Useful information and questionnaires

A questionnaire is an inquiry process, using a probabilistic latticoïd. We suppose that a positive valuation, called an utility, characterizes every terminal vertex. The useful information and the useful routing length of a questionnaire have been introduced in a particular case. We propose, in this article, to define and to study these quantities in the general case, and to exhibit some properties relative to a product of questionnaires, corresponding to dependent or independent processes

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

Characterizing player’s experience from physiological signals using fuzzy decision trees

Author manuscript, published in "IEEE Conference on Computational Intelligence and Games (CIG) 2010, Copenhagen : Denmark (2010)"In the recent years video games have enjoyed a dramatic increase in popularity, the growing market being echoed by a genuine interest in the academic field. With this flourishing technological and theoretical efforts, there is need to develop new evaluative methodologies for acknowledging the various aspects of the player’s subjective experience, and especially the emotional aspect. In this study, we addressed the possibility of developing a model for assessing the player’s enjoyment (amusement) with respect to challenge in an action game. Our aim was to explore the viability of a generic model for assessing emotional experience during gameplay from physiological signals. In particular, we propose an approach to characterize the player’s subjective experience in different psychological levels of enjoyment from physiological signals using fuzzy decision trees.In the recent years video games have enjoyed a dramatic increase in popularity, the growing market being echoed by a genuine interest in the academic field. With this flourishing technological and theoretical efforts, there is need to develop new evaluative methodologies for acknowledging the various aspects of the player’s subjective experience, and especially the emotional aspect. In this study, we addressed the possibility of developing a model for assessing the player’s enjoyment (amusement) with respect to challenge in an action game. Our aim was to explore the viability of a generic model for assessing emotional experience during gameplay from physiological signals. In particular, we propose an approach to characterize the player’s subjective experience in different psychological levels of enjoyment from physiological signals using fuzzy decision trees

A Measurement Theory Characterization of a Class of Dissimilarity Measures for Fuzzy Description Profiles

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ASSESSING GAMEPLAY EMOTIONS FROM PHYSIOLOGICAL SIGNALS: A FUZZY DECISION TREES BASED MODEL

International audienceAs 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 episode

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

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