Introduction
Lecture
Plan
Summary
Lectures,
Units and Topics
Teachers
Location
Organising
Committee
Participant
Details
Bulletin
Board
Photos
Final
Report
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MONET Summer School  May 1519 2000, Bertinoro, ItalyThe MONET
summer school on ModelBased Systems and Qualitative Reasoning (MBS/QR)
was held at the University Residential Centre of Bertinoro (Italy) on
May 1519, 2000 (Monday 09:00 until Friday 17:00).


The
Summer School took place in the University
Residential Centre of Bertinoro. The Centre, built inside the
age old Stronghold and its fortified walls, has been operating
since June 1994 as the seat of University training courses, of
national and international meetings and congresses as well as
qualifying cultural events.
Further
details of location

The goals of the Summer School were:
 To inspire and prepare the next generation of researchers in the
field of MBS/QR.
 To convey to young industrialists the potential of MBS/QR
technology for solving problems resulting from interacting with
complex systems and their behaviour.
 To communicate the type of problems that can be solved by applying
MBS/QR technology.
 To present a coherent and comprehensive overview of the main
results and achievements of MBS/QR research.
 To layout the state of the art of MBS/QR technology, highlight
problems and open issues, and point out the research opportunities for
the near future.
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Lecture Plan (Subject to minor changes)

Monday 15 
Tuesday 16

Wednesday 17

Thursday 18

Friday 19

Morning 
Welcome 





A1 
A3 
A4 
A5 
B5 





B6
(unit 1) 






Afternoon 
A2 
B1 
Soc. 
B3 
B6
(unit 2) 


