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\begin{document}

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%\lfoot{\tiny Ingénierie Mathématique 99}
%\lfoot{\tiny Hinzergarten 2002}
%\lfoot{\tiny ~}
%\cfoot{\tiny \thepage}
%\rfoot{\tiny M. Sebag}
%\rfoot{\tiny ~}
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\begin{slide}
\begin{center}
{\Large\blue Impact Studies and Sensitivity Analysis in Medical Data Mining \\~\\
{\red with ROC-based Genetic Learning}\\~\\}
{\blue Michèle Sebag, Jérôme Azé, Noël Lucas}\\~\\~\\~\\
\end{center}
\centerline{\small LRI, CNRS UMR 8623, Université Paris-Sud Orsay}
\end{slide}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\Titre{Yet another learning criterion\\~{\small \hspace*{2in} Bradley 97, Provost et al. 98\\}~\\}
\Corps{
\hspace*{-.1in}\begin{tabular}{lll}
{\red Usual} predictive accuracy $\frac{a+d}{a + b + c + d}$ & \esp &{\blue Drawbacks}\\
\multicolumn{1}{c}{\begin{tabular}{ccc}
$\hat {tc}$ $\cdot$ $tc$ & positive & negative \\
positive &  a & b \\
negative & c & d \\
\end{tabular}} & &
\multicolumn{1}{c}{\blue\begin{tabular}{l}
$\bullet$ skewed distributions\\
\esp\esp (positive $<<$ negative)\\
$\bullet$ misclassification costs\\
\end{tabular}}
\end{tabular} \\~\\

{\red Receiver Operating Characteristics (ROC) Curve}:\\
%\vspace{-.8in}
\esp \hfill {\red True Positive Rate vs False Positive Rate}
\vspace{-.8in}
\centerline{\rotatebox{90}{\includegraphics*[width=1.5in,height=3in]{Roc_curve.pdf}}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Optimizing the Area Under the ROC Curve\\~{\small \hspace*{2in} Mozer et al. NIPS 01, Flach et al., ICML 02\\}}
\Corps{
$\bullet$ Search space : Linear hypotheses \hfill baseline: SVMs\\
$\bullet$ Criterion:\\
\esp $h \rightarrow$ Order on examples\\
\esp Quality(h) = Sum ranks positive examples\hfill {\blue\em to minimize}\\
~\\
\centerline{\red NP-complete optimization $\rightarrow$ Evolutionary Computation}
~\\
{\red Evaluation} on Irvine repository \bigskip
\NOTE{\black Artificial Evolution VI, 2004}
Compared to linear SVMs: similar performances\\
\esp\esp\esp\esp\esp\esp\esp\esp\esp\esp\esp\esp\esp smaller computational cost\\

\centerline{\red Application to Medical Data Mining}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Identify Atherosis Risk\\~\\}
\Corps{
\centerline{\includegraphics*[width=3in]{../Dessins/NL_plaque_rupture.jpg}}
{\red PKDD Challenge \hfill 2002, 2003}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Atherosclerosis\\}
\Corps{
%\vspace{-.8in}
{\blue \bf Experimental setting} 2/3 training, 1/3 test \hfill $\times$ 10\\
%\vspace{-.8in}\\
\begin{tabular}{l}
On each training set, 21 independent runs\\
Display the median ROC curve\\
\vspace{-1in}
\end{tabular}\\
\vspace{-.8in}\\
\centerline{{\includegraphics*[width=4in]{../Dessins/Athero_MED.pdf}}}
\vspace{-1.8in}\\
{\blue ROGER: ROC-based Genetic LearneR \hfill {\em vs} \hfill SVMTorch}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Influence Analysis - The tobacco factor~\\}
\Corps{
{\red Procedure}\\
\esp A = \{ 100 non smokers \}
\\\esp B = \{ 100 heaviest smokers \}
\\\esp Sort individuals in A (in B) by increasing risk
%\vspace{-1in}
\\\vspace{-1in}
\esp Plot (i, risk(i))\\
\vspace{-1in}
\hspace*{1in}{\includegraphics[width=12cm,height=12cm]{../Dessins/risque_tabac.pdf}}
\vspace{-1in}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Influence Analysis - The alcool factor\\}
\Corps{
{\red Procedure}\\
\esp A = \{ 100 light drinkers \}
\\\esp B = \{ 100 heavy drinkers \}
\\\esp Sort individuals in A (in B) by increasing risk
\\ \vspace{-1in}
\esp Plot (i, risk(i))\\
\vspace{-1in}
\hspace*{1in}{\includegraphics[width=12cm,height=12cm]{../Dessins/risque_alcool.pdf}}
%\vspace{-1in}

Beware: no light drinkers in the db...
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Sensitivity Analysis - For free}
\Corps{
\vspace{-1in}
%Exploitation of 21 runs \hfill (stochastic algorithm)\\
%\vspace{-1in}
\centerline{\includegraphics[width=15cm,height=15cm]{../Dessins/Influ.pdf}}
\vspace{-1in}
21 runs, 21 solutions, 21 curves: $(i, weight(attribute_i))$
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Conclusions - Perspectives\\~\\}
\Corps{
{\red Present}
\begin{itemize}
\item Good predictive performances
\item Affordable complexity
\item UNDERSTANDABLE RESULTS\\
\NOTE{\red Using Vision to Think, Card et al. 2001}
\end{itemize}
{\red Future}
\begin{itemize}
\item Extend to kernel spaces
\item Use for constructive induction
\end{itemize}
}\FIN

\FIN


\ROC
\hfill (on test set)\\

{\red BUT}\\

$\bullet$ Find valid, novel, useful regularities \hfill Fayyad 96\\
\esp {\black $\rightarrow$ Identify the complex system underlying the data}\\

{\red Medical Data Mining}\\
$\bullet$ Discriminate diseases {\black $\rightarrow$ Identify medical risk factors}\\
$\bullet$ Data quality: \esp  biological variability; time window; privacy\\
$\bullet$ Experts want to {\black UNDERSTAND}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Overview\\~\\}
\Corps{\black
\begin{enumerate}
\item Change the problem formulation
\item A novel optimization criterion + EAs
\item Outcomes:
{\blue \begin{itemize}
\item Impact studies
\item Sensitivity analysis
\end{itemize}}
\end{enumerate}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{A Medical Data Mining Application\\~\\}
\Corps{
\centerline{\includegraphics*[width=3in]{../Dessins/NL_plaque_rupture.jpg}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Understanding Cardio-vascular Diseases\\~\\}
\Corps{
{\red PKDD 2002-2003 Challenge} \\
$\bullet$ Study Atherosclerosis Risk Factors\\
 \NOTE{\black First death cause in Western countries}

{\red Data}\\
$\bullet$ ENTRY database (medical cliché, 1419 men, 219 attributes, 1976)\\
$\bullet$ CONTROL database (longitudinal study of a sample, 1976-1996)\\

{\red First goal}\\
$\bullet$ Given the medical cliché at $t_0$, predict health state at $t_0 + 20$.
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\Titre{Some limitations of the data\\~\\}
\Corps{
{\red Initial description}:\\
\esp very detailed\NOTE{\black MI of 4$th$ sister}
\esp {\black ...more can be less}...\\

{\red What cannot be learned}: \hfill sufficient conditions for diseases\smallskip\\
\centerline{\black \fbox{\begin{tabular}{ll}
(1) & If father or mother diabetic\\
(2) & And high stress\\
(3) & And does not laugh once a day\\
& Then disease\\\end{tabular}}}
\smallskip
\esp ... (Condition 3 likely missing in hospital db)\\
\centerline{\red  $\rightarrow$ find at best necessary conditions }
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Changing the problem \\~\\}
\Corps{
{\red Initial goal: classification}\NOTE{\black predefined classes}
\centerline{Patient $\mapsto$ $\{$ normal, at risk, pathological $\}$}
~\\

{\red Alternative: ranking} \NOTE{\black Mr $X$ is more at risk than Ms $Y$}
\centerline{ (Patient $\times$ Patient) $\mapsto$ $\{ true, false\}$}
{\black $+$ more smooth (frontier between normal and pathological)\\
$+$ more flexible (medical / economical concerns)\\}

{\red Proposed: ?regression?}\NOTE{\black Risk(Mr $X$) is 3.7}
\centerline{ Patient $\mapsto$ \RR}

