For a Data-Driven Interpretation of Rules wrt GMP Conclusions in Abductive Problems

International audienceAbductive reasoning is an explanatory process in which potential causes of an observation are unearthed. In its classical – crisp – version it offers little lattitude for discovery of new knowledge. Placed in a fuzzy context, abduction can explain observations which did not, originally, exactly match the expected conclusions. Studying the effects of slight modifications through the use of linguistic modifiers was, therefore , of interest in order to describe the extent to which observations can be modified yet still explained and, possibly, create new knowledge. We will concentrate on the formal definition of fuzzy abduction given by Mellouli and Bouchon-Meunier. Our results will be shown to be incompatible with established theories. We will show where this incompatibility comes from and derive from it a selection of fuzzy implication , based on observable data

Vers une classification de problèmes abductifs en fonction d'observations possibles

National audienceIn a context where all knowledge is given by rules and the only observable data lies in the solution space, inferring potential explanations for a given observation is not an easy task, even if the observation is close to the expected conclusion. This is why we originally considered the impact of hedges on observations in abductive reasoning. Extending a formal definition of fuzzy abduction given by Mellouli and Bouchon-Meunier, we show that some modifiers are inexplicable for a given implication. Instead of reconsidering our original rule as being incompatible with the data, we choose to question the selection of fuzzy implication. Indeed, we will show that possible conclusions are dependent on the implication operator, as is the semantic interpretation of the associated rules.Dans un contexte où la connaissance émane de règles et que les seules observations possibles proviennent de l’espace des conclusions, l’inf́erence d’explications potentielles n’est pas aisée. Ceci explique pourquoi nous nous sommes intéressés aux modificateurs linguistiques dans l’abduction floue. En étendant des résultats de Mellouli et Bouchon-Meunier, nous montrons que des modificateurs sont inexplicables avec certaines implications. Au lieu de revoir notre règle originelle comme incompatible avec les données, nous questionnons le choix de l’opérateur d’implication. Nous montrerons que les conclusions acceptables dépendent de l’implication, comme l’interprétation sémantique des règles correspondantes

Incertain et inconnu, deux facettes de la cotation

International audienceLa génération automatique de connaissances s'assortit généralement d'une mesure de confiance. Les systèmes d'apprentissagé evaluent leurs performances en fonction des standards et de leurs pertinences. Les outils de recherche d'informations classent leurs résultats selon diverses stratégies, en fonction du contexte d'utilisation. Cette nécessiténécessité´nécessitéémane autant des algorithmes, des modèles que des faits eux-mêmes. Cependant, la majorité des degrés de confiance affectésaffectés`affectésà des informa-tions le sont demanì ere globale. Nous percevons la cotation comme la projection de différentes dimensions d'in-certitude ou d'imperfections sur la donnée elle-même. PourêtrePourˆPourêtre utile, la cotation doitêtredoitˆdoitêtre compréhensible. Nous nous proposons donc de focaliser notre attention sur la représentation de la cotation. Pour la favoriser, nous proposons de distinguer la cotation indéterminée de sonévaluationson´sonévaluation impossible. Mots-clés : Cotation d'information, incertitude, logique multivalente, ´ evaluation impossible

Assessing Gameplay Emotions from physiological signals: a fuzzy decision trees based model

Paper presented at INTERNATIONAL CONFERENCE ON KANSEI ENGINEERING AND EMOTION RESEARCH 2010, KEER2010, PARIS | MARCH 2-4 2010As video games become a widespread form of entertainment, there is need to develop new evaluative methodologies for acknowledging the various aspects of the player’s subjective experience, and especially the emotional aspect. Video game developers could benefit from being aware of how the player reacts emotionally to specific game parameters. In this study, we addressed the possibility to record physiological measures on players involved in an action game, with the main objective of developing adequate models to describe emotional states. Our goal was to estimate the emotional state of the player from physiological signals so as to relate these variations of the autonomic nervous system to the specific game narratives. To achieve this, we developed a fuzzy set theory based model to recognize various episodes of the game from the user’s physiological signals. We used fuzzy decision trees to generate the rules that map these signals to game episodes characterized by a variation of challenge at stake. A specific advantage to our approach is that we automatically recognize game episodes from physiological signals with explicitly defined rules relating the signals to episodes in a continuous scale. We compare our results with the actual game statistics information associated with the game episodes.As video games become a widespread form of entertainment, there is need to develop new evaluative methodologies for acknowledging the various aspects of the player’s subjective experience, and especially the emotional aspect. Video game developers could benefit from being aware of how the player reacts emotionally to specific game parameters. In this study, we addressed the possibility to record physiological measures on players involved in an action game, with the main objective of developing adequate models to describe emotional states. Our goal was to estimate the emotional state of the player from physiological signals so as to relate these variations of the autonomic nervous system to the specific game narratives. To achieve this, we developed a fuzzy set theory based model to recognize various episodes of the game from the user’s physiological signals. We used fuzzy decision trees to generate the rules that map these signals to game episodes characterized by a variation of challenge at stake. A specific advantage to our approach is that we automatically recognize game episodes from physiological signals with explicitly defined rules relating the signals to episodes in a continuous scale. We compare our results with the actual game statistics information associated with the game episodes

Twelve numerical, symbolic and hybrid supervised classification methods

International audienceSupervised classification has already been the subject of numerous studies in the fields of Statistics, Pattern Recognition and Artificial Intelligence under various appellations which include discriminant analysis, discrimination and concept learning. Many practical applications relating to this field have been developed. New methods have appeared in recent years, due to developments concerning Neural Networks and Machine Learning. These "hybrid" approaches share one common factor in that they combine symbolic and numerical aspects. The former are characterized by the representation of knowledge, the latter by the introduction of frequencies and probabilistic criteria. In the present study, we shall present a certain number of hybrid methods, conceived (or improved) by members of the SYMENU research group. These methods issue mainly from Machine Learning and from research on Classification Trees done in Statistics, and they may also be qualified as "rule-based". They shall be compared with other more classical approaches. This comparison will be based on a detailed description of each of the twelve methods envisaged, and on the results obtained concerning the "Waveform Recognition Problem" proposed by Breiman et al which is difficult for rule based approaches

Similarity, analogy and case-based reasoning

International audienc