(AI)
Connexionist
Models (ANN), Evolutionary Computation, Collaborative Resolution
(“Swarm intelligence“)
(Stats) Multiple sand Simple Linear Regression Models, Variance
Analysis, Time Series Models
(AI&Stats) Regression Trees / Model Trees
Uncertainty Models
(Stats) Pure Probablistic
Model
(AI&Stats) Bayesian Networks
(AI) Certainity Factor Model
(AI) Possibilistic Model (Fuzzy Logic)
l
Postprocessing and Model Validation
Postprocessing Techniques
Validation
Graphical Validation Tools
Validation methods for Predictive and
Discriminant Models
Validation methods for Descriptive and Associative Models
Statistical Methods for Hypotheses Verification
Software Tools for IDSS Development
GESCONDA
KLASS
WEKA
System
R
DAVIS
Application of IDSS to
real-world problems
DAI-DEPUR+
PSARU
Future Trends in IDSS and
Conclusions
Learning Resources
Literature
References
Internet Resources
Course
material
The “Racó de la
FIB” is the Virtual Campus for you. There, you will find
any announcement, change in the schedule, the practical works,
the results of the evaluation of the works and final marks,
etc. There you will also find a pointer to this WEB page of
the course where you could find some other material (slides,
internet references, etc.) in electronic format
2011/2012 Sylabus
Part 1 - Introduction/Decision Theory
Part 2 - Evolution of DSS/IDSS
Part 3 - KDD in
IDSS/Preprocessing
Part 4 - DM Methods/Descriptive & Associative Models
Part 5 - DM
Methods/Discriminant & Predictive Models
Part 6 - Uncertainty Models
Part 7 -
PostProcessing and Model Validation
Part 8 - Tools
and Applications
Part 9 - Future Trends in IDSS/Conclusions
2012/2013 Sylabus
Part 1 - Introduction/Decision Theory
Part 2 - Evolution of DSS/IDSS
Part 3 - KDD in IDSS/Preprocessing
Part 4 - DM Methods/Descriptive &
Associative Models
Part 5 - DM Methods/Discriminant & Predictive Models
Part 6 - Uncertainty Models
Part 7 - PostProcessing and Model Validation
Part 8 - Tools and Applications
Part 9 - Future Trends in
IDSS/Conclusions
Case Study 1a - Implementation of an IDSS in a computer (I)
(Customer Fidelization Case)
Case Study 1b - Implementation of an
IDSS in a computer (II) (Optimal Wastewater Treatment System
Assignment Case)
Case Study 2 - The use of Descriptive Models for Decision
Support (Health/Medical Case)
Case Study 3 - The use of Associative
Models for Decision Support (I)
Case Study 4 - The use of
Predictive Models for Decision Support (Breast Cancer Case)
Case Study 5 - The use of
Discriminant Models for Decision Support (I) (Wastewater
Treatment Plant Management Case)
Case Study 6 - The use of
Discriminant Models for Decision Support (II) (The Textile
Industry Case)
Case Study 7 - The use of Associative Models for Decision
Support (II) (Ecological Interrelationships Case)
Practical
exercises
Three practical works must be done by all students in the
course (PW1, PW2 and PW3). The practical works will be:
PW1 (Individual work) - Modelling a decision making process in a
concrete domain.
PW1 will be given on: February 15th, 2013
PW1 is due on: October 22nd, 2013
PW2 (Individual work) - Reviewing the state of the art of
Intelligent Decision Support Systems.
PW2 will be given on: February 22nd, 2013
PW2 is due on: March 8th, 2013
PW3 (Group work) - A research work, including some practical
software development, about several features of Intelligent
Decision Support Systems, such as in some data mining models.