}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Quality of a Risk estimate}
\Corps{
{\red Ranking hypothesis}
\[ h : \mbox { Patient } \mapsto \RR \]
{\red $\equiv$ set of boolean hypotheses}
\[ \{ h_\tau : \mbox{Patient } \mapsto \{ 0,1\} ~, h_\tau(\mbox{Patient}) = (h(\mbox{Patient}) > \tau), \tau \in \RR \} \]
 
{\red Evaluate $h_\tau$}\\
\esp Predictive accuracy \hfill 1D\\
\esp (False positive rate, True positive rate)\hfill 2D\\~\\
{\red Receiver Operating Characteristics (ROC) Curve}\\
\esp depicts the trade-off FP rate / TP rate.
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{ROC Curve}
\Corps{
\vspace{-.5in}
\centerline{{\includegraphics*[width=4in]{../Dessins/ROC_curve.pdf}}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{ROC Curve, 2\\~\\}
\Corps{
{\red Used to compare learners}\hfill Bradley 97\\
\esp multi-objective-like\\
\esp insensitive to imbalanced distributions\\
\esp shows sensitivity to error cost.\\

{\red Used as learning criterion}: Area under the ROC curve\\
\esp Given Dataset = $\{ ({\bf x}_i,y_i), {\bf x}_i \in \RR^d, y_i \in \{-1,1\}~\}$\\
\esp Genotype $h$ (hypothesis) $\mapsto$ ordered examples\\
\esp \centerline{\small$ +++-++-++++---+---+---------$}
\esp ${\cal F}(h)$ = sum of ranks of positive examples.
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Area Under the ROC Curve\\~\\}
\Corps{
{\red Previous}\\
\esp EP-based NN optimization \hfill Fogel+, 1998\\
\esp GA-based linear optimization \hfill Mozer+, 2001\\
\esp greedy Decision Tree optimization \hfill Ferri-Flach, 2002\\

{\red ROGER}: ROC-based Genetic Evolutionary LearneR\\
\esp $(\mu+\lambda)$-ES\\
\esp Parameters
\centerline{\begin{tabular} {|r|cc|} \hline
population size & \# parents $\mu$ & 10\\
& \# offspring $\lambda$ & 50\\ \hline
 max nb evaluations & & 10,000\\ \hline
crossover & uniform & rate .6 \\
mutation & self-adaptive & rate 1\\ \hline
\end{tabular}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Experiments\\}
\Corps{
\black
{\red Reference results: Support Vector Machines (SVMTorch)}

{\red Datasets from Irvine repository}\\
\hspace*{-.4in}{\small \begin{tabular}{cc}
\begin{tabular}{|c ||cc  | cc|}
\multicolumn{5}{c}{~}\\\hline
& \#att & \#weight & \#Train & \#Test \\ \hline
Br. Canc. & 9 & 42 & 189 & 97\\
Crx & 15 & 47 & 70 & 620 \\
German & 25 & 25 & 100 & 900 \\
Promoters & 59 & 229 & 70 & 36\\
Satimage & 36 & 36 & 139 & 1237\\
Vehicle & 18 & 18 & 125 & 291\\
Votes & 16 & 32 & 287 & 148 \\
Waveform & 22 & 22 & 211 & 3321 \\\hline
\end{tabular}
&
\begin{tabular}{|c|c||c|c|} \hline
 \multicolumn{2}{|c|}{ROGER } & \multicolumn{2}{|c|}{SVMTorch } \\ \hline
 AUC & time & AUC & time\\ \hline
 .674 $\pm$ .05 & 7'' & .672 $\pm$ .05 & 1''\\
.816 $\pm$ .06 & 7'' & .839 $\pm$ .04 & 886'' \\ 
 .712 $\pm$ .03 & 6'' & .690 $\pm$ .02 & 96''\\
.863 $\pm$ .07 & 2''& .974 $\pm$ .02 & $<$ 1''\\
 .918 $\pm$ .01 & 4''& .876 $\pm$ .02 & 14''\\
 .994 $\pm$ .005 & 1''& .993 $\pm$ .007 & $<$ 1'' \\
.993 $\pm$ .004 & 7'' & .989 $\pm$ .005 & $>$ 1,000\\
 .971 $\pm$ .004 & 4''& .963 $\pm$ .008 & 2'' \\ \hline
\end{tabular}
\end{tabular}}
\smallskip\\
{\red Experimental setting}\\
\esp 10 train/test splits\\
\esp For each split, 1 SVMTorch run, 21 ROGER runs (take median)
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Partial conclusions - ML aspects\\~\\}
\Corps{
{\red PROS}
\begin{itemize}
\item Competitive wrt state of art, SVM.
\item Affordable cost, fitness computation $n log(n)$
\item Learning stability wrt imbalanced distribution, error cost
\end{itemize}
{\red Current limitations}
\begin{itemize}
\item Does not scale up well with \# attributes
\end{itemize}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Atherosclerosis}
\Corps{
Experimental setting: 2/3 training, 1/3 test \hfill $\times$ 10\\
\esp\esp On each training set, 21 independent runs\\
\vspace{-.8in}
\esp\esp Display the median ROC curve\\
\vspace{-1in}
\centerline{{\includegraphics*[width=4in]{../Dessins/Athero_MED.pdf}}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Influence Analysis - The tobacco factor~\\}
\Corps{
{\red Procedure}\\
\esp A = \{ 100 non smoking individuals \}
\\\esp B = \{ 100 most smoking individuals \}
\\\esp Sort A and B by increasing value of the risk
\\\esp Plot (i, risk(i))\\
\vspace{-3cm}
\hspace*{1in}{\includegraphics[width=12cm,height=12cm]{../Dessins/risque_tabac.pdf}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Influence Analysis - The alcool factor\\~\\}
\Corps{
\vspace{-1in}
\centerline{\includegraphics[width=12cm,height=12cm]{../Dessins/risque_alcool.pdf}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Sensitivity Analysis - For free}
\Corps{
\vspace{-1in}
%Exploitation of 21 runs \hfill (stochastic algorithm)\\
%\vspace{-1in}
\centerline{\includegraphics[width=15cm,height=15cm]{../Dessins/Influ.pdf}}
\vspace{-1in}
21 runs, 21 solutions, 21 curves: $(i, weight(attribute_i))$
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Conclusions - Perspectives\\~\\}
\Corps{
{\red Present}
\begin{itemize}
\item Good predictive performances
\item Affordable complexity
\item UNDERSTANDABLE RESULTS\\
\NOTE{\red Using Vision to Think, Card et al. 2001}
\end{itemize}
{\red Future}
\begin{itemize}
\item Extend to kernel spaces
\item Use for constructive induction
\end{itemize}
}\FIN

\centerline{\black \begin{tabular}{lccc}
& Int. & Joint. & Excel\\
Thematic programme & x & x & x \\
Tutorials& x & x & x \\
Open House& x & x & x \\
Challenges& x & x & x \\
Workshops &   & x & x\\
Pump-priming & x & x & x\\
Resources & & & x\\
Summer schools & & & x\\
PhD, Cur. development & & & x\\
Brokerage of Expertise & & & x\\
\end{tabular}}~\\~\\
{\red Measures of progress}\\
\esp publications and activities attendance\\
\esp spawning of funded new projects\\
\esp Downloads of resources\\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Responsibilities\\~\\}
\Corps{
Thematic programme \hfill J. Shawe Taylor + Bill Triggs\\
Visiting programme \hfill J. Shawe Taylor \\
Attending Conferences \hfill N. Cesa-Bianchi\\
Vision programme \hfill Bill Triggs\\
Speech programme \hfill S. Bengio\\
Haptic programme \hfill H. Wynn\\
Brain Computer Interfaces  \hfill José del R. Millan\\
Natural Language Processing \hfill Nicola Cancedda\\
Information Retrieval and Textual Information  \hfill D. Mladenic\\
MM Integration \hfill S. Bengio\\
User Modelling \hfill Martin Mühlenbrock\\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Responsibilities, 2\\~\\}
\Corps{
Links ML - Stats \esp \hfill P. Grünwald
and G. Lugosi\\
Links ML Stats -- Math. programming, optimisation\\\esp \hfill K. Schittkowski and JST\\
Links theory and algorithms \hfill B. Triggs and S.
 Bengio\\
Challenges \hfill Florence d'Alche-Buc\\
Tutorial workshop \hfill Z. Ghahramani\\
Summer Schools  \hfill B. Williamson, Canberra.\\
Conference Organisation  \hfill P. Grünwald\\ 
Brokerage of Expertise and Virtual science park \hfill
S. Bengio\\
}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Management\\~\\}
\Corps{
\begin{itemize}
\item Strategic Advisory Council (external members)
\item Steering Committee\\
{\black main programme, bidding, co-ordination, liaison}

