Automated reasoning with uncertainties

In this work we assume that uncertainty is a multifaceted concept which admits several different measures, and present a system for automated reasoning with multiple representations of uncertainty. Our focus is on problems which present more than one of these facets and therefore in which a multivalued representation of uncertainty and the study of its possibility of computational realisation are important for designing and implementing knowledge-based systems. We present a case study on developing a computational language for reasoning with uncertainty, starting with a semantically sound and computationally tractable language and gradually extending it with specialised syntactic constructs to represent measures of uncertainty, preserving its unambiguous semantic characterisation and computability properties. Our initial language is the language of normal clauses with SLDNF as the inference rule, and we select three facets of uncertainty, which are not exhaustive but cover many situations found in practical problems: vagueness, statistics and degrees of belief. To each of these facets we associate a specific measure: fuzzy measures to vagueness, probabilities on the domain to statistics and probabilities on possible worlds to degrees of belief. The resulting language is semantically sound and computationally tractable, and admits relatively efficient implementations employing ff \Gamma fi pruning and caching

Performance in dynamic decision-making : the impact of different types of temporal uncertainty

Bien que certaines études se soient intéressées aux tâches dynamiques ou a différentes situations d 'incertitude, aucune n 'a porte particulièrement sur l'influence de différents types d'incertitude sur la prise de décision dynamique. Deux types d'incertitude sont comparés : Incertitude dans la connaissance (KU), soit la variabilité de l'information en lien avec une situation, et Incertitude dans les données (DU), soit la nature plus ou moins complète de cette information. L 'objectif de la présente étude est d'établir l'existence empirique de ces