Trends (October 2012): Cyber-physical systems

“Cyber-physical systems are physical, biological, and engineered systems whose operations are integrated, monitored, and/or controlled by a computational core. Components are networked at every scale. Computing is deeply embedded into every physical component, possibly even into materials. The computational core is an embedded system, usually demands real-time response, and is most often distributed. ”

Helen Gill, NSF, USA

The term and its predecessors… … and other steps towards cyber-physical systems

1926 Nikola Tesla “Teleautomation”

1948 Norbert Wiener “Cybernetics”

1961 Charles Stark Draper “Apollo Guidance Computer” one of the first embedded systems

2006 Helen Gill “Cyber-physical systems”

1999 Kevin Ashton “Internet of Things”

1988 Mark Weiser “Ubiquitous computing”

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… its definition and its “relatives”... Internet of Things & Industry 4.0

Cyber-Physical Systems OR Internet of things ?

• large-scale distributed computing systems of systems

• computation and “intelligence” is not decoupled from environment

• internet as large-scale network • embedded systems

(= intelligent components)

Shared

Vision

Core Technology

• Internet of Things driven from computer sciences, Internet technologies driven by EC

• Cyber-Physical System driven from engineering aspects driven by the NSF

• Internet of Things focusing on openness and on the network - virtuality

• Cyber-Physical System focusing on the physical process behind, often a closed-loop system

Distinct

Scientific Community

Philosophy, focus

• Today: more or less synonym • Industry 4.0 as a special field of application For all practical purposes:

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… leading to the 4th industrial (r)evolution... The Drivers….

Power revolution Centralized electric power infrastructure; mass production by division of labor

1st industrial revolution Mechanical production systematically using the power of water and steam

today

Digital revolution Digital computing and communication technology, enhancing systems’ intelligence

Information revolution Everybody and everything is networked – networked information as a “huge brain”

Communication technology bandwidth and computational power

Embedded systems miniaturization

Semantic technologies information integration

around 1750 around 1900 around 1970

Towards intelligent and (partly-) autonomous systems AND systems of systems

Watson 2011

Google Car 2012

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… leading to the 4th industrial (r)evolution... Towards complex and networked social-technical systems

… let’s have a look Communication Consumer Energy Infrastructure Health Care Manufacturing Military Robotics Transportation

[CAR2CAR, 2011] and [ConnectSafe, 2011]

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… towards a networked world Not restricted to industry: cyber physical systems in all areas

Back to: The earth converted into a huge “brain”… (Tesla 1926) Integrating complex information from multiple heterogenous sources opens multiple possibilities of optimization: e.g. energy consumption, security services, rescue services as well as increasing the quality of life

… and more

Building automation

Smart grid

Room automation

Smart environment

Smart metering

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IOT CPS

… towards a networked world Two worlds coming together

Physical world

Internet Manufacturing process Material behavior Service-oriented

Things Semantics

Unique Identifier

Embedded Systems

Simulation Automation

Cyber-physical Digital world

“Closed” System controllable and partly predictable by simulation

“Open” System difficult to control or to predict system behavior

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IOT CPS

… towards a networked world Two worlds coming together

“Closed” System controllable and partly predictable by simulation

“Open” System difficult to control or to predict system behavior

! Timed communication and information exchange

! Well-known and controlled interaction between participants

! Time delayed communication

! Interaction between unknown participants

! “Dynamic” (continuously changing) ! “Static”

(changes are controlled)

Beyond traditional technical systems: Systems of distributed intelligence

VS

Change of perspective: from system to individual, from top-down to bottom-up

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Outline

I. Cyber-Physical Systems alias the Internet of Things The term, its predecessors, its definition and its “relatives”...

… leading to the 4th industrial (r)evolution...

… towards a networked world

II. Heterogeneous Agents - Stabilization by Local Autonomy The rise of Agent Systems …

… its visions and views, …

… its scientific challenges

III. Towards an Autopoietic Production: 1. Decentralized production planning

2. Virtual Production Intelligence

3. Self-optimizing in socio-technical assembly systems

4. MAS in (Intra-)Logistics

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The rise of agent systems … Its origins

B.F. Skinner (1953)

John B. Watson (1928)

Hadley Cantril (1940)

Charles Darwin (1838)

Peter Corning (1971)

Psychological and philosophical foundations

Classical behaviorism S-R model

Functionalism S-O-R model

Refle

xive

Co

nditi

oned

Classical conditioning US + NS = CS CR

Operant conditioning S-O-R-K-C model

Iwan Pawlow (1926)

H. Putnam (1960)

Social and biological foundation

Syst

em

theo

ry

com

mun

i-ca

tion

evol

utio

n

©The Daily Telegraph

med

ia

Shannon/Weaver (1949)

Inspired by social systems and biological models …

The idea of agent-based modeling was developed as a relatively simple concept in the late 1940s. Since it requires computation-intensive procedures, it did not become widespread until the 1990s.

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Division of labor

Macro-scale Automation

Micro-scale Multi-Core Ha

rdw

are

Soft

war

e

Service oriented

Agent- based

The rise of agent systems … Beyond traditional technical systems: organic computing

Specializations

Learning and reasoning

Adaptive behavior

Motivation

Multimodal communication modes

Touc

h

Faci

al

Emot

ion

Affective Computing ©Michael S. Ryoo, CalTech

©Fraunhofer IOSB

Spee

ch

©sciencedirect.com

Following social systems and biological models … ! Large heterogeneous systems = societies

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Evolution

The rise of agent systems … Beyond traditional technical systems: organic computing

… inspired by social systems and biological models …

SOFTWARE - Algorithms HARDWARE - Robotics

Genetic Algorithms

Neural networks [Gar3t, 2010] and [Robbins, 2010]

Awareness

Self-replication [Bongard, 2006; Lipson, 2007]

Zykov V., Mytilinaios E., Adams B., Lipson H. (2005) "Self-reproducing machines", Nature Vol. 435 No. 7038, pp. 163-164 Bongard J., et al., Resilient Machines Through Continuous Self-Modeling, Science 314, 2006 Lipson H. (2005) "Evolutionary Design and Evolutionary Robotics", Biomimetics, CRC Press (Bar Cohen, Ed.) pp. 129-155

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… its visions and views … Conservative system design

[Sztipanovits, Vanderbilt, 1997]

Use of domain-specific modeling languages

Models are computer-readable

Check of correctness against pre-specified design rules

! Requires deep understanding of system‘s behavior

! Modeling of complex systems often: time consuming, difficult

Top-Down: Model-Integrated Computing

Janos Sztipanovits E. Bronson Ingram Chair in Engineering Professor of Electrical and Computer Engineering Director of the Institute for Software Integrated Systems

Vanderbilt University

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… its visions and views An agent based approach of system design

[Lin, 2010 (on CPS)]

Bottom-up: Agent-based modeling

Dynamically interacting rule-based agents

Model using autonomous agents

Open Agent Based Modeling Consortium (www.openabm.org) Swarm Development Group (www.swarm.org) More information www.agent-based-models.com

! Established method to simulate large distributed systems

! Good at predicting appearance of complex phenomena

! Good at modeling of dynamically changing participants

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… its visions and views Visions towards multi agent systems

A key air-traffic control system at the main airport of Ruritania suddenly fails, leaving flights in the vicinity of the airport with no air-traffic control support. Fortunately, autonomous air-traffic control systems in nearby air-ports recognize the failure of their peer, and cooperate to track and deal with all affected flights. [Wooldridge 199%2009]

… ideas how things might be …

“By mid-21st century, a team of fully autonomous humanoid robot soccer players shall win the soccer game, comply with the official rule of the FIFA, against the winner of the most recent World Cup. ….” [Kitano/Assada 2000]

Interplanetary space flights …: “[The control system] must be capable of reacting autonomously … Goals may be implanted in the [system] prior to flight, but rigid seeking of those goals may result in total mission failure; those implanted goals may have to be expanded, contracted, or superseded in the light of unanticipated circumstances.” [Bond 1978]

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MAS: integrating a variety of heterogeneous fields

… its visions and views MAS: “distributed diversity” in all perspectives

Self-interested computation

Ubiquitous Computing

Autonomic computing

The semantic Web

Distributed Artificial Intelligence

Cyber-Physical Systems

Internet of Things

Concurrent systems

Game Theory

“Diversity”: Heterogeneous Agents for complex systems

John Ashbys Law of “requisite variety”: complex system demand for bottom-up-design with autonomous units

Bottom-up design hopes for an emergent interplay among its autonomous units

Heterogenous agents: interdisciplinarity promotes emergent potential by the ability to be different

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… its scientific challenges The agent in its society

How to build agents that are capable of autonomous action in order to successfully carry out the tasks that we delegate to them?

microscopic agent design

Proactiveness Generation of goal-directed behavior and attempts to achieve goals.

Agent communication Ability to communicate agent plans and world information and to provide coordination services.

Coordination Dynamic election of coordinator/ mediator to assign tasks/ moderate negotiation processes

MAS Group

Mediator

Human Machine Interaction

Transparency Transparent decision making, predictability (“traceability”) , creating confidence.

Cooperation Task decomposition and goal-directed interaction as a team to achieve a shared goal.

Service Provider Awareness of abilities which are provided to a central planning or coordinating unit.

Negotiation Reaching agreements on matters of common interest. Involves offer and counter-offer.

Intentional systems Behaviors show the intention of agents for a smooth human machine cooperation.

Matching of local optimization goals of agent and global optimization goals. Altruistic vs. egoistic behavior.

macroscopic society design

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Outline

I. Cyber-Physical Systems alias the Internet of Things The term, its predecessors, its definition and its “relatives”...

… leading to the 4th industrial (r)evolution...

… towards a networked world

II. Heterogeneous Agents - Stabilization by Local Autonomy The rise of Agent Systems …

… its Visions and Views, …

… its scientific challenges

III. Towards an Autopoietic Production: 1. Decentralized production planning

2. Virtual Production Intelligence

3. Self-optimizing in socio-technical assembly systems

4. MAS in (Intra-)Logistics

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How it all began...: the early ones Electronically coupled vehicle convoys

The KONVOI project (RWTH & partners)

automated / partly autonomous transportation e.g. by electronically coupling trucks to convoys

several successful tests have been conducted with trucks (KONVOI, SARTRE (EU), Energy-ITS (Japan))

expected improvements: reduction of fuel consumption; improved vehicle occupancy; gained road space; optimization of traffic flow; relief for professional drivers; increase in safety

!

advanced driver assistance system for trucks

short distances between vehicles of approx. 10m at a velocity of 80 km/h

Energy-ITS: 4m ! (2013)

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And how it went on…: Integrative Production Technology for High-Wage Countries

DFG Excellence Cluster (and DFG GradKolleg “RampUp”) IMA/ZLW & IfU in 1. Decentralized production planning 2. Self-optimizing socio-technical

assembly systems 3. Virtual Production Intelligence 4. MAS in (Intra-)Logistics

Speaker: Christian Brecher

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Decentralized production planning 1) Agent models for decentralized production planning…

Organization forms on demand – individualized by client - initialized by product

transport unit

production unit

virtual service provider

! Plans production steps and transportation steps Requests service from agents Negotiates with other products for agent-resources

Product agitates as super-agent Autopoiesis model & embodiment theory

fabr

icat

ion

outs

ide

wor

ld

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Decentralized production planning 1) Leading to “products acting as a super-agent”

transport unit

production unit

virtual service provider

production unit

provides services realizes product

features adds parts

communicates state

transportation unit

• specialized production unit

• often realized as mobile robot

virtual service provider

Provides Central Services knowledge base booking/accounting logging & monitoring re-scheduling

product

knows own goal configuration: desired parts desired

features requests

services from production units

negotiates with other products for resources

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Decentralized production planning 1) Everything is an agent, but: Who is in charge?

No central decision-making entity: Product manages its own creation

functionality emerges by interaction of independently agitating subsystems

Rivaling entities: Only local perspective, resource conflicts must be resolved by negotiation

Errors and perturbations can be handled locally

Central Control: Hierarchical agents

Central broker manages sub agents, coordinates interactions, plans main work flows

Omniscient entity: No resource conflicts, globally optimal solutions

Central control is error prone: Failure affects whole production, all changes in subsystems must be dealt with

centralization decentralization

transparency, optimality flexibility, fault tolerance

mar

kedn

ess

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Decentralized production planning 1) Flexible production chaining - “Factories of the Future”

Network of production entities forming an ad-hoc just-in-time process synthesis

flexible production stations

Symmetrical approach: everything/body is an agent!

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Application-layer

Simulation A Simulation C Simulation B

Integration-layer

Integration- server

Database- server

Service- provider

Middleware

Service- provider

Integration Bus

Analysis-layer Presentation-layer

Data-integration

Data-exploration • Data-similarity • User-centered Pattern-recognition

Data Mining • Classification • Clustering

Data-extraction Data-exploration • Interaction • User adaptive

Dashboard • Cockpit / Scoreboard • Solution analysis • Failure analysis

Visualization • CAVE • Immersion

Visualization

Data-analysis

Process-supervision

Coupling

Analysis

Cybernetics in production technology 2) Virtual Production Intelligence (VPI)

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Grand Unified Theory (GUT) for CPS

Cybernetics in production technology 2) Facing heterogeneity: towards a universal language

“GUT… is about developing a modeling language and conceptual framework into which heterogeneous modeling languages and frameworks can be translated.” [www.icyphy.org, 2013]

Global Ontology

Concrete data of arbitarily domain

Semantic annotation

Translation

A “top-down approach”

! Introduction of Semantic Web standards by Tim-Berners Lee, about 2005

! W3C (World Wide Web Consortium) standards provide web-scale semantic data exchange and federation

First steps towards “semantic interoperability”

www.icyphy.org

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Cybernetics in production technology 2) Facing heterogeneity: … or the universal translator

A “bottom-up approach” “Computational linguistics is an interdisciplinary field dealing with the statistical or rule-based modeling of natural language from a computational perspective.” [Wikipedia, 2013]

Computational Linguistics (CL) – NPL (Natural Language Processing)

! Today: CL as a subfield of AI

! Until shortly: interesting results but mostly restricted to specific subfields and restricted scenarios

Development of CL /NLP

! Lately: several “breakthroughs” in more comprehensive application areas

http://withfriendship.com

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Cybernetics in production technology 2) Facing heterogeneity: … or something-in-between

Abstract domain semantics for translation

Domain Ontology

Concrete data (product design)

Semantic annotation

Concrete data (resource planning) Transformation

Interface Ontology

Translation

Domain Ontology

“Abstract semantics … is about developing interfaces between heterogeneous modeling languages that are sufficient for interoperation…” [www.icyphy.org, 2013]

Semantic annotation

Our approach: The “something-in-between approach”

! Besides others: S. Jeschke, D. Schilberg, T. Meisen, RWTH Aachen

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human safety Objects are scanned by various sensors and fused into 3D point-cloud

Socio-technical assembly systems 3) Human machine interaction and cooperative robotics

Robots are no longer locked in work-cells but cooperate with eachother and/or with humans

past scenario: fenced working cell

hybrid planning integrates several robots and/or human and robot in assembly task („assembly by disassembly“), splitted into „online-offline“ for real-time capabilities

hybrid teams Elected leader starts assembly and delegates jobs

HM communication Human is informed about assembly job by virtual reality

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Socio-technical assembly systems 3) Human machine interaction and cooperative robotics

Robots are no longer locked in work-cells but cooperate with each other and/or with humans

hybrid teams Elected leader starts assembly and delegates jobs

Human-Machine Interaction in the X-Cell

Machine-Machine Cooperation

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Socio-technical assembly systems 3) Human machine interaction and cooperative robotics

Robots are no longer locked in work-cells but cooperate with each other and/or with humans

Direct interaction – object transfer

Working against visual occlusions of the object resulting from the robot body / the hands of the human Evaluating possible grasping points Offering good grasping options for the human co-worker

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MAS in (Intra-)Logistics 4) Adaptive logistics: autonomous transportation vehicles

!

Competencies: localization & navigation computer vision adaptive planning multi agent strategies sensory & hardware

Mission: Team of mobile robots autonomously plans and realizes intralogistic tasks within a dynamic production scenario

Mobile transportation robots enable flexible routing “A new trend in automation is to deploy so-called cyber-physical systems (CPS) which combine computation with physical processes. The novel RoboCup Logistics League Sponsored by Festo (LLSF) aims at a such CPS logistic scenarios in an automation setting. A team of robots has to produce products from a number of semi-finished products …” [Niemueller et.al. 2013]

Mixed teams (mech./electrical engineering and informatics) of Master and PhD students

Goal: „Logistics League“ – Industry scenario of cooperative robotics

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MAS in (Intra-)Logistics 4) Adaptive logistics: The Carologistics agent

Deliberative-layer: Rule-based expert-system

!

Incremental task-level reasoning approach

Deterministic and thus transparent behavior

Reproducible by replay of recorded facts

Reactive-layer: Lua based behavior engine (roslua)

! Simple and known language Deterministic and thus

transparent behavior

Low level: Fawkes and ROS

CLIPS Agent Deliberative layer

(defrule s0-t23-s1 (state IDLE) (holding S0) ?g <- (goto (machines $?ms& ̃:(contains$ ?n ?ms )) (min-prio ?mp&:(<= ?mp (m-prio ?mt)))) => (modify ?g (machines (merge ?mp (m-prio ?mt) ?ms ?n)) (min-prio (m-prio ?mt))) )

Behavior Engine Reactive Behaviors

Components

A classical three-layered system architecture based on open source technology enables fast growing systems by crowd development

AI: Practical reasoning approach and role coordination among agents

Robotics: Model-building and use of established algorithms / SW libs

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MAS in (Intra-)Logistics 4) Next steps: Rio … we are coming!

Competitions

2012: 0 points in World Cup 2013: 4th in World Cup 2014: Winner of the GermanOpen

Critical factors for success: Totally decentralized No „brute force components“ Strong cooperation Re-planning during tasks

! Next Step - July 2014:

RoboCup - international Competition: Joáo Pessoa – Brasil 2014

! http://www.carologistics.org/

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Thank you!

Univ.-Prof. Dr. rer. nat. Sabina Jeschke Head of Institute Cluster IMA/ZLW & IfU phone: +49 241-80-91110 sabina.jeschke@ima-zlw-ifu.rwth-aachen.de

Co-authored by: Dipl.-Wirt.-Ing. Sebastian Reuter Institute Cluster IMA/ZLW & IfU phone: +49 241 / 80 91143 sebastian.reuter@ima-zlw-ifu.rwth-aachen.de Dr.-Ing. Tobias Meisen Institute Cluster IMA/ZLW & IfU phone: +49 241 / 80 91139 tobias.meisen@ima-zlw-ifu.rwth-aachen.de

www.ima-zlw-ifu.rwth-aachen.de

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1968 Born in Kungälv/Schweden

1991 Birth of Son Björn-Marcel 1991 – 1997 Studies of Physics, Mathematics, Computer Sciences, TU Berlin 1994 NASA Ames Research Center, Moffett Field, CA/USA 10/1994 Fellowship „Studienstiftung des Deutschen Volkes“ 1997 Diploma Physics

1997 – 2000 Research Fellow , TU Berlin, Institute for Mathematics 2000 – 2001 Lecturer, Georgia Institute of Technology, GA/USA 2001 – 2004 Project leadership, TU Berlin, Institute for Mathematics 04/2004 Ph.D. (Dr. rer. nat.), TU Berlin, in the field of Computer Sciences from 2004 Set-up and leadership of the Multimedia-Center at the TU Berlin

2005 – 2007 Juniorprofessor „New Media in Mathematics & Sciences“ & Director of the Media-center MuLF, TU Berlin

2007 – 2009 Univ.-Professor, Institute for IT Service Technologies (IITS) & Director of the Computer Center (RUS), Department of Electrical Engineering, University of Stuttgart

since 06/2009 Univ.-Professor, Institute for Information Management in Mechanical Engineering (IMA) & Center for Learning and Knowledge Management (ZLW) & Institute for Management Cybernetics (IfU), RWTH Aachen University

since 10/2011 Vice dean of the department of Mechanical Engineering, RWTH Aachen University

since 03/2012 Chairwoman VDI Aachen

Prof. Dr. rer. nat. Sabina Jeschke

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Literature [Shannon/Weaver 1949] – Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. Urbana, Illinois: University of Illinois Press [Bond 1978] - Bond, A.: Project Daedalus: Final Report of the BIS Starship Study; Journal of the British Interplanetary Society (JBIS)Interstellar Studies; 1978. [Graves 1999] - Stephen C. Graves: Manufacturing Planning and Control; Massachusetts Institute of Technology (1999) [Mintzberg 1993] – Mintzberg, H.: Structure in fives: designing effective organizations; Prentice-Hall, 1983 - 312 Seiten [van Aart, 2000] – van Aart, C.: Organizational Principles for Multi-Agent Architectures; Birkhäuser Verlag, 2000 [Kitano,Assada 2000] – H. Kitano and M. Asada, “The RoboCup humanoid challenge as the millennium challenge for advanced robotics,” Adv. Robot., vol. 13, no. 8, pp. 723-737, 2000 [Wooldridge 2009] –M. J. Wooldridge: An Introduction to Multi Agent Systems (2. ed.). Wiley 2009 [Russel/Norvig 1995] – Russel, S, Norvig, P. : Artificial Intelligence: A modern approach; Prentice Hall (1995) [Weber 1994] – Weber, H.: Die Evolution von Produktionsparadigmen: Craft Production, Mass Production, Lean Production; Discussion papers at Universität Kaiserslautern - Fachgebiet Soziologie; FG Soziologie, Univ., 1994 [Maturana 1980] Maturana, H.R.: Autopoiesis and cognition: The realization of the living. No. 42. Springer, 1980. p. 80 [Maturana 1987] Maturana, H.R. : Everything is said by an observer; Gaia, a Way of Knowing, edited by W. Thompson, Lindisfarne Press, 1987, p. 71 [Probst 1987] Probst, G.J.B.: Selbstorganisation - Ordnungsprozesse in sozialen Systemen aus ganzheitlicher Sicht; Verlag Paul Parey, 1987 [Ewert, 2014] Ewert, D.: Adaptive Ablaufplanung für die Fertigung in der Factory of the Future; VDI Verlag GmbH, 2014 [Hartmann, 2002] Hartmann, E.: Arbeitssysteme und Arbeitsprozesse; Habilitation at RWTH Aachen; 2002

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Cybernetics in production technology Gaining Trust: “Zoom-in” by explorative information analysis

Virtual Production Intelligence Platform

summarizing view and information-distribution

Simulation Metal coating

Simulation Cutting the foils

Simulation winding

Simulation impregnation

factory layer

machine layer

material layer

Analysis

Assistance system for planning and decision support by integrative and explorative analysis

Integrated Views

! comprehensive consideration of “material, machine and factory”

Casual relationships

! online consolidation of “real-data” to gain knowledge about casual connections

Exploration

! user adaptive visualization for explorative data analysis

Vertical coupling of production layers

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The CloudLogistic Project (RWTH & partners)

› Using geographical information of origin and destination combined with a time-based approach to calculate needed distances

› Using heuristical approaches (e. g. genetic solvers) to handle the implied Constraint Satisfaction Problems (CSP)

› Using data mining techniques to improve the networks structure in a cybernetic way

…

› Innovative line-based logistics concept: combining Less Than Truckload (LTL) shipments of cooperating Logistic Service Provider (LSP)

› Pickups along the baseline realize direct transportation with no need for a centralized HUB-structure

Examples from the institute cluster … e.g., by increasing the efficiency of SME freight cooperation, …

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… its definition and its “relatives”... Cyber-physical systems

Cyber-physical systems OR Internet of things ? “The frontier between CPS and Internet-of-Things has not been clearly identified since both concepts have been driven in parallel from two independent communities, although they have always been closely related.” [Koubâa, 2009]

A quick glance into the web: “Cyber Physical System is the US version

of the ’internet of things’”

“The term ‘Internet of Things’, originally aiming at RFID technologies, is smoothly becoming synonymous for cyber-physical systems.”

Vision: Internet of Things, Data and Services e.g. Smart City

Vision: Cyber physical systems e.g. intelligent networked road junction

“Internet of Things & Services, M2M or cyber physical systems are much more than just buzzwords for the outlook of connecting 50 billions devices by 2015.”

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… leading to the 4th industrial (r)evolution... Industry 4.0 - Everybody and everything is networked

today

Information revolution Everybody and everything is networked – networked information as a “huge brain”

1st industrial revolution Mechanical production systematically using the power of water and steam

around 1750

Power revolution Centralized electric power infrastructure; mass production by division of labor

around 1900

Digital revolution Digital computing and communication technology, enhancing systems’ intelligence

around 1970

„local“ to „global“

„local“ to „global“

Weidmüller, Vission 2020 - Industrial Revolution 4.0 Intelligently networked, self-controlling manufacturing systems)

“The first three industrial revolutions came about as a result of mechanisation, electricity and IT. The introduction of the Internet of Things is ushering in a fourth industrial revolution. … Industry 4.0 will address and solve some of the challenges facing the world today such as resource and energy efficiency, urban production and demographic change. ” Henning Kagermann et.al., acatech, 2013

Vision of Wireless Next Generation System (WiNGS) Lab at the University of Texas at San Antonio, Dr. Kelley

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The crux of the matter Big Data induce “Intelligence”: From Big Data to Smart Data…

Acquistion/ Recording

Extraction/ Cleaning/

Annotation

Integration/ Aggregation/

Representation

Analysis/ Modeling Interpretation

The Big Data analysis pipeline…

! … transfers big data (many…) into smart data (meaningful data)

+

! … accumulates intelligence from information fragments

! … is a pipeline of aggregating (artificial) intelligence.

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DS

The way so far and beyond Two Worlds coming together

Distributed Systems

Big data - Volume

Distributed storage

Distributed Artificial Intelligence

DISTRIBUTED

Distributed computing

Distributed sources

Velocity

AI SMART

Artificial Intelligence

Smart data

Variety

Social media data

Natural language analysis

Real-time capability

Autonomy

Prediction

Veracity

46

07.05.2014

S. Jeschke

Challenge ‘Scale’ Data-parallel models - Fynn classification of distributed systems

Single Instruction Multiple Instruction

Single Data

Multiple Data

SISD

SIMD

MISD

MIMD

Do all CPUs have their one storage, or are they sharing it?

Do the CPUs apply the same instruction on all data oder different ones?

„regular“ desktop computer (one single CPU; not a distributed system!)

This is all about parallelization and distributed computation…

47

07.05.2014

S. Jeschke

Challenge ‘Scale’ MapReduce - first major data-processing paradigm

Doc 1 word1 word2 word2 word3

Doc 2 word2 word2 word1 word3

Input

Parallel handling of several documents

(word1, 2)

(word2, 4)

(word3, 2)

Reduce

Reduce

Reduce

Reduce Result

Summarizing all findings (by words)

Map

Map

(word1, 1) (word2, 1) (word2, 1) (word3, 1)

(word2, 1) (word2, 1) (word1, 1) (word3, 1)

Map

Word recognition

(word1, (1,1))

(word2, (1,1,1,1))

(word3, (1,1))

Summarize, Group, Share

Re-arrangement of results by words (before: by documents)

Result: word frequency of different words in the collection of documents

Example Extreme high degree of parallelization possible