\item Executive Committee\\
{\black budgets, quality, risk, delivery}
\item Individual programme Committees
\end{itemize}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Budgeting\\~\\}
\Corps{
{\black Total : 1.58 M E / year}
\begin{itemize}
\item 33\% for researchers : {\black 3 600 + 840 per PhD}\\
\item 67\% distributed on activities\\~\\
{\black 
\begin{tabular}{rl}
 8.2 &Knowledge Management (JST)\\
 22.3 &Infrastructure (Cancedda)\\
 8.4 &Wshops (Grunwald)\\
 11.2 &Pump-priming (Cesa-Bianchi)\\
 8.8 &External visitors (Singer)\\
\end{tabular}}
\end{itemize}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Questions - Answers\\~\\}
\Corps{
\begin{itemize}
\item Cooperation ML and MMI ? \\
{\black integration ! already for BCI, Vision, Text.\smallskip
}\item Why a limited involvment of neural nets ? \\
{\black $\rightarrow$ Kernel machines, Bayes nets...\smallskip
}\item Examples of MMI testbeds / showcases.\\
{\black BCI - User Modeling, Vision, Text\smallskip
}\item Position about software sharing / distribution\\
{\black Selected PASCAL code available eg GNU GPL\smallskip
}\item Funding of proposal writing ? \\
{\black funding pump-priming workshops\\
}\end{itemize}
}\FIN

\begin{itemize}
\item Infrastructure provision \\

\centerline{\black one man's interface is another
woman's deadlock}

\FIN
{\red Propositions / Pistes}\\
Paul B.: un outil permettant la description et la mise en 
                oeuvre de workflow (Société DENALI - 50 000 euros).\\

Jean S.:\\
\esp        Fonctionnalités : financières / agenda / missions.\\
\esp                Obligation de résultats.\\
\esp\esp Moyens (ex.) : Palms + obligation de synchronisation \\

Thierry Rayna:       Présentation de l'outil DENALI : matérialisation d'une description
                d'activités collectives sous forme de workflow compilé.\\

Discussion...
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Résolution\\~\\}
\Corps{
{\red 1. Externaliser les tâches}\\
\esp qui doivent être faites sérieusement\\
\esp et n'amusent personne (administration, gestion).\\

{\red APPROCHE}\\
\esp profiter des aides STIC/CEE pour faire simple\\
\esp \hfill (ndlc : en logiciels, pas en postes :-(\\
\esp déléguer la gestion à XXX (8-10\% de frais)\\
\esp\hfill sur les futurs contrats
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\Titre{Résolution, suite\\~\\}
\Corps{
{\red 2. Expérimenter pour la gestion de contenu}\\
\esp murs informatiques de l'institut sans mur\\

{\red APPROCHE}\\
\esp faire (nous-qui ?) un brouillon\\
\esp qui sera opérationnalisé par une entreprise de soft\\

{\red EN VUE}\\
\esp rentabiliser l'expérience : \\
\esp STREP pour le chainon manquant entre Knowledge Management
et e-Learning.
}\FIN
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Du QUOI au COMMENT\\
...revenons à la politique scientifique\\~\\}
\Corps{
{\red Conditions nécessaires}\\
\esp Pluri-disciplinarité\\
\esp Applications communes\\
\esp Intégration\\~\\
{\red Les instruments}\\
\esp RTP\\
\esp AS\\
\esp Equipes pluri-laboratoires\\
\esp Instituts sans murs\\
}
\FIN


\FIN
\begin{enumerate}


\Titre{Inference and Learning Team\\~\\
{\small founded by Yves Kodratoff (1980)}}
\Corps{
Main Expertise \\
\centerline{\begin{tabular}{lll}
Machine Learning & since 1980\\
Evolutionary Computation & since 1994\\
Data Mining & since 1995\\
\end{tabular}
}
~\\~\\
Head : Michèle Sebag
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Supervised Machine Learning\\~\\}
\Corps{
Given labelled examples, \\
Find a hypothesis explaining these examples, and/or\\
\esp with low expected error on the application domain.\\~\\

\esp Given $\{ (x_i,y_i), x_i \in X, y_i \in \{-1,1\} \mbox{ or } \RR \}$\bigskip\\
\esp Find $h^* \in H,~ h^* : X \mapsto Y, ~s.t.~ h^*(x_i) = y_i$ \bigskip\\
\esp\esp or $E_{\cal D}[err (h^*)]$ = Arg min \{ $E_{\cal D}[err (h)],~h \in H$ \}
~\\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Supervised Machine Learning, 2\\~\\}
\Corps{

{\red Input: Data}\\
\begin{tabular}{l|ccccc|ccccccccc}
 & x & y & material & stress & aging & {\red Failure} \\ \hline
$Ex_1$ & 3.12 & 1.21 & Ni & 3.21 & Yes & No \\
$Ex_2$ & 2.78 & .78 & Cr & 3.02 & ? & Yes\\
\end{tabular}\\~\\~\\
{\red Output: Hypotheses} : Decision tree, set of rules, neural nets, ...\\


{\red Applications}\\
\esp Failure diagnosis\\
\esp Process control\\
\esp Signal classification \\
\esp ... 
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Settings for Supervised Machine Learning\\~\\}
\Corps{

{\red Binary classification / concept learning}\\
\esp Identification of faulty components in an industrial process\\
\esp Good / Bad driver (for insurance companies)  \\
\esp Cells submitted or not to low radiation exposure (genomics) \\

{\red Multiclass classification}\\
\esp Recognition of visual scenes (for movie databases or military
applications)\\
\esp Recognition of handwritten digits (US Postal)  \\
\esp Speaker identification (security applications) \\

{\red Regression}\\
\esp Prediction of the electrical consumption in 3 hours / 3 days / 3 months)\\
\esp Completion of a robot movement  \\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Evolutionary Computation\\~\\}
\Corps{
Given (an ill-behaved) function $\cal F$ to be optimized\\
\esp on a (complex) search space $\Omega$ \\
Find the optima.\\~\\
{\red Inspiration}: biological evolution, the survival of the fittest.\\~\\
\centerline{\includegraphics[width=10cm]{Figures/EA_tutorial.pdf}}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Evolutionary Computation, 2\\~\\}
\Corps{

\esp Given ${\cal F} : \Omega \mapsto \RR$\\
\esp Find Arg max $\{ {\cal F}(x), ~ x \in \Omega \}$\\ 

{\red Applications}\\
\esp Inverse problems, identification\\
\esp Optimal design  \\
\esp Multi-objective optimization \\
\esp Interactive optimization \\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Data Mining\\~\\}
\Corps{
Given huge amounts of data\\
extract all ``interesting, valid and novel'' regularities.\bigskip\\

\esp Given ${\cal E} = \{ (x_i), x_i \in X \}$\\
\esp Find all $h \ in H$, such that $Constraints(h,{\cal E})$ hold\\
\esp\esp (e.g. frequency, syntactical constraints)\\

