Franck Dernoncourt's home page

franck.dernoncourt@gmail.com
Skype: franck.dernoncourt
+33 (0)601 188 949

Publications

1	The medial Reticular Formation (mRF): a neural substrate for action selection? An evaluation via evolutionary computation. [ENG/FR]	2011
2	Fuzzy logic: introducing human reasoning within decision support systems? [ENG/FR]	2011
3	Fuzzy logic: between human reasoning and artificial intelligence [ENG/FR]	2011
4	Presentation on the Motion-Induced Blindness (MIB) phenomenom [ENG/FR]	2011
5	Presentation on the paper Automated Variable Weighting in k-Means Type Clustering [ENG/FR]	2010
6	Prediction of the water inflow to a lake [FR]	2010

The medial Reticular Formation (mRF): a neural substrate for action selection? An evaluation via evolutionary computation. [ENG/FR]

The medial Reticular Formation (mRF) is located in the brainstem: it receives many sensory inputs and it can control motor actions through its projections on the spinal cord and cranial nerves. The mRF is phylogenetically one of the oldest neural structures of the brainstem, the latter being regarded as one of the oldest centers of the central nervous system. Subsequently it seems to be a low-level system for action selection.

The first model of the mRF was proposed by Kilmer and McCulloch in 1969, who already proposed that the mRF could be a "mode selector". In 2005, Humphries et al. (2005) tested the efficiency of this model in the minimal survival task defined in Girard et al. (2003). It performed poorly, but another version of it that included artificially evolved weights performed quite honorably. As a result, Humphries proposed a second model of the mRF, based on neural network formalism and taking into account new anatomical data. Nevertheless, it showed poor performances in the minimal survival task and turns out not to be anatomically very plausible.

In this Master's Thesis, we propose a new model of the mRF:

constrained by anatomical information about its structure,
constructed based on neural networks generated by artificial evolution,
assessed on tasks of action selection.

The model we obtain successfully manages the tasks of selection, indicating that the mRF can be used as an action selection system. We also demonstrate an anatomical property of the mRF, which coupled with the results of the paper Humphries et al. (2006) shows that it is very likely that the mRF network has a small-world structure.

This project was funded by the ANR (ANR-09-EMER-005-01. ANR = French National Agency for Research) in the project EvoNeuro.

Fuzzy logic: introducing human reasoning within decision support systems? [ENG/FR]

Fuzzy logic is based on solid mathematical foundations, including the mathematical theory of fuzzy sets, generalizing classical set theory. Firstly, we define fuzzy operators, which generalize operators of classical logic.

As a second step, we see how fuzzy logic can imitate human reasoning. We analyze the contribution of fuzzy logic for the modeling of human reasoning, and also experimentally investigate whether the decisions taken by humans correspond to decisions taken by fuzzy systems. To this end, given that the literature is deficient on this point, we design an experiment for that purpose and analyze the results.

We will then study the potential applications for databases and decision support systems in Chapter 5. How to integrate the advantages of fuzzy logic in the database? To which extent decision-making systems can use the flexibility of fuzzy logic?

We then study the potential applications for decision support systems and databases.

We show that at the heart of the company, bringing together all the interesting information from the operational databases, decision systems could benefit greatly from fuzzy logic by giving the keys to human reasoning, allowing to refine the decision-making.

Database theorists know what fuzzy logic could bring them in terms of information modeling: queries more intuitive and more powerful on the one hand, the data more consistent with the reality on the other. Many papers have been written, but few significant achievements have followed. The lack of consensus on a standard is probably the main reason behind.

Fuzzy logic: between human reasoning and artificial intelligence [ENG/FR]

Fuzzy logic is an extension of Boolean logic by Lotfi Zadeh in 1965 based on the mathematical theory of fuzzy sets, which is a generalization of classical set theory. By introducing the concept of degree in the verification of a condition, allowing a condition of being in a state other than true or false, fuzzy logic provides a very valuable flexibility to use reasoning, which makes it possible taking into account the inaccuracies and uncertainties. One of the advantages of fuzzy logic to formalize human reasoning is that the rules are set in natural language.

