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INFORMATION AND KNOWLEDGE MANAGEMENT IN COMPLEX SYSTEMS. Ksiazka jest do nabycia w GTN, Gdansk. Zamowienia mozna przesylac do Biura Towarzystwa e-mailem [email protected] , faksem 305-81-31. Ksiazka jest wysylana poczta z faktura VAT.

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slide3

Ksiazka jest do nabycia w GTN, Gdansk.

Zamowienia mozna przesylac do

Biura Towarzystwa e-mailem [email protected] , faksem 305-81-31.

Ksiazka jest wysylana poczta z faktura VAT.

Sprzedaz prowadzi tez Ksiegarnia Naukowa przy ul. Lagiewniki 56 w Gdansku

social characteristics of the agricultural industrial and information societies castells 2001 2003
SOCIAL CHARACTERISTICS OF THE AGRICULTURAL, INDUSTRIAL AND INFORMATION SOCIETIES (Castells 2001, 2003)
management of information
MANAGEMENT OF INFORMATION…

Presentation outline:

  • significance and aims
  • methods and techniques
  • results so far
  • implementations
significance and aims
SIGNIFICANCE AND AIMS
  • If all the N elements of a system are required to communicate, the amount of information transfer is likely to become unmanageable.
  • The above has been the reason why systems that are divided into smaller subsystems (called atomized or multi-agent or multi-component systems) are recently gaining considerable attention.
  • In atomized approach efficiency of components depends on quality and quantity of information flow (Jain 2001)
  • Our research is primarily concerned with capture of knowledge useful in structuring and evaluation of such an information flow.
autonomous agents systems
AUTONOMOUS AGENTS/SYSTEMS
  • AUTONOMOUS AGENTS CONSIST OF GROUPS OF PEOPLE, MACHINES, ROBOTS, AND/OR GUIDED VEHICLES TIED BY THE FLOW OF INFORMATION BETWEEN AN AGENT AND ITS EXTERNAL ENVIRONMENT AS WELL AS WITHIN AN AGENT
  • AUTONOMOUS AGENTS CAN STILL BE INTERRELATED AND EMBEDDED IN LARGER SYSTEMS, AS AUTONOMY AND INDEPENDENCE ARE NOT EQUIVALENT CONCEPTS.

(E. Szczerbicki, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, p. 1302)

solving complex problems
SOLVING COMPLEX PROBLEMS

DECOMPOSITION

INTEGRATION

INTEGRATED

SOLUTION

COMPLEX

SYSTEM

REPRESENTATION

solving complex problems1
SOLVING COMPLEX PROBLEMS

COMPLEX

SYSTEM

DECOMPOSITION

REPRESENTATION

INTEGRATION

INTEGRATED

SOLUTION

questions
QUESTIONS…….
  • How to structure an exchange of information between a system and its uncertain, dynamic and imprecise environment?
  • What is better, complete information but heavily delayed, or incomplete information less delayed?
information structure
INFORMATION STRUCTURE

1 0 0 0

0 1 0 0

0 0 1 1

0 0 1 1

X1

X2

X3

X4

soft vs hard modelling
SOFT VS HARD MODELLING

THEORY RICHNESS

SOFT MODELLING

NEURAL NETWORKS

EXPERT SYSTEMS

PHYSICAL MODELLING

DATA RICHNESS

sample of production rules
SAMPLE OF PRODUCTION RULES

RULE 12

IF an external environment of an autonomous agent is static,

AND there is an interaction in the internal environment,

AND the relationship between variables describing the external environment is of statistical character,

THEN information structure should include observation (sensoring) and communication.