{\red Applications}\\
\esp Consumer Relationship Management\\
\esp Insurances, banks, ..\\
\esp Bioinformatics, Numerical engineering,...
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Inference and Learning Team\\~\\}
\Corps{
\centerline{\begin{tabular}{lrl}
{\em\red Permanent members} & \multicolumn{2}{l} {\em\red PhD}  \\
\esp Isabelle Bournaud \esp\esp\esp&\esp & Erick Alphonse\\
\esp Antoine Cornuéjols & \esp  &Ahmed Amrani\\
\esp  Marta Franova & \esp &Jérome Azé\\
\esp Yves Kodratoff & \esp & Chahine Hamila\\
\esp Michèle Sebag & \esp   & Jérome Maloberti (co-adv Einoshin Suzuki)\\
& \esp & Jérémie Mary \\
& \esp & Benoit Mathieu \\
& \esp  &Mohammed Ould Vetah \\
 & \esp  &Mathieu Roche \\
& \esp &Alexandre Termier  \\
\end{tabular}}}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Some references\\~\\}
\Corps{
\begin{enumerate}
\item AI for Numerical Engineering\\
non parametric identification of behavioral laws in mechanics\\
Advances in Genetic Programming, 1996
\item Relational Learning - Inductive Logic Programming \\
winner IJCAI Challenge, Predictive Toxicology Evaluation 97-99
\item Machine Learning and Visual Data Mining\\
Atherosclerosis risk identification,\\
\esp\hfill best contribution to PKDD02 Challenge\\
\end{enumerate}
EU contracts / networks :
{\small \begin{itemize}
\item ML Net, ML Toolbox
\item ILP Net, ILP$^2$
\item EvoNet (migrating from Ecole Polytechnique)
\end{itemize}
}}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Presentation of some\smallskip\\ Scientific and Technological Advances\\~\\}
\Corps{
\begin{enumerate}
\item Exploring the expert's search space\\
{\small better efficiency, comprehensibility}
\item Dealing with multiple objectives\\
{\small experts have conflicting goals}
\item Gradual resolution\\
{\small What if the goal cannot be tackled directly}
\item Optimizing expert's satisfaction : Interactive evolution
\end{enumerate}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Exploring the expert's search space\\
Identification of Behavioral Laws\\}
\Corps{
\vspace{-.5in}
\centerline{\includegraphics[width=8cm]{Figures/expe.pdf}}
\vspace{-1in}
{Input: Experimental curves}\\
\AUT{ Schoenauer, Sebag et al., Advances in GP 96}\\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Identification of Behavioral Laws, 2\\~\\}
\Corps{
{\red Input: Experimental curves}
\begin{itemize}
\item observed strain $\epsilon(t)$ for applied stress $\sigma(t)$;
\item observed stress $\sigma(t)$ for applied strain $\epsilon(t)$;\\
\end{itemize}
{\red Output: Behavioral law}\\
Differential equations
linking $\epsilon(t)$, $\sigma(t)$ and their derivatives, e.g.
\begin{tabbing}
aaaaa\=aaaaaaaaaa\= aaaaaaaaaaaa\= \kill
\> if \> $ \sigma(t) < \sigma_1$ \> then $ \sigma(t) = a.  \epsilon(t) + b. \dot\epsilon(t)$\\
\> else if \> $ \sigma(t) < \sigma_2$ \> then $ \sigma(t) = c.  \epsilon(t) + d. \dot\epsilon(t)$\\~\\
\end{tabbing}
{\red Criteria}: the law must fit the experiments {\mybf and} be comprehensible.
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{ Search space: Rheological models\bigskip\\
Dynamic 1-D laws.~\\}
\Corps{
Assembly in series or parallel of\\
$\bullet$ springs (elastic behavior)\\
$\bullet$ sliders (plastic behavior)\\
$\bullet$ dashpots (viscous behavior)\\

\centerline{
\includegraphics[width=8cm]{GP/polypropylene.png}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Identification Goals\\~\\}
\Corps{
\begin{itemize}
\item For a given model, adjust the parameters \\
{\mybf $\Longrightarrow$ Parametric optimization}\\~\\
\item Optimize both the model and the parameters\\
{\mybf $\Longrightarrow$ Non-parametric optimization}
\end{itemize}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{ Genetic Programming~\\~\\}
\Corps{
$\bullet$ Same principles as GAs:\\
\esp {\small initialization, (selection, crossover, mutation)$\ast$}\\

$\bullet$ Search space = Trees ($\cal N$, $\cal T$) \smallskip\\
\begin{tabular}{ll}
\esp Polynoms & \begin{tabular}{l}
${\cal N} = \{ +, -, * \}$\\
${\cal T} = \{ X_1, X_2, \ldots, X_n, {\cal R} \}$ 
\end{tabular} \smallskip\\
\esp Programs & \begin{tabular}{l}
${\cal N} = \{ $if-then-else, do-while$ \}$\\
${\cal T} = \{ $expressions, instructions$\}$ 
\end{tabular}  \smallskip\\
\end{tabular}\\
\centerline{~}
\centerline{~}
$\bullet$ Fitness : fitting experimental data\\
%\esp {\small inverse problem}\\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{ Non parametric identification with GP\\~\\}
\Corps{

$\bullet$ Rheological models = Trees ($\cal N$, $\cal T$) \smallskip\\
${\cal N} = \{ $//, +$ \}$\hfill ${\cal T} = \{ $springs, sliders, dashpots $\}$ \smallskip\\~\\
\centerline{
\begin{tabular}{cc}
\includegraphics[width=7cm]{GP/polypropylene.png} &
\includegraphics[width=7cm]{GP/arbre_poly.png}
\end{tabular}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{ Non parametric Identification\\~\\ with Background Knowledge\\~\\}
\Corps{
$\bullet$ Macro-Mechanical Identification, Indentation laws
\begin{center}
\begin{tabular}{c c c }
\includegraphics[width=6.5cm]{GP_Grammar/essai2.png} &
\includegraphics[width=5cm]{GP_Grammar/grapheft.png}\\

\end{tabular}\\~\\
\AUT{ Ratle \& Sebag, PPSN 2000, AE 2001}
\end{center}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{GP and Background Knowledge \\ ex: Dimension consistency~\\}
\Corps{
\centerline{meters and seconds should not be added...}
~\\
{\bf Goal}: Enforcing dimension consistency\\

{\bf Assumption}:\\
{\small\bl \begin{tabular}{ll}
 & finite set of units \hfill $\{m, s, kg\}$\\
            & compound units \hfill$U_{ijk} : m^i s^j kg ^k$\\
            & limited combinations \hfill \esp $i,j,k \in [-2,2]$\bigskip\\
\end{tabular}}

{\bf Representation: BNF grammars} \\
{\small \begin{tabular}{llr}
$S$ & start symbol & $U_{1,-2,1}$\\
$N$ & non-terminals & $\{U_{ijk} \}$ \\
$T$ & terminals & $\{ Vars,{\cal R},+,-,*,/,exp\}$ \\
$P$ & production rules\\ 
\end{tabular}}
{\small
\[ \begin{array} {ll}
U_{ijk} := & U_{ijk} + U_{ijk} ~|~ U_{ijk} - U_{ijk} ~|~ U_{ijk} ~exp^{~ U_{000}}\\
            & |_{abc+def = ijk}~ U_{abc}~*~ U_{def} \\
            & |_{abc-def = ijk}~ U_{abc}~/~ U_{def} \\
            & |_{unit(var) = ijk}~ Var \\
            \end{array} \]}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{GP and Background Knowledge, 2}
\Corps{
{\red Conveys expert guesses}\\
\esp Bias on the solution shape through the first production rule
\[ S := U ~* exp^{ - V \times t} \]

{\red Exponential reduction of the search space}
\begin{center}
\begin{tabular}{c}
\includegraphics[width=8cm]{GP_Grammar/space.png}\\
\end{tabular}
\end{center}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Presentation of some\smallskip\\ Scientific and Technological Advances\\~\\}
\Corps{
\begin{enumerate}
\item Exploring the expert's search space\\
{\small better efficiency, comprehensibility}
\item {\red Dealing with multiple objectives}\\
{\small experts have conflicting goals}
\item Gradual resolution\\
{\small What if the goal cannot be tackled directly}
\item Optimizing expert's satisfaction : Interactive evolution
\end{enumerate}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Dealing with Multiple Objectives\\
Application to Optimal Design\\~\\}
\Corps{
\begin{itemize}
\item Find a shape in a given {\myem design domain}
\item of minimal weight
\item with minimal displacement (or strain, or stress) under given loading(s)
\end{itemize}