PW3 will be given on: February 22nd, 2013
PW3 is due on: May 24th, 2013
The work delivery
and oral exposition of the PW3 will be done on Friday 24th May 2013 (20
min. approx. for each group)
Evaluation
Evaluation of
the Knowledge and skills obtained by the students will be
assessed through the 3 practical Works. The final grade will
be the weighted mean of the grade of each practical
work. Each practical work will have the following
weights:
PW1 -> 10%
PW2 -> 20%
PW3 -> 70%
Thus, the final grade will be computed as follows:
Evaluation of the course performance will be based on three
marks coming from the three exercises. The first exercise
about decision modelling will have a 10% weight, the second
one about the state of the art in IDSS will have a 20% weight
and the research and software development part will have a 70%
weight in the final evaluation. The first exercise will be
evaluated by means of its quality and its justified
explanation in the document. The second exercise will be
evaluated according to its accuracy and completeness.The third
exercise will be evaluated through the quality of the research
undertaken, the software and/or documentation delivered, and
the quality of the oral exposition (both presentation and
content assessed).
Honour Code
(adapted from Stanford University Honor Code)
A. The Honor Code is an undertaking of the
students, individually and collectively:
that they will not give or
receive aid in examinations; that they will not give or
receive unpermitted aid in class work, in the preparation
of reports, or in any other work that is to be used by the
instructor as the basis of grading;
that they will do their share
and take an active part in seeing to it that others as
well as themselves uphold the spirit and letter of the
Honor Code.
B. The faculty
on its part manifests its confidence in the honor of its
students by refraining from proctoring examinations and from
taking unusual and unreasonable precautions to prevent the
forms of dishonesty mentioned above. The faculty will also
avoid, as far as practicable, academic procedures that create
temptations to violate the Honor Code.
C. While the faculty alone has the right and
obligation to set academic requirements, the students and
faculty will work together to establish optimal conditions for
honorable academic work.
Examples of conduct which have been regarded as being in
violation of the Honor Code include:
• Copying from another’s examination paper
or allowing another to copy from one’s own paper
• Unpermitted collaboration
• Plagiarism
• Revising and resubmitting a quiz or exam
for regrading, without the instructor’s knowledge and consent
• Giving or receiving unpermitted aid on a
take-home examination
• Representing as one’s own work the work of
another
• Giving or receiving aid on an academic
assignment under circumstances in which a reasonable person
should have known that such aid was not permitted
In recent years, most student disciplinary cases have involved
Honor Code violations; of these, the most frequent arise when
a student submits another’s work as his or her own, or gives
or receives unpermitted aid. The standard penalty for a first
offense or multiple violations will be proposed by the
teachers in charge of the course, and approved by the Academic
Commission of the Master MAI.
Resources for student learning
Literature
Basic References
P. C. Bell. Decision Support
Systems: Past, Present and Prospects. Revue des systèmes
de décision 1(2-3):126-137, 1992.
A. J. Beulens and J. A. Van Nunen. The Use of Expert System
Technology in DSS. Decision Support Systems 4(4):421-431,
1988.
R. J. Brachman and T. Anand. The Process of Knowledge
Discovery in Databases. In Advances in Knowledge Discovery
and Data Mining (Eds. U.M. Fayyad, G. Piatetsky-Shapiro, P.
Smyth & R. Uthurusamy). AAAI Press / MIT Press, 1996.
E. Charniak & D. McDermott. Introduction to Artificial
Intelligence. Addison-Wesley, 1985.
J. Comas, S. Dzeroski, K. Gibert, I. R.--Roda, M.
S\'anchez--Marr\`e. Knowledge Discovery by means of
inductive methods in wastewater treatment plant data.
AI Communications, 14, 45—62, 2001
U. Cortés, M. Sànchez-Marrè, L. Ceccaroni, I. R-Roda y M.
Poch. Artificial Intelligence and Environmental Decision
Support Systems. Applied Intelligence 13(1):77-91, July
2000.
Curwin, J. & Slater, R. Quantitative Methods for
Business Decisions, Thomson Business Press, 2001.
A. L. Delbelcq. The Management of Decision-Making within the
Firm: Three Strategies for Three Types of Decision-Making.
Academy of Management Journal, 1967.
Dewhurst, F. Quantitative Methods for Business and
Management, McGraw-Hill, 2002.
Dhar, V. & Stein, R.
Intelligent Decision Support Methods, Prentice-Hall, 1997.