In this report, we will:

introduce the basic concepts of fuzzy logic,
propose some arguments which support the view that fuzzy logic can model human reasoning better than standard logic and probability theory,
conduct an psychological experiment on humans to see if their way of thinking can be reflected by fuzzy logic.

We show that fuzzy logic can explain many experiments that had undermined traditional models of human reasoning in the 20th century. We show how the non-additivity of probability judgments can be expressed in a fuzzy system. We then confront fuzzy logic with some paradoxes of classical logic when it tries to model human reasoning: the sorites paradox is typically the kind of threshold problem that fuzzy logic reduces and the paradox of entailment does not pose a problem in fuzzy logic. It would be interesting to further explore Hempel's paradox and especially how we could express it in a neuro-fuzzy system. Similarly, Wason selection task would require further analysis, this time by focusing on fuzzy modus ponens and modus tollens.

Thus fuzzy logic appears as a powerful theoretical framework for studying human reasoning. Surprisingly, we find only one study comparing the decisions made by humans with that of a fuzzy system, whose purpose was essentially to design a system of decision support for medical personnel, not analyze human reasoning as such. We conduct our own experiment and investigate whether a fuzzy system could mimic the results observed in humans. For this purpose, we use a technique for optimizing fuzzy system using neural networks (neuro-fuzzy), through which we obtain good results, although the correlation between the two criteria for entry is high: a fuzzy system gives results closer to experimental values than those obtained by a polynomial system. This result reinforces the hypothesis that fuzzy logic can be used to explain decisions from human reasoning.

Presentation on the Motion-Induced Blindness (MIB) phenomenom [ENG/FR]

The visual system has a number a 'bugs', some of which we call illusions. Motion-induced blindness (MIB) belongs to a very interesting class of illusions in which objects in plain sight just disappear from phenomenal perception. Other classical examples of disappearance illusions are:

Binocular rivalry, in which two very different objects are presented to the two eyes, and at any given moment one of the obects--or most of it--remains invisible,
Backward masking, in which a stimulus is 'erased' from perception by a second stimulus, called a "mask", presented a brief time later,
Troxler fading, in which a low-contrast object may fade from visual perception after some time.

In addition, a number of neurological conditions usually involving lesions in parietal cortex, such as hemineglect and extinction, lead to cases in which objects in plain view are not seen, or not noticed. For a good review of these phenomena, see article "Psychophysical magic" by Kim and Blake (2005).

Motion-induced blindness MIB is a recently discovered and quite spectacular example of a disappearance illusion. The stimulus consists of a field of small objects, moving in a coherent way (either a 2D or 3D rotation, for example). Superimposed on this moving field is a number of high-contrast stationary objects. When most observers fixate a stationary point in this stimulus (such as one of the high-contrast objects, or a fixation point), after several seconds one or more of the stationary objects just disappear.

Activate full-screen, fix the white point in the center. After a few seconds, you will notice that the yellow point seems to disappear.

Presentation on the paper Automated Variable Weighting in k-Means Type Clustering [ENG/FR]

Abstract of the original paper: This paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed. The convergency theorem of the new clustering process is given. The variable weights produced by the algorithm measure the importance of variables in clustering and can be used in variable selection in data mining applications where large and complex real data are often involved. Experimental results on both synthetic and real data have shown that the new algorithm outperformed the standard k-means type algorithms in recovering clusters in data.

Prediction of the water inflow to a lake [FR]

The purpose of this project is to predict the water inflow to a lake, the Lac St-Jean, based on the evolution of the inflow to the lake from the history of this flow, snowmelt and precipitation in the watershed. All the data for this work have already been collected: our work aims to process, analyze and use these data to build a model which should be able to accurately predict the lake's water inflow.

In the first part, we conduct a preliminary study of the data so as to extract general information. In the second part, we establish a classification of the data to see the main trends. In the third and last part, we build several models to predict and we evaluate them through quality measurements.