\centerline{\includegraphics[width=6cm]{Formes/forme_vide.png}}
\AUT{Hatem Schoenauer ACDM 2000, 2002}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Multi-objective optimization}
\Corps{
\begin{tabular}{cc}
\parbox[b]{7.8cm}{
\begin{itemize}
\item {\myem Pareto Front} $\equiv$ \\
\hypertarget{pareto}{~}
\bl\hfill Best compromises
\item No deterministic method
\item Specific {\myem Sélection}
\item $\rightarrow$ One single run {\mybf with niching} samples
the whole Pareto front
\end{itemize}
} &
\fbox{
\parbox[b]{5.5cm}{
\begin{center}
\includegraphics[width=5.5cm]{Figures/pareto_eng.pdf}
\vskip -0.4cm
{\small Pareto front \\ (in {\mybf objective} space)}
\end{center}
}}
\end{tabular}

\vfill

Many applications in  design
\NATE{Eurogen 97-99, ACDM 98-00}

{\mysc A  niche for  EAs !}
\NATE{EMO I mars 2000, EMO II march 2003}

}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Multi-criterion results\\~\\}
\Corps{
\hyperlink{pareto}{Pareto Front} :
Minimize both weight and displacement
\begin{center}

{\tiny
\setlength{\tabcolsep}{1pt}
\begin{tabular}{ccccccccccccccc}
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol01.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol02.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol03.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol04.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol05.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol06.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol07.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol08.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol09.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol10.pdf}\\


$6.45 ~ -$ & $6.61~ 90$ & $6.68~ 88$ & $6.89~ 87$ & $6.93 ~85$
& $7.15 ~75$ &  
        $7.12 ~ 74$ & $8.01 ~ 70$ & $8.16 ~ 61$ & $8.44 ~ 59$  \\  

\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol11.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol11bis.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol12.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol13.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol14.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol15.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol16.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol17.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol18.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol19.pdf} \\


        $9.48 ~ 50$ & $9.96 ~ 47$ & $10.91 ~ 42$ & $12.25 ~ 34$
        & $14.6 ~ 30$ &
 $17.79 ~ 25$ & $18.84 ~ 24$ & $19.69 ~ 23$ &
 $20.2 ~ 22$ & $30.1 ~ 20$ \\

\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol20.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol21.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol22.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol23.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol24.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol25.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol26.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol27.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol28.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol29.pdf}\\
 $45.86 ~ 15$ & $65.85 ~ 14$ &
 $163.9 ~ 12$ & $220.1 ~ 11$ &
        $482 ~ 9$ & $1149 ~ 8$ & $4338 ~ 7$ & $4585 ~ 6$ & $4756 ~ 5.5$ & $\infty ~ 5$
         \end{tabular}
}\\
{\mybf CPU time $\equiv$ single objective!}
\end{center}
\AUT{Hatem, Roudenko, Schoenauer, ACDM 2002}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Presentation of some\smallskip\\ Scientific and Technological Advances\\~\\}
\Corps{
\begin{enumerate}
\item Exploring the expert's search space\\
{\small better efficiency, comprehensibility}
\item Dealing with multiple objectives\\
{\small experts have conflicting goals}
\item {\red Gradual resolution}\\
{\small What if the goal cannot be tackled directly}
\item Optimizing expert's satisfaction : Interactive evolution
\end{enumerate}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Gradual Resolution with Behavioral Memory\\
}
\Corps{
\centerline{\includegraphics[width=3cm]{Legs.pdf}}
~\\
\begin{tabular}{ll}
{\red Goal} & A motion program\smallskip\\
{\red Criterion} & Human-like motion\smallskip\\
{\red Search Space} & Neural Nets : Hips and Knees angles = F(state)
\end{tabular}\\
\AUT{de Garis, ICML 90}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Gradual Resolution, 2\\~\\}
\Corps{
First results : miserable \hfill (crawls rather than walks)\\

{\red Behavioral memory}
\begin{enumerate}
\item Optimize ${\cal F}_1$ : doesn't fall down\\
\item Optimize ${\cal F}_2$ : doesn't fall down and legs are moving\\
\item Optimize ${\cal F}_3$ : doesn't fall down and walks
\end{enumerate}
\centerline{\em stand up before walking...}
~\\
\AUT{Application for constrained optimization: Schoenauer Xanthakis ICGA 93}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Gradual Resolution\\Application to Evolutionary Robotics\\~\\}
\Corps{
\begin{tabular}{ll}
{\red Goal} & An autonomous robot controller\smallskip\\
{\red Criterion} & Scalability\smallskip\\
{\red Search Space} & Neural Nets : Action = F(state)\\~\\
\end{tabular}

Standard Controller : actions = speed left and right motors.
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Symbolic Controllers\\~\\}
\Corps{
\centerline{\includegraphics[width=15cm]{Robot/controller.pdf}}
\AUT{Godzik, Schoenauer, Sebag, EvoRob03}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Gradual Resolution with Symbolic Controllers, 2\\~\\}
\Corps{
 
{\red Scalability - Modularity}\hfill The blind watchmaker, Dawkins 88
\begin{itemize}
\item Elementary controllers : actions = \{ forward, left, right\}\\
\item Controllers( level $t$) : actions = \{ Controllers(level $t-1$) \}\\
\end{itemize}

{\red Robustness - Creativity}
\begin{itemize}
\item Irrelevant actions in the library don't harm 
\item Unexpected selection / combinations of actions 
\end{itemize}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Presentation of some\smallskip\\ Scientific and Technological Advances\\~\\}
\Corps{
\begin{enumerate}
\item Exploring the expert's search space\\
{\small better efficiency, comprehensibility}
\item Dealing with multiple objectives\\
{\small experts have conflicting goals}
\item Gradual resolution\\
{\small What if the goal cannot be tackled directly}
\item {\red Optimizing expert's satisfaction : Interactive evolution}
\end{enumerate}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Optimizing expert's satisfaction\\
}
\Corps{
{\red Search space}~: Coffee mixtures
\NOTE{Real-valued proportion of given blends}

{\red Goal}~: Reach a given flavor
\NOTE{$\cal F$ = consumer opinion}

\begin{center}
\includegraphics[width=5cm]{Figures/tasse}
\end{center}
\AUT{ Herdy,  PPSN96}}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Subjective Optimization\\~\\ The expert in the loop\\~\\}
\Corps{
\begin{itemize}
\item Visual criteria; 
e.g. morphing, feature selection,..
\item Acoustic criteria; e.g.
the sound of the car doors
\item Taste; finding Coca-Cola recipe ? :-)\\
\end{itemize}
\centerline{\red Computer science new challenge: addressing underspecified goals}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Subjectively Optimal Design for Musical Instruments\\
{\small \blue on-going work, coll. Lab. Acoustique Musicale, Paris VI}\\~\\}
\Corps{
\centerline{Mechanical parameters $\longleftrightarrow$ Acoustic signatures $\longleftrightarrow$ Aesthetic judgment}
~\\~\\
{\red What is a good guitar ?} \hfill a deterministic approach\\
\esp interviews of experts (interprets, designers)\\
\esp use composites with desired qualities (e.g. clarity, strength)\\
\esp but after a while, the sound is ``not interesting''...
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Subjectively Optimal Design, 2\\~\\}
\Corps{
{\red Intermediate representation}\\
\esp A sound generator / with variation operators\\
\esp $+$ The expert in the loop\\~\\
\centerline{{\bf Output} : desirable acoustic signatures}
~\\

{\red The inverse problem}\\
\esp from the acoustic signature \\
\esp\hfill to the physical (mechanical, geometric,..) parameters
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{(Tentative) Room for cooperation\\~\\}
\Corps{
{\red Learning based motion}\\
\esp Goal : general collision avoidance\\
\esp Existing approach : manual solution, manual extension on failures.\\
\esp\hfill (convergence ?)\\
\esp Learning approach : inspired from robot controller\\
\esp \hfill (including the design of ``difficult'' situations)\\~\\

{\red Assembly Dis-assembly}\\
\esp same as collision avoidance\\
\esp Note: the learning approach can provide a ``difficulty'' measure.
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{(Tentative) Room for cooperation, 2\\~\\}
\Corps{
{\red Modelling of motion dynamics}\\
\esp A mixture law (body dependent)\\
\esp Criteria: Human-like motion (simplified models)\\