Dhar,
V. & Stein, R. Seven Methods for Transforming Corporate
Data Into Business Intelligence. Prentice-Hall, 1997.
J. C. Emery. Management Information Systems. The Critical
Strategic Resource. New York: Oxford University Press, 1987.
U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth & R.
Uthurusamy. Advances in Knowledge Discovery and Data Mining.
AAAI Press / MIT Press, 1996.
J. Fox and S. Das. Safe and sound. Artificial Intelligence
in Hazardous Applications, AAAI Press / The MIT Press, 2000.
K. Gibert, J. Izquierdo, G. Holmes, I. Athanasiadis, J.
Comas, M. Sànchez-Marrè. On the role of pre and
post-processing in Environmental Data Mining. In Procs.
iEMSs’08 (II-Workshop on Data Mining for Environmental
Scientitsts DM-TES’2008). vol. III, 1937-1958, 2008.
K. Gibert, M. Sánchez-Marrè, V. Codina. Elección de la
técnica de minería de datos: Mapa conceptual de técnicas.
Procs. V Simposio de Teoría y Aplicaciones de la Minería de
Datos (III Congreso Nacional de Informática), 37- 44.
Ibergaceta Publicaciones, 2010.
K. Gibert, M. Sànchez-Marrè, I. Rodríguez-Roda, E. Bueno, L.
Mozo, A. Clavell, M. Martín, P. Rougé. Development of an
Intelligent Data Analysis System for Knowledge Management in
Environmental Data Bases. Procs iEMSs02, vIII, 420-425,
2002.
S.O. Funtowicz and J.R. Ravetz. Post-Normal Science – an
insight nom maturing, Futures 31(7), 641-646, 1999.
S.O. Funtowicz and J.R. Ravetz. Science for the post-normal
age, Futures 25(7), 739-755, 1993.
Jakeman, A. J., Voinov, A., Rizzoli, A. and Chen, S.
Environmental Modeling, Software and Decision Support. State
of the art and New Perspectives. IDEA Series 3, Elsevier,
NL, 2008. Particularly interesting the two following
chapters:
M. Sànchez-Marrè, K.
Gibert, R. S. Sojda, J. P. Steyer, P. Struss, I.
Rodríguez-Roda, J. Comas, V. Brilhante, E. A. Roehl.
Intelligent Environmental Decision Support Systems. IDEA
Series 3, 119—14.
K. Gibert, J. Spate, M.
Sànchez-Marrè, I. Athanasiadis, J. Comas. Data Mining for
Environmental Systems, IDEA Series 3, 205-228.
I.G. Haagsma and R.D. Johanns, “Decision support systems: An
integrated approach,” in Environmental Systems, edited by P.
Zannetti, vol. II, pp. 205–212, 1994.
P. G. Keen and M. S. Scott-Morton. Decision Support Systems:
An Organizational Perspective. Reading, MA: Addison-Wesley,
1978.
M. R. Klein and L. B. Methlie. Knowledge-Based Decision
Support Systems. 2nd edition. New York: Wiley, 1995.
J.D. Little. Models and Managers: The Concept of a Decision
Calculus. Management Science 16(8):B466-85, 1970.
G.M. Marakas. Decision Support Systems in the
Twenty-first Century. Upper Saddle River, NJ:
Prentice-Hall, 1999, 2nd edition, 2003.
B. C. McNurlin and R. Sprague Jr. Information Systems
Management in Practice. 4th edition. New Jersey:
Prentice-Hall, 1993.
H. Mintzberg. The Nature of Managerial Work. Englewood
Cliffs, NJ: Prentice-Hall, 1973.
Mitchell, T. Machine Learning, McGraw-Hill, 1997.
J. H. Moore and M. G. Chang. Design of Decision Support
Systems. Data Base 12(1-2), 1982.
Morris, C. Quantitative Approaches in Business Studies.
Financial Times/Prentice Hall, 2002.
Daniel J.
Power. Decision Support Systems: concepts and resources
for managers. Greenwood Publishing Group, 2002
C.K. Riesbeck and R.S. Schank. Inside Case-Based Reasoning.