B2 

B4 
B7 





Closing 
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Lectures, Units and Topics (Subject to minor
changes)Three hour
lectures A three hour lecture consisted of 3 units.
Each unit lasts one hour, including a 15 min break between units.
Lecture A1: Introduction to Qualitative Modelling and
Simulation (Teacher: Ken Forbus )
 unit A1.1: motivation
 problems, principles, ideas and theories/approaches
 what is the role of simulation (prediction/postdiction)
 QR/MBS history
 unit A1.2: qualitative model(ling)(s)
 ontologies for simulation models
(components, processes, constraints, functional models, etc.)
 quantities, qualitative values and quantity spaces
 dependencies
(influences, proportionalities, inequalities, etc).
 basic concept of qualitative calculus
(incl. the notion of ambiguity)
 unit A1.3: model construction and simulation
 model fragments
 qualitative states, state transition and simulation
 properties: completeness, soundness
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Lecture A2: Advanced Topics in MBS/QR (Teacher:
Unit A2.1 & A2.3 Ken
Forbus and Unit A2.2 Peter
Struss)
 unit A2.1: causality
 causality (what is meant by it?)
 causality as a basis for prediction and explanation
 techniques for causal ordering
 unit A2.2: advanced qualitative calculus
 qualitative calculus
 order of magnitude reasoning
 interval reasoning
 unit A2.3: modelling paradigms
 compositional modelling
 abstractions (time scale, parameter)
 points of view (behavioral, functional, etc.)
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Lecture A3: ModelBased Diagnosis (Teacher: Luca Console)
 unit A3.1: introduction and motivation
 overview of diagnostic approaches / problems
(rulebased, functional, causal models etc., should lead to
the conclusion: MBD is a specific type of diagnosis)
 why modelbased ?
(what are the special features, why is it better)
 componentconnection model
 consistencybased diagnosis
(basic steps including: detecting discrepancies, conflict
generation, candidate generation, and probing)
 unit A3.2: multiple and large scale models
 fault models
(including elaboration on: consistency versus abductive
diagnosis)
 multiple (fault) models, structural models, hierarchical models
dealing with large scale models
 unit A3.3: examples, nontechnical domains and remaining
problems
 applications and examples
 MBD in nonengineering/technical domains
 remaining problems and future research goals
(e.g. bridgefaults, time varying faults)
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Lecture A4: Qualitative Reasoning and Educational Systems
(Teacher: Bert Bredeweg)
 unit A4.1: introduction and motivation
 overview on research on cognition and learning (brief)
(highlight notions such as: novice/expert distinction, common
sense reasoning, analogical reasoning)
 why use simulation models?
(what are the benefits, how are they used, methods of
teaching)
 relevance of articulate models (in ILE)
 unit A4.2: teaching functions in Interactive Learning
Environments (ILE)
 diagnosing learner behaviour
 generating explanations
 visualisation of simulation models/results
 model progression
 unit A4.3: examples and remaining problems
 CSCL and other WWW related issues
 applications and examples
 remaining problems and future research goals
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Lecture A5: Qualitative and SemiQuantitative Simulation
(Teacher: Ben
Kuipers)
 unit A5.1: modeling and simulation with incomplete knowledge
 introduction and motivation
 Qualitative Differential Equations and qualitative behavior:
 Qualitative Simulation
(incl. constraint filtering, behavior filters, behavior
abstraction)
 research problem/question
 unit A5.2: semiquantitative simulation
 partial quantitative knowledge, semiquantitative differential
equations
 semiquantitative simulation: Q2, Q3, NSIM
 semiquantitative system identification
 open research problems
 unit A5.3:
 abstraction methods that help make qualitative simulation
tractable
 temporal logic methods that make it possible to query the
results of qualitative simulation automatically (rather than
inspecting them by hand)
Two hour lectures A two hour lecture
consists of 2 units. Each unit lasts for one hour, including a 15 min
break between units.
Lecture B1: Modelbased Diagnosis in the Automotive Industry
(Teacher: Peter
Struss)
 unit B1.1: introduction, motivation and problems
 what are the characteristics of the task and/or domain
 what are the problems (in terms of the domain and/or task)
 what are the challenges for the MBS/QR technology
 problems and solutions
 unit B1.2: examples and remaining problems
 problems and solutions (cont'd)
 applications and examples
 future (research) goals
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Lecture B2: Modelbased Reasoning in Ecology Domains
(Teacher: Peter
Struss)
 unit B2.1: introduction, motivation and problems
 what are the characteristics of the task and/or domain
 what are the problems (in terms of the domain and/or task)
 what are the challenges for the MBS/QR technology
 problems and solutions
 unit B2.2: examples and remaining problems
 problems and solutions (cont'd)
 applications and examples
 future (research) goals
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Lecture B3: Modelbased Reasoning for Control tasks & Medical
applications (Teacher: Unit B3.1 Ben Kuipers and Unit
B3.2 Liliana Ironi)
 unit B3.1 Modelbased reasoning for control tasks
 introduction
 examples of control: feedback, adaptive, integrated control
 overview of conventional control approaches and their
limitations
 motivations for qualitative/modelbased reasoning for control
 unit B3.2: QR and medical applications
 how might medical reasoning/tasks benefit from QR
 modeling ontologies for pathophysiological systems
 which level of representation
(pure qualitative, semiquantitative, quantitative) versus
tasks examples
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Lecture B4: Diagrammatic Reasoning and Visualisation of System
Behaviour (Teacher: Alan Blackwell)
 unit B4.1: introduction and motivation
 what are the problems
 overview on research in this area
 unit B4.2: examples and remaining problems
 problems and solutions (cont'd)
 applications and examples
 future research goals
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Lecture B5: Qualitative Reasoning and Mathematical Modeling
(Teacher: Liliana
Ironi)
 unit B5.1 quantitative modeling
 introduction: what is mathematical modeling, what are the
problems
(physical and numerical accuracy)
 integration of qualitative and quantitative approaches to deal
with:
 automated structural modeling (qualitative analysis of
experimental data, QR for delimiting the search model space, QR
for the choice of proper numerical methods and their possible
initialization)
 nonparametric/blackbox modeling (QR for the initialization
of a proper identifier scheme)
 brief overview of qualitativequantitative integrated approaches
and tools
 unit B5.2 a case study
 Automated modeling of viscoelastic materials and its use for
the assessment of physicochemical properties of drugdelivery
systems
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Lecture B6: Knowledge Management using MBS/QR Technology
(Teacher: Andy Steele)
 unit B6.1: introduction and motivation
 what are the issues?
(to enable organisations to compete effectively in emerging
knowledge economy; can help represent qualitative/heuristic/tacit
knowledge in easytounderstand structured format, thus enabling
sharing and reuse of valuable corporate and industrial
knowledge)
 overview on research in this area
(problems and solutions, e.g.: qualitative influence diagrams
for physical or business process modelling)
 unit B6.2: examples and remaining problems
 problems and solutions (cont'd)
 applications and examples (success stories?)
 future research goals
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Lecture B7: Qualitative Spatial Reasoning and Kinematics
(Teacher: Tony
Cohn)
 unit B7.1: introduction, motivation and problems
 what are the problems
 overview on research in this area
 unit B7.2: examples and remaining problems
 problems and solutions (cont'd)
 applications and examples
 future research goals
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Teachers Teachers were
senior researchers and experienced users with a wide range of insights.
They were able to present clearly the main ideas, point out problems,
discuss outstanding issues, and reflect on the area as a whole. Teachers
were encouraged to stay around during the whole summer school and mingle
with participants, so that they were available for indepth discussion
and questioning by students.
List of TeachersAlan
Blackwell Computer Laboratory University of
Cambridge Cambridge, UK
Bert
Bredeweg Department of Social Science Infomatics
(SWI) Universiteit van Amsterdam Amsterdam, The Netherlands
Tony
Cohn School of Computer Studies University of
Leeds Leeds, UK
Luca
Console Dipartimento di Informatica Universita' di
Torino Torino, Italy
Ken
Forbus Institute for the Learning Sciences Northwestern
University Evanston, Illinois, USA
Liliana
Ironi Istituto di Analisi Numerica (CNR) Pavia, Italy
Ben
Kuipers Department of Computer Sciences The University of
Texas at Austin Austin, Texas, USA
Andy
Steele Unilever London, UK
Peter
Struss Department of Computer Science Technical
University of Munich Munich, Germany
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Location The Summer School
took place in the University
Residential Centre of Bertinoro.
Bertinoro is half way between Forlė and Cesena, 6 kilometres from
State Road No.9 (Via Emilia), immediately east of Forlimpopoli. For
details of how to reach the Centre, see the Centre's "How to Reach Us"
page (http://www.spbo.unibo.it/bertinoro/ecomeraggiungerci.html)
You may find the "Ferrovie
dello Stato" (Italian Railway) web site useful for train timetables
etc.
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Organising
Committee
Bert
Bredeweg Department of Social Science Infomatics
(SWI) Universiteit van Amsterdam Amsterdam, The Netherlands
Liliana
Ironi Istituto di Analisi Numerica (CNR) Pavia,
Italy
Louise
TraveMassuyes LAAS/CNRS Toulouse, France