{\red Intuitive tools - 3D sketching}\\
\esp Goal: from the 2D sketch to a faithful 3D form\\
\esp One step further: finding out what the expert meant\\
\esp \hfill {\red ... subjective optimization}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{A Vision for Design\\~\\}
\Corps{
{\red Human-machine cooperation}\\
\esp The human being has preferences\\
\esp The machine explores seamlessly the search space\\
\esp The interaction: biased toward the expert's preferences\\
\esp\hfill but offering ... surprises\\ ~\\
\centerline{\red Controlling the trade off}
~\\
\centerline{\red  EXPLORATION - EXPLOITATION}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{~\\}
\Corps{
\vfill
{\red Time for coffee ?}
\begin{center}
\includegraphics[width=5cm]{Figures/tasse}
\end{center}
\vfill
}
\FIN


\begin{enumerate}
\item Exploring the expert's search space\\
{\small better efficiency, comprehensibility}
\item Dealing with multi-objective optimization\\
{\small experts have conflicting goals}
\item Gradual resolution\\
{\small What if the goal cannot be tackled directly}
\item Optimizing expert's satisfaction : Interactive evolution
\end{enumerate}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Non parametric identification\\
{\small Advances in Genetic Programming (1996)}}
\Corps{
{\mysc Behavioral law of materials}
\begin{itemize}
\item  needed for accurate CAD;
\item  ill-known for new materials (e.g. polymers).
\end{itemize}

{\mysc Art of macro-mechanical modeling}:
\begin{itemize}
\item  Adapting the model of another material;
\item  Designing a brand new model;
\item  Starting with a micro-mechanical analysis.
\end{itemize}

{\mysc Fails when the current material}:
\begin{itemize}
\item  does not resemble other materials;
\item  does not fit expert's guesses;
\item  is not provided a tractable model by $\mu$-M analysis.
\end{itemize}
}
\begin{slide}
%\begin{center}
\centerline{\mytitre Identification of Behavioral Laws}
%\end{center}
\centerline{\includegraphics[width=4cm]{Figures/expe.pdf}}
\vspace{-.2in}
{\mysc Input: Experimental curves}
\begin{itemize}
\item observed strain $\epsilon(t)$ for applied stress $\sigma(t)$;
\item observed stress $\sigma(t)$ for applied strain $\epsilon(t)$;
\end{itemize}
{\mysc Output: Behavioral law}\\
Differential equations
linking $\epsilon(t)$, $\sigma(t)$ and their derivatives, e.g.
\begin{tabbing}
aaaaa\=aaaaaaaaaa\= aaaaaaaaaaaa\= \kill
\> if \> $ \sigma(t) < \sigma_1$ \> then $ \sigma(t) = a.  \epsilon(t) + b. \dot\epsilon(t)$\\
\> else if \> $ \sigma(t) < \sigma_2$ \> then $ \sigma(t) = c.  \epsilon(t) + d. \dot\epsilon(t)$\\
\end{tabbing}
{\mysc Criteria}: the law must fit the experiments {\mybf and} be comprehensible.
\end{slide}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{slide}
\begin{center}
{\mytitre Search space: Rheological models\bigskip\\
Dynamic 1-D laws.}\\
\end{center}
Assembly in series or parallel of\\
$\bullet$ springs (elastic behavior)\\
$\bullet$ sliders (plastic behavior)\\
$\bullet$ dashpots (viscous behavior)\\

\centerline{
\includegraphics[width=8cm]{GP/polypropylene.png}
}
\newslide

{\mysc Identification Goals:}\\
\begin{itemize}
\item For a given model, adjust the parameters \\
{\mybf $\Longrightarrow$ Parametric optimization}
\item Optimize both the model and the parameters\\
{\mybf $\Longrightarrow$ Non-parametric optimization}
\end{itemize}
\end{slide}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{ Genetic Programming~\\~\\}
\Corps{
$\bullet$ Same principles as GAs:\\
\esp {\small initialization, (selection, crossover, mutation)$\ast$}\\

$\bullet$ Search space = Trees ($\cal N$, $\cal T$) \smallskip\\
\begin{tabular}{ll}
\esp Polynoms & \begin{tabular}{l}
${\cal N} = \{ +, -, * \}$\\
${\cal T} = \{ X_1, X_2, \ldots, X_n, {\cal R} \}$ 
\end{tabular} \smallskip\\
\esp Programs & \begin{tabular}{l}
${\cal N} = \{ $if-then-else, do-while$ \}$\\
${\cal T} = \{ $expressions, instructions$\}$ 
\end{tabular}  \smallskip\\
\end{tabular}\\
\centerline{~}
\centerline{~}
$\bullet$ Fitness : fitting experimental data\\
%\esp {\small inverse problem}\\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{ Non parametric identification with GP\\~\\}
\Corps{

$\bullet$ Rheological models = Trees ($\cal N$, $\cal T$) \smallskip\\
${\cal N} = \{ $//, +$ \}$\hfill ${\cal T} = \{ $springs, sliders, dashpots $\}$ \smallskip\\~\\
\centerline{
\begin{tabular}{cc}
\includegraphics[width=7cm]{GP/polypropylene.png} &
\includegraphics[width=7cm]{GP/arbre_poly.png}
\end{tabular}}
}\
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{ Behavioral Law of a Rheological Model\\~\\}
\Corps{
$\bullet$ Resolution of differential equations\smallskip\\
\centerline{
{\begin{tabular}{rl}
  Spring($k$)  &  $ \sigma(t) = k~.~\varepsilon(t) $\smallskip\\
 Dashpot($\eta$) &  $ \sigma(t) = \eta~.~\dot\varepsilon(t) $\smallskip\\
 Slider($\sigma_S$) & $(\dot\varepsilon(t)  = 0) ~OR~ (|\sigma(t)| = \sigma_S)$\smallskip\\
 Series & $\varepsilon_{parent}(t) = \varepsilon_{son_1}(t) + \varepsilon_{son_2}(t)$\\
        & $ \sigma_{parent}(t) = \sigma_{son_1}(t) =  \sigma_{son_2}(t)$\smallskip\\
 Parallel & $\varepsilon_{parent}(t) = \varepsilon_{son_1}(t) = \varepsilon_{son_2}(t)  $\smallskip\\
 &$ \sigma_{parent}(t) = \sigma_{son_1}(t) + \sigma_{son_2}(t)$
\end{tabular}}}

\centerline{~}
\centerline{~}
$\bullet$ Advantage: VERY intelligible\hfill for experts\\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{ Non parametric Identification\\~\\ with Background Knowledge\\~\\}
\Corps{
$\bullet$ Macro-Mechanical Identification, Indentation laws
\begin{center}
\begin{tabular}{c c c }
\includegraphics[width=6.5cm]{GP_Grammar/essai2.png} &
\includegraphics[width=5cm]{GP_Grammar/grapheft.png}\\

\end{tabular}\\~\\
\end{center}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{GP and Background Knowledge \\ ex: Dimension consistency~\\}
\Corps{
\centerline{meters and seconds should not be added...}
~\\
{\bf Goal}: Enforcing dimension consistency\\

{\bf Assumption}:\\
{\small\bl \begin{tabular}{ll}
 & finite set of units \hfill $\{m, s, kg\}$\\
            & compound units \hfill$U_{ijk} : m^i s^j kg ^k$\\
            & limited combinations \hfill \esp $i,j,k \in [-2,2]$\bigskip\\
\end{tabular}}

{\bf Representation: BNF grammars} \\
{\small \begin{tabular}{llr}
$S$ & start symbol & $U_{1,-2,1}$\\
$N$ & non-terminals & $\{U_{ijk} \}$ \\
$T$ & terminals & $\{ Vars,{\cal R},+,-,*,/,exp\}$ \\
$P$ & production rules\\ 
\end{tabular}}
{\small
\[ \begin{array} {ll}
U_{ijk} := & U_{ijk} + U_{ijk} ~|~ U_{ijk} - U_{ijk} ~|~ U_{ijk} ~exp^{~ U_{000}}\\
            & |_{abc+def = ijk}~ U_{abc}~*~ U_{def} \\
            & |_{abc-def = ijk}~ U_{abc}~/~ U_{def} \\
            & |_{unit(var) = ijk}~ Var \\
            \end{array} \]}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{GP and Background Knowledge, 2}
\Corps{
{\red Conveys expert's requirements}\\
\esp $-$ Bias on the solution shape through the first production rule
\[ S := U ~* exp^{ V \times t} \]