Lawrence Erlbaum Associates Publishers, 1989.
E. Rich and K. Knight. Artificial Intelligence.
McGraw-Hill, 2nd edition, 1991.
M. Sànchez-Marrè, K. Gibert, R. Sojda, J. Philippe Steyer,
P. Struss, I. Rodríguez-Roda. Uncertainty Management,
Spatial and Temporal Reasoning and Validation of Intelligent
Environmental Decision Support Systems (AITENV). Procs.
iEMSs Third Biennal Meeting: "Summit on Environmental
Modelling and Software", II IEDSS Workshop, CD-Rom,
Burlington, Ver, USA, 2006.
http://www.iemss.org/iemss2006/sessions/all.html
R. Schank. Dynamic memory: a theory of learning in computers
and people. Cambridge University Press, 1982.
R. Schank and R. Abelson. Scripts, plans, goals and
understanding. Lawrence Erlbaum, 1977.
Senn,
J. A. Information Technology: Principles, Practices, and
Opportunities. Prentice-Hall, 2003.
H. A. Simon. The New Science of Management Decision. New
York: Harper & Row, 1960.
J. Spate, K. Gibert, M. Sànchez-Marrè, E. Frank, I.
Athanasiadis, A. Vellido. Data Mining as a tool for
analyzing environmental systems. Procs LEARNING 2006
(Workshop TAMIDA), 2006.
J. Spate, K. Gibert, M. Sànchez-Marrè, E. Frank, J. Comas,
I. Athanasiadis, R. Letcher. Data Mining as a tool for
environmental scientist. Procs. iEMSs Third Biennal Meeting:
"Summit on Environmental Modelling and Software" (I DMTES
workshop) CD-Rom, Burlington, Vermont, USA, 2006. http://www.iemss.org/iemss2006/sessions/all.html
L. Steels. Ten Years. VUB Artificial Intelligence
Laboratory. Vrije Universiteit Brussel, 1993.
J. D. Thompson. Organizations in Action. New York:
McGraw-Hill, 1967.
E.
Turban, J.E. Aronson, T-P Liang. Decision Support
Systems and Intelligent Systems. Pearson/Prentice Hall,
2005
Witten, I.H. & Frank, E.
Data Mining. Practical Machine Learning Tools and
Techniques with Java Implementations, Morgan Kaufman,
2000.
Other
IDSS references and IDSS Applications
2012
M. Poch, U. Cortés, J. Comas, I.
Rodríguez-Roda, M. Sànchez-Marrè. Decisions on Urban
Water Systems: some Support. Servei de Publicacions,
Universitat de Girona. ISBN 978-84-8458-401-8. September
2012. http://hdl.handle.net/10256/7162.
B. Sevilla, M. Sànchez-Marrè.Case-Based
Reasoning Applied to Textile Industry Processes. Procc. of 20th International
Conference on Case-Based Reasoning (ICCBR’2012)"Lecture
notes in computer science", 2012, vol. 7466, p.
428-442.Lyon,
France, 3-6 September 2012. <doi:
10.1007/978-3-642-32986-9_32>
K.
Gibert, M. Sànchez-Marrè, B. Sevilla(2012) Tools
for environmental data mining and intelligent decision
support. in
R. Seppelt et al. eds. Proccedings of iEMSs 2012,
pp.1726-1734. Leipzig, Germany
M. Sànchez-Marrè,
K. Gibert, A. Cabello, F. Sem(2012): A
methodology for the characterization of intelligent
environmental decision support systems. In Proceedings
of iEMSs'2012 (R. Seppelt et al eds ): 153-162, Leipzig,
Germany
K. Gibert, M.
Sànchez-Marrè(2012)
A picture on Environmental Data Mining Real
Applications. What is done and how?. In Proceedings of
iEMSs'2012 (R. Seppelt et al eds): 1612-1619, Leipzig,
Germany
K. Gibert, D.
Conti, D. Vrecko(2012)
Assisting the end-user in the interpretation of profiles
for decision support. An application to wastewater
treatment plants. Environmental Engineering and
Management Journal, 11(5): 931-944.