RULE 13

IF an external environment of an autonomous agent is static,

AND the relationship between variables describing the external environment is given by function dependence,

THEN communication between agent elements does not affect the value of information structure; information flow should be restricted to observation (sensoring).

solving complex problems2
SOLVING COMPLEX PROBLEMS

COMPLEX

SYSTEM

DECOMPOSITION

REPRESENTATION

INTEGRATION

INTEGRATED

SOLUTION

implementations
IMPLEMENTATIONS….
  • Product design co-ordination
  • Modeling and simulation of information flow for performance enhancement:
    • coal mine
    • steel processing
    • hospital operation
    • maintenance
    • manufacturing
  • Concurrent engineering philosophy
  • Information systems development for environmental issues
support for integration technology
SUPPORT FOR INTEGRATION TECHNOLOGY

EMBODIMENT OF INTEGRATED APPROACH TO INFORMATION PROBLEMS GENERALLY AND INFORMATION SYSTEMS DESIGN SPECIFICALLY

slide34

PROJECTS

  • steel processing
  • manufacturing
  • hospital operation
  • mining
  • preventive maintenance
  • servicing
visual slam awesim
VISUAL SLAM & AWESIM

The computer simulation software package that we use is called Visual SLAM.

“Visual SLAM supports the modeling of systems from diverse points of view through a graphical based interface.

AweSIM is a simulation problem-solving environment for Visual SLAM.

AweSIM provides a database, project maintainer, interactive execution environment, standard textual and graphical reports and concurrent and post process animation facilities.”

solving complex problems3
SOLVING COMPLEX PROBLEMS

COMPLEX

SYSTEM

DECOMPOSITION

REPRESENTATION

INTEGRATION

INTEGRATED

SOLUTION

slide42

SIMULATION…

  • Simulation Modelling can be Applied to the Whole Life Cycle of a Typical Industrial Systems Project.
  • Probably one of the most important advantages of simulation modelling is its adaptability. It can be easily applied concurrently to all project stages as the project evolves. The stages usually involve (i) concept design, (ii) detailed design, (iii) implementation, and (iv) operation. Using simulation models developed concurrently with each stage we can:
    • understand basic system operation at the concept design level,
    • select the best concept to proceed with to the detailed design,
    • test all proposed operating and control procedures,
    • test the impact of all design changes made during the implementation stage,
    • test the impact of all proposed changes during the operation stage,
    • predict necessary changes in the system operation to follow the envisaged changes in the external and internal environments of the system
slide43

SIMULATION…

  • Do not simulate when:
  • the problem can be solved using common sense analysis,
  • the problem can be solved analytically,
  • it\'s easier to change or perform direct experiment on the real system,
  • the cost of the simulation exceeds possible savings,
  • there are not proper resources available for the project,
  • there is not enough time for the model results to be useful,
  • there is no data - not even estimates,
  • the model can not be verified or validated,
  • project expectations can not be met,
  • the system\'s behaviour is too complex or can\'t be defined.
sequential product development process
SEQUENTIAL PRODUCT DEVELOPMENT PROCESS

MARKETING

DESIGN

PRODUCTION

DISTRIBUTION

concurrent engineering ce has been described as ida report 1988
Concurrent Engineering (CE) has been described as(IDA Report 1988):

“a systematic approach to the integrated design of products and their related processes, including manufacture and support. This approach is intended to cause the developers from the outset, to consider all elements of the product life cycle from conception through disposal, including quality, cost, schedule and user requirements.”

slide47

CE BENEFITS: US DEPARTMENT OF DEFENCE

  • Engineering Change Orders reduced by 50% or greater.
  • Product development cycle time reduced 40-60%.
  • Cost to Manufacture reduced 30-40%.
  • Rework and scrap reduced by up to 75%.
slide48

KEY FEATURES OF CE:

  • Cross-Functional Teams (CFT’s)
  • Concurrent Product Realisation Process Activities
  • Incremental Information Sharing and Use
  • Design for “X” (DFX)
  • Integrated Project Management
  • Early and Continual Supplier Involvement
  • Early and Continual Customer Focus
slide50

THE MAIN ELEMENTS OF CE MANAGEMENT

  • COLLABORATION
  • COMMUNICATION
  • COORDINATION
  • CONTROL
  • INFORMATION DEPENDENT SYSTEM
solving complex problems4
SOLVING COMPLEX PROBLEMS

COMPLEX

SYSTEM

DECOMPOSITION

REPRESENTATION

INTEGRATION

INTEGRATED

SOLUTION

gddi planning cycle
GDDI PLANNING CYCLE
  • Plan generation,
  • Plan decomposition,
  • Plan distribution, and
  • Plan integration
blackboard database control structure
BLACKBOARD DATABASE CONTROL STRUCTURE