{\red Exponential reduction of the search space}
\begin{center}
\begin{tabular}{c}
\includegraphics[width=8cm]{GP_Grammar/space.png}\\
\end{tabular}
\end{center}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Multi-Objective Optimization\\
Application to Optimal Design\\~\\}
\Corps{
\begin{itemize}
\item Find a shape in a given {\myem design domain}
\item of minimal weight
\item with minimal displacement (or strain, or stress) under given loading(s)
\end{itemize}

\centerline{\includegraphics[width=6cm]{Formes/forme_vide.png}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Multi-objective optimization}
\Corps{
\begin{tabular}{cc}
\parbox[b]{7.8cm}{
\begin{itemize}
\item {\myem Pareto Front} $\equiv$ \\
\hypertarget{pareto}{~}
\bl\hfill Best compromises
\item No deterministic method
\item Specific {\myem Sélection}
\item $\rightarrow$ One single run {\mybf with niching} samples
the whole Pareto front
\end{itemize}
} &
\fbox{
\parbox[b]{5.5cm}{
\begin{center}
\includegraphics[width=5.5cm]{Figures/pareto_eng.pdf}
\vskip -0.4cm
{\small Pareto front \\ (in {\mybf objective} space)}
\end{center}
}}
\end{tabular}

\vfill

Many applications in  design
\NATE{Eurogen 97-99, ACDM 98-00}

{\mysc A  niche for  EAs !}
\NATE{EMO I mars 2000, EMO II march 2003}

}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Multi-criterion results\\
H. Hamda et O. Roudenko \\~\\}
\Corps{
\hyperlink{pareto}{Front de Pareto} :
Minimize both weight and displacement
\begin{center}

{\tiny
\setlength{\tabcolsep}{1pt}
\begin{tabular}{ccccccccccccccc}
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol01.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol02.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol03.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol04.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol05.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol06.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol07.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol08.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol09.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol10.pdf}\\


$6.45 ~ -$ & $6.61~ 90$ & $6.68~ 88$ & $6.89~ 87$ & $6.93 ~85$
& $7.15 ~75$ &  
        $7.12 ~ 74$ & $8.01 ~ 70$ & $8.16 ~ 61$ & $8.44 ~ 59$  \\  

\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol11.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol11bis.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol12.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol13.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol14.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol15.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol16.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol17.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol18.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol19.pdf} \\


        $9.48 ~ 50$ & $9.96 ~ 47$ & $10.91 ~ 42$ & $12.25 ~ 34$
        & $14.6 ~ 30$ &
 $17.79 ~ 25$ & $18.84 ~ 24$ & $19.69 ~ 23$ &
 $20.2 ~ 22$ & $30.1 ~ 20$ \\

\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol20.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol21.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol22.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol23.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol24.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol25.pdf} &
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol26.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol27.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol28.pdf}&
\includegraphics[width=1truecm]{Formes/Hatem/Multi/voronoicol29.pdf}\\
 $45.86 ~ 15$ & $65.85 ~ 14$ &
 $163.9 ~ 12$ & $220.1 ~ 11$ &
        $482 ~ 9$ & $1149 ~ 8$ & $4338 ~ 7$ & $4585 ~ 6$ & $4756 ~ 5.5$ & $\infty ~ 5$
         \end{tabular}
}\\
{\mybf CPU time $\equiv$ single objective!}
\end{center}
}
\FIN

$\bullet$ BK Coding \\
\esp $-$  Dimension-consistency  through automatic grammar generation\\~\\
\esp $-$ Bias on the solution shape through the first production rule\\
\centerline{$S := U ~* exp^{ V \times t}$}
}
\FIN
{\red Goal}\\
Behavioral law for 
Central Expertises \\
\centerline{\begin{tabular}{lll}
Machine Learning & since 1980\\
Evolutionary Computation & since 1994\\
Data Mining & since 1995\\
\end{tabular}
}
~\\~\\
Head : Michèle Sebag
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{slide}
\begin{center}
{\Large\blue Inference and Learning Team \\~\\
Laboratoire de Recherches en Informatique\\~\\
Universit\'e Paris-Sud Orsay}\bigskip\\
\end{center}
\end{slide}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Inference and Learning Team\\~\\
{\small founded by Yves Kodratoff (1980)}}
\Corps{
Central Expertises \\
\centerline{\begin{tabular}{lll}
Machine Learning & since 1980\\
Evolutionary Computation & since 1994\\
Data Mining & since 1995\\
\end{tabular}
}
~\\~\\
Head : Michèle Sebag
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Setting\\~\\}
\Corps{
{\red Supervised Machine Learning}\\
Given labelled examples, \\
Find a hypothesis explaining these examples, and/or\\
\esp with low expected error on the application domain.\\~\\

\esp Given $\{ (x_i,y_i), x_i \in X, y_i \in \{-1,1\} \mbox{ or } \RR \}$\bigskip\\
\esp Find $h^* \in H,~ h^* : X \mapsto Y, ~s.t.~ h^*(x_i) = y_i$ \bigskip\\
\esp\esp or $E_{\cal D}[err (h^*)]$ = Arg min \{ $E_{\cal D}[err (h)],~h \in H$ \}
~\\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Supervised Machine Learning\\~\\}
\Corps{

{\red Example}\\
\begin{tabular}{lcccccccccccccc}
 & x & y & material & stress & aging & failure \\
$Ex_1$ & 3.12 & 1.21 & Ni & 3.21 & Yes & No \\
$Ex_2$ & 2.78 & .78 & Cr & 3.02 & ? & Yes\\
\end{tabular}

{\red Applications}\\  
\esp Failure diagnosis\\
\esp Process control\\
\esp Signal classification\\
\esp ...
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Setting, 2\\~\\}
\Corps{
{\red Evolutionary Computation}\\
Given an ill-behaved function $\cal F$ to be optimized\\
mimic biological evolution.\\~\\
\centerline{\includegraphics[width=10cm]{EA_tutorial.pdf}}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Evolutionary Computation, 2\\~\\}
\Corps{

\esp Given ${\cal F} : \Omega \mapsto \RR$\\
\esp Find Arg max $\{ {\cal F}(x), ~ x \in X \}$\\ 

{\red Applications}\\
\esp Inverse problems, identification\\
\esp Optimal design  \\
\esp Multi-objective optimization \\
\esp Interactive optimization \\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Setting, 3\\~\\}
\Corps{
{\red Data Mining}\\
Given huge amounts of data\\
extract all ``interesting, valid and novel'' regularities.\bigskip\\

\esp Given ${\cal E} = \{ (x_i), x_i \in X \}$\\
\esp Find all $h \ in H$, such that $Constraints(h,{\cal E})$ hold\\
\esp\esp (e.g. frequency, syntactical constraints)

{\red Applications}\\
\esp Consumer Relationship Management\\
\esp Insurances, banks, ..
\esp Bioinformatics, Production,...
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Inference and Learning Team\\~\\}
\Corps{
\centerline{\begin{tabular}{lrl}
{\em\red Permanent members} & \multicolumn{2}{l} {\em\red PhD}  \\
\esp Isabelle Bournaud \esp\esp\esp&\esp & Erick Alphonse\\
\esp Antoine Cornuéjols & \esp  &Ahmed Amrani\\
\esp  Marta Franova & \esp &Jérome Azé\\
\esp Yves Kodratoff & \esp & Chahine Hamila\\
\esp Michèle Sebag & \esp   & Jérome Maloberti (Einoshin Suzuki)\\
& \esp & Jérémie Mary \\
& \esp & Benoit Mathieu \\
& \esp  &Mohammed Ould Vetah \\
 & \esp  &Mathieu Roche \\
& \esp &Alexandre Termier  \\
\end{tabular}}}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Some realizations\\~\\}
\Corps{
\begin{enumerate}
\item AI for Numerical Engineering\\
non parametric identification of behavioral laws in mechanics\\
Advances in Genetic Programming, 1996
\item Relational Learning - Inductive Logic Programming \\
winner IJCAI Challenge, Predictive Toxicology Evaluation 97-99
\item Machine Learning and Visual Data Mining\\
Atherosclerosis risk identification, best contribution to PKDD02 Challenge\\
\end{enumerate}
EU contracts / networks :
{\small \begin{itemize}
\item ML Net
\item ILP Net, ILP$^2$
\item EvoNet
\end{itemize}
}}
\FIN