2011
K. Gibert, M. Sànchez-Marrè(2011) Outcomes
from the iEMSs Data Mining in the Environmental Sciences
Workshop Series. Environmental Modelling and Software, 26:
983-985 doi: 10.1016/j.envsoft.2011.01.009
Karina Gibert, Gustavo Rodríguez-Silva,
Roberta Annicchiarico (2011)
Post-processing: bridging the gap between modelling and
effective decision-support. The Profile Assessment Grid in
Human Behaviour. Mathematical and Computer Modelling
<doi: 10.1016/j.mcm.2011.10.046>, Elsevier
2010
K. Gibert, M. Sànchez-Marrè, V. Codina.
Choosing the right data mining technique: classification
of methods and intelligent recommenders. Proc. of the
IEMSs’10, 5th biennial meeting (III DMTES Workshop),
S23.03.1-S23.03.9, 2010.
M. Sànchez-Marrè, K. Gibert, B. Sevilla.
(2010). Evolving GESCONDA to an intelligent decision
support tool. Proc. of the iEMSs’10, 5th biennial meeting,
(IV IEDSS Workshop), S24.04.1-S24.04.10, 2010.
K. Gibert, E. Rojo, J. Rodas(2010). Effects
of using mean curve in the analysis of repeated time
series. Acta Informatica Medica 18: 140-146
K. Gibert, C. García-Alonso, L.
Salvador Carulla (2010). Integrating clinicians, knowledge
and data: expert-based cooperative analysis in healthcare
decision support. Health Research Policy and Systems
8(28):1-16
2009
Montse
Aulinas, Clàudia Turon and Miquel Sànchez-Marrè (2009). Agents as a
Decision Support Tool in Environmental Processes: The
State of the Art. In the book:Advanced
Agent-Based Environmental Management Systems (Ulises
Cortés y Manel Poch, Editors). In Series:
Whitestein Series in Software Agent Technologies and
Autonomic Computing. Birkhäuser, Verlag Basel/Switzerland,
2009. ISBN 978-3-7643-8897-3.
2008
A.
Chieppa, K. Gibert, I. Gómez-Sebastià, M. Sànchez-Marrè.
Improving pseudobagging techniques. Artificial
Intelligence Research and Development. Frontiers in
Artificial Intelligence and Applications, 184, 161—169,
IOS Press, NL, 2008.
K. Gibert, R. Nonell. Pre and post-processing
in KLASS. Proc. of the iEMSs 4th biennal Meeting (II
DM-TES'08 Workshop), vol III, 1965-1966, 2008.
M. Sànchez-Marrè, K. Gibert. GESCONDA: From
environmental data mining to environmental decision
support. Proc. of the iEMSs 4th biennal meeting (II
DM-TESS'08 Workshop), 1967-1970, 2008.
K. Gibert, A. García-Rudolph, G.
Rodríguez-Silva(2008)
The role of KDD Support-Interpretation tools in the
conceptualization of medical profiles: An application to
neurorehabilitation. Acta Informatica Medica 16(4):
178-182
2007
K.
Gibert, X. L. Martorell, R. Massanet, M. Sánchez-Marrè, J.
C. Martín-Sánchez, A. Martorell. A Knowledge Discovery methodology for
identifying vulnerability factors of mental disorder in an
intellectually disabled population. Artificial
Intelligence Research and Development. Frontiers in
Artificial Intelligence and Applications, 163, 426-435,
IOS Press, NL, 2007.
K. Gibert, I. Pinyol, L. Oliva, M.
Sànchez-Marré. Pseudobagging: Improving class discovery by
adapting bagging techniques to clustering algorithms. V
Taller Nacional de Minería de Datos y Aprendizaje (TAMIDA
2007), CEDI2007, 131-138, Thomson, Madrid,2007.
2006
X.
Flores, J. Comas, K. Gibert, M. Sànchez-Marrè and I.
Rodríguez-Roda. Comparison of
different clustering techniques in environmental data
bases using GESCONDA. Procs. iEMSs Third Biennal Meeting:
"Summit on Environmental Modelling and Software" (II IEDSS
workshop) CD-Rom, Burlington, Vermont, USA, 2006. http://www.iemss.org/iemss2006/sessions/all.html
X.