BLACKBOARD

DATABASE

KNOWLEDGE

SOURCES

PLAN

INTEGRATION

PLAN

DECOMPOSITION

KNOWLEDGE

SOURCES

PLAN

DISTRIBUTION

KNOWLEDGE

SOURCES

PLAN

GENERATION

KSAR

CONTROL

SOURCE

some main challenges for future research
SOME MAIN CHALLENGES FOR FUTURE RESEARCH:
  • TRANSFORMATION OF INFORMATION INTO KNOWLEDGE
  • KNOWLEDGE REPRESENATION

(Davis, Davenport, Prusak, Grudzewski, Hejduk, Probst, Takeuchi, Minsky, Boahene, Ditsa, Mitchell, Gonzalez, Pukszta)

slide57

Knowledge management is an emerging, interdisciplinary business model dealing with all aspects of knowledge within the context of the firm, including knowledge creation, codification, and sharing, and using these activities to promote learning and innovation. It encompasses both technological tools and organizational routines of which there are a number

of components. These include generating new knowledge; acquiring valuable knowledge from outside sources; using this knowledge in decision making; embedding knowledge in processes, products, and/or services; coding information into documents, databases, and software; facilitating knowledge

growth; transferring knowledge to other parts of the organization; and measuring the value of knowledge assets and/or the impact of knowledge management.

slide58

PHASE 1 (FINISHED) – 2005-07, Project supported by grants from AAS and DAAD; University of Newcastle, Fraunhofer Berlin, University of Hannover, RWTH Aachen, Gdansk University of Technology, UC Berkeley

  • Knowledge Management
  • Proposed Platform
  • Five Fundamentals
  • KSCS
slide59

PHASE 2 – 2007-10, Project supported by grants from AAS and NSF; University of Newcastle, UC Berkeley, Technical University of Gdansk

  • KR
  • Proposed KR
  • Set of Experience
  • Converting Set of Experience
  • Set of Experience in XML
slide60
PHASE 3 – 2010-13, Project supported by grants from AAS, DAAD, MARIE CURIE EU Program
  • e-Community development
proposed platform
Proposed Platform…
  • Multi-SourceKnowledge-Experience Management System.
  • Integrated tool of rule-based systems, experts systems, numerical models, and self-learning technology to help in the decision-making process.
  • Interrelated net of similar systems supplying knowledge and sharing experiences and perceptions of their own worlds.

KNOWLEDGE SUPPLY CHAIN SYSTEM

(KSCS)

slide62

U

V

If X>70 then K = good

R

If W<2 then Z = 2

Z = 0.78

If G=blue then B = high

K = average

X = 100

H = good

RtÈRlÈRtl

W = 1.5

G = blue

Y = 210

B = high

V = 8451.54

C

2X+3Y-V <= 3450

Vl

Vt

H>=Excellent

G<>blue AND Y+70X<2500

F

Max P=3X-2Y+RQ

CtÈCl

Max K=Excellent

Min C=YQ AND B=high

FtÈFlÈFtl

Set of Experience Knowledge Structure

The four components are variables, functions, constraints, and rules, and constitute the knowledge structure.

Set of Experience Ei = (Vij, Fi, Ci, Ri)

set of experience knowledge structure

Image credit U.S. Department of Energy Human Genome Program (http://www.ornl.gov/hgmis).

Variables

Rules

Functions

Constraints

Set of Experience Knowledge Structure
  • Surrogate
  • Set of Ontological Commitments
  • Fragmentary Theory of Intelligent Reasoning
  • Medium for Efficient Computation
  • Medium of Human Expression
slide64

?

KNOWLEDGE

INFORMATION

DATA

slide65

WISDOM

KNOWLEDGE

INFORMATION

DATA

slide66

Wisdom versus Knowledge: What\'s the Difference?

"I don\'t know and so, I don\'t do.I now know, but I haven\'t learned how.I\'ve learned how and so, I do and I teach others."

- Rodolfo Neiva de Sousa

slide67

TRUST

KNOWLEDGE

INFORMATION

DATA

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