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Axe 1~: Fouille de Données\\Extraction de Connaissances\\~\\}
%\\
%{\small (AS Fouille de Bases de Données)}}
\Corps{
%{\red Id\'eal : Le si\`ecle des connaissances}\smallskip\\
\hspace{-.2in}\begin{tabular}{ll}
L'id\'eal : & le si\`ecle des connaissances\smallskip\\
La pratique : & connaissances morcelées en expertises spécialisées\\
& à l'état de traces dans les données\\
\end{tabular}\bigskip\\
 $\Longrightarrow$ {\red L'objectif : restituer à l'expert}\\
\centerline{\red des connaissances nouvelles, utiles, valides}
~\\

{\red Particularités} \\
\esp nécessité de collaboration pluri-disciplinaire (BD, Stat, IHM)\\
\esp passage à l'échelle\\
\esp évaluation subjective, processus cyclique\\
\esp enjeux de société
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Fouille de Données Médicales\\~\\}
\Corps{
$\bullet$ Prévention des maladies cardio-vasculaires\\
\esp\hfill {\red Coll. Hopital Broussais}\\~\\
$\bullet$ Le phénomène des réadmissions hospitalières\\
\esp\hfill {\red Coll. Hopital Universitaire de Genève}\\~\\
$\bullet$ Analyse de l'athérosclérose\\
\esp\hfill {\red Challenge ECML-PKDD-02}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Analyse de l'athérosclérose\\{\small Challenge ECML-PKDD-02}\\~\\}
\Corps{
$\bullet$ Redescription des données\\
$\bullet$ Objectif exploratoire\\
$\bullet$ De la classification à la régression\\~\\
{\red Visualisation des risques}\\
\begin{center}
\vspace{-.8in} \begin{tabular}{cc}
{\includegraphics[width=8cm,height=7.5cm]{pp2.pdf}} &
{\includegraphics[width=8cm,height=7.5cm]{Alcohol_pp2.pdf}}\\
\vspace{-1in} Tabac & Alcool\\
\end{tabular}
\end{center}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Axe 2~: Programmation Logique Inductive\\}
%\\
\Corps{
Apprentissage \hfill $\bigcap$ \hfill Programmation Logique\\

{\red Entrées} Données structurées\\
\esp \hfill {\red Chimie moléculaire, PTE Challenge IJCAI 97}\\
\esp \hfill {\red Analyse syntaxique}\\

{\red Difficulté}~: la barrière de la Transition de Phase \hfill Jonction CSP
\begin{center}
\includegraphics[width=8cm]{t10p.png}
\end{center}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Programmation Logique Inductive}
%\\
\Corps{
\begin{center}
Carte de compétence de FOIL\\
\includegraphics[width=7cm]{problems.png}
\end{center}
\esp\hfill Coll. Saitta-Giordana, Italie\\
{\red Avancées}~\\
\esp Appariement stochastique\\
\esp Propositionnalisation\\
\esp Satisfaction de Contraintes \hfill Coll. Suzuki, Japon\\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Axe 3~: Fouille de textes\\~\\}
\Corps{
{\red Entrées} Données semi-structurées : les textes\\

{\red Objectifs} ~: Recherche d'Information, Résumer, ..\\

{\red Difficultés}~: synonymie, polysémie.\\
 
{\red Etape clé}~: construction d'ontologie
\esp\hfill {\small définir généralité, similarité,...}\\

\centerline{\red Mais pas d'ontologie universelle !}
~\\
$\rightarrow$ Ontologies spécifiques d'un domaine.
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Aide à la construction d'Ontologies\\{\small Système Rowan}}
\Corps{
\esp 1/ Acquisition manuelle d'une masse critique de concepts\\
\esp\hfill 20 concepts, 100 schémas d'instantiation\\~\\
\esp 2/ Interface graphique pour l'aide à l'acquisition\\
\esp\hfill 20 + 10 concepts, 1000 schémas\\~\\
\esp 3/ Induction : graphe de concepts\\
\esp\hfill ++ signatures grammaticales.\\~\\

{\red Applications}~:\\
\esp  Bioinfo \hfill Coll IMG\\
\esp  Ressources humaines \hfill Coll.  VediorBis, PerformanSe\\
\esp Stylistique \\
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{\vspace{-1in}}
\Corps{
\hspace{-.5in}

\includegraphics[width=17cm,height=12cm]{rowan_poster.png}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Axe 4~: Optimisation\smallskip\\
{\large Algorithmes génétiques, Programmation génétique}\\~\\}
\Corps{
\blue {\red Motivations} \\
\esp  l'apprentissage est un pb d'optimisation\\
\esp la résolution de l'apprentissage conduit à l'optimisation\\ 

{\red Particularités}\\
\esp problèmes mal posés\\
\esp espaces de recherche trop vastes\\
\esp besoin de connaissances du domaine.
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Identification non paramétrique\\~\\}
\Corps{
Représentation conforme à l'expertise\\
\esp\hfill $\nearrow$ faisabilité, satisfaction\\

Ex. 1, Modèles rhéologiques\\

\centerline{\includegraphics[width=7cm]{polypropylene.png}
~~\includegraphics[width=7cm]{arbre_poly.png}}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Identification non paramétrique}
\Corps{
Ex. 2 Lois dimensionnelles\\
\centerline{\begin{tabular}{c}
\includegraphics[width=4cm]{essai2.png}
\end{tabular}
\esp
{\small
\begin{tabular}{|l|c|c|c|}
\hline
\multicolumn{4}{|c|}{Physical units}\\
\hline
 & mass & length & time \\
\hline
\multicolumn{4}{|c|}{\it Variables} \\
\hline
$E$ (Force)                     &  +1 &  +1 & --2  \\
$K$ (Elastic elements)          &  +1 &   0 & --2  \\
$C$ (Viscous elements)          &  +1 &   0 & --1  \\
$t$ (time)                      &   0 &   0 &  +1  \\
\hline
\multicolumn{4}{|c|}{\it Solution} \\
\hline
$u$ (displacement)              &   0 &  +1 &   0 \\
\hline
\end{tabular}}}~\\~\\~\\
\centerline{\red\bf Programmation Génétique + Grammaires Stochastiques}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Développements Futurs\\~\\~\\}
\Corps{
COUPLAGE \\
\centerline{\large\red\bf Apprentissage - Optimisation  }
~\\~\\
APPLICATIONS EMERGENTES : 
\begin{center}{\red
 Assistants de Modélisation\\
 Robotique
}
\end{center}
}
\end{document}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Axe 3~: Optimisation\smallskip\\
{\large Algorithmes génétiques, Programmation génétique}\\}
\Corps{
\blue {\red Pourquoi} : apprentissage $\equiv$ pb d'optimisation mal posé\\

\blue {\red Comment} : prise en compte des connaissances du domaine\\
\esp\hfill Programmation génétique + grammaires\\

\blue {\red Intérêt} : meilleures satisfaction + faisabilité\\

{\red Applications} : Identification non paramétrique,\\
\esp\esp\esp identification
de lois dimensionnelles en mécanique \\
\centerline{\includegraphics[width=7cm]{polypropylene.png}
~~\includegraphics[width=7cm]{arbre_poly.png}}

}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{Axe 4~: Mesures de qualité\\}
\Corps{

{\red Difficultés}~: \\
\esp approche statistique, comportement asymptotique\\
\esp approche subjective, courbe en cloche\\ 
\esp\hfill {\small ({\red ce qui est très général ou très rare sont rarement intéressants...})}

{\red Objectif}~: \\
\esp Trouver des régularités {\em assez} rares\\
\esp ... et filtrer le bruit\\

{\red Application}~: \\
\esp Identification de la ``zone grise''\hfill {\red Hôpital Broussais}\\

{\red A terme}~:\\
\esp Optimisation multi-critères\\

}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\Titre{INFERENCE ET APPRENTISSAGE}
\Corps{
\include{conclut_plus}
\esp {\red STIC} {Action Spécifique 20 : Fouille de Bases de Données}\\
\centerline{\red GafoDonnées}
~\\
\esp {\red Réseaux Européens} : KDNet, ILPNet, EvoNet
}
\FIN