Flores, J. Comas, K. Gibert, M. Sanchez-Marre, I.
Rodriguez Roda. A Data Mining
Approach to Enhance Knowledge Extraction in Environmental
Databases. Voinov et al eds. Procc. of iEMSs Third Biennal
Meeting: "Summit on Environmental Modelling and Software",
CD-Rom, Burlington, Vermont, USA, 2006. http://www.iemss.org/iemss2006/sessions/all.html
K. Gibert, M. Sànchez-Marrè, J. Comas. The
impact of feature weighting in environmental class
discovery using GESCONDA. In Procs 17th ECAI, BESAI
Workshop, paper 7, 2006.
2005
K. Gibert, X. Flores, M. Sánchez Marrè. Comparación
de clasificaciones en bases de datos ambientales
utilizando GESCONDA. III Taller de Minería de Datos y
Aprendizaje (TAMIDA 2005), Digital3,
299-308, 2005, También en Procs CEDI 2005, S24-S24.
K.
Gibert, M. Sànchez-Marrè, I. Rodríguez-Roda. GESCONDA: An Intelligent Data Analysis
System for Knowledge Discovery and Management in
Environmental Databases. Environmental Modelling and
Software, 21(1), 115—120, 2005
2004
K. Gibert, X. Flores, I. Rodríguez-Roda, M.
Sánchez-Marrè Knowledge Discovery in environmental
databases using GESCONDA. Second Biennal Meeting of the
iEMSs (I IEDSS workshop), vol I, 51-56. Switzerland,2004. K. Gibert, X. Flores, M. Sànchez-Marrè.
Comparison of classifications in environmental data bases
using GESCONDA. In procs ECAI, BESAI Workshop, 13-1/13-10
2004.
M.
Sánchez-Marrè, K. Gibert, I. Rodríguez-Roda. GESCONDA: A tool for Knowledge Discovery
and Data Mining in Environmental Databases. In Prastacos,
et al eds: e-environment: Progress and Challenge, Research
on Computing Science Series, 11, 355-370. Ins. Politécnico
Nacional, México D. F., 2004.
2003
J.L. Arcos. Development of an Industrial Design Support
System for Air Pollution Treatment. Procc. of IJCAI’03
Workshop on Environmental Decision Support Systems
(EDSS’03), pp. 1-8, Acapulco, 2003.
J. Hastings, K. Branting, J. Lockwood and S. Schell.
CARMA+: A General Architecture for Pest Management. Procc.
of IJCAI’03 Workshop on Environmental Decision Support
Systems (EDSS’03), pp. 18-21, Acapulco, 2003.
J. Wiese, S. Schmitt, R. Bergmann and T. G. Schmitt. A
Case-Based Predictive Sequencing Batch Reactor Controller.
Procc. of IJCAI’03 Workshop on Environmental Decision
Support Systems (EDSS’03), pp. 22-28, Acapulco, 2003.
R. Srinivasan and I. Halim. Multi-perspective Models for
Diagnosing Waste Generation in Chemical Processes. Procc.
of IJCAI’03 Workshop on Environmental Decision Support
Systems (EDSS’03), pp. 44-49, Acapulco, 2003.
P. Struss, M. Bendati, E. Lersch, W. Roque and P.
Salles. Design of a Model-based Decision Support System
for Water Treatment. Procc. of IJCAI’03 Workshop on
Environmental Decision Support Systems (EDSS’03), pp.
50-59, Acapulco, 2003.
M. Martínez, M. Sànchez-Marrè, J. Comas and I.
Rodríguez-Roda. Defining a Decision Support System to
Manage Filamentous Bulking Episodes in Activated Sludge
Systems. Procc. of IJCAI’03 Workshop on Environmental
Decision Support Systems (EDSS’03), pp. 69-76, Acapulco,
2003.
2002
D. Nute, W.D. Potter, F. Maier, J. Wang, M. Twery, H.M.
Rauscher, P. Knopp, S. Thomasma, M. Dass and H. Uchiyama.
Intelligent Model Management in a Forest Ecosystem
Management Decision Support System. Procc. of 1st
International Conference on Environmental Modelling and
Software (iEMSs’2002), pp. 396-401, Lugano, 2002.
F. Borrell, D. Riaño, M. Sànchez-Marrè and I.
Rodríguez-Roda. Implementation of a Multiagent Prototype for
WWTP Management. Procc. of 1st International Conference on
Environmental Modelling and Software (iEMSs’2002), pp.
402-407, Lugano, 2002.
A. Perini and A. Susi. Designing a Multi-Agent System for
Integrated Protection in Agriculture. Procc. of 1st
International Conference on Environmental Modelling and
Software (iEMSs’2002), pp. 408-413, Lugano, 2002.
L. Ceccaroni, U. Cortés and M. Sànchez-Marrè. OntoWEDSS:
an ontology-underpinned decision-support system for
wastewater management. Procc. of 1st International
Conference on Environmental Modelling and Software
(iEMSs’2002), pp. 432-437, Lugano, 2002.
J. Comas, E. Llorens, M. Poch, G. Markakis, T. Battin, S.
Gafny, E. Maneux, E. Martí, M. Morais, M.A. Puig, M. Pusch,
J.L. Riera, F. Sabater, A.G. Solimini and P. Vervier. The
STREAMES project: linking heuristic and empirical knowledge
into an Expert System to assess stream managers. Procc. of
1st International Conference on Environmental Modelling and
Software (iEMSs’2002), pp. 444-449, Lugano, 2002.
P. Salles, B. Bredeweg, S. Araujo and W. Neto. Qualitative
Models of Interactions between Two Populations. Procc. of
3rd ECAI Workshop on Binding Environmental Sciences and
Artificial Intelligence (BESAI’2002), pp. 1-1:1-8, Lyon,
2002.
N. Conruyt, D. Grosser, Y. Geynet, G. Faure, M. Pichon, M.
Guillaume and N. Gravier-Bonnet. Knowledge Engineering in
Environmental Sciences: Application to Systematics of Corals
of the Mascarene Archipelago. Procc. of 3rd ECAI Workshop on
Binding Environmental Sciences and Artificial Intelligence
(BESAI’2002), pp.
2-1:2-10, Lyon, 2002.
U. Cortés, I. Rodríguez-Roda, J. Comas, M. Sànchez-Marrè,
M. Martínez and M. Poch. A Conceptual Model to Facilitate
Knowledge Sharing for Problem Solving in Wastewater
Treatment Plants. Procc. of 3rd ECAI Workshop on Binding
Environmental Sciences and Artificial Intelligence
(BESAI’2002), pp. 7-1:7-6, Lyon, 2002.
F. Fdez-Riverola, J.M. Corchado and J.M. Torres.
Forecasting Red Tides Using an Hybrid Neuro-Symbolic System.
Procc. of 3rd ECAI Workshop on Binding Environmental
Sciences and Artificial Intelligence (BESAI’2002), pp.
10-1:10-9, Lyon, 2002.
L. Lardon, A. Puñal, J.P. Steyer, E. Roca, J. Lema, S.
Lambert, P. Ratini, S. Frattesi and O. Bernard.
Specifications of Modular Internet-Based Remote Supervision
Systems for Wastewater Treatment Plants. Procc. of 3rd ECAI
Workshop on Binding Environmental Sciences and Artificial
Intelligence (BESAI’2002), pp. 5-1:5-7, Lyon, 2002.
2000
E.D. Gamboa. Water Resource Management: a Case Study for
EcoKnowMICS. In Decision Support Systems for Sustainable
Development (Eds. G.E. Kersten, Z. Mikolajuk & A.G.
Yeh), pp. 99-130. Kluwer Academic Publishers, 2000.
M. Rais, S. Gameda, E.T. Craswell, A. Sajjapongse &
H.-D. Bechstedt. DSS for Sustainable Land Management: a
South-East Asia Case. In Decision Support Systems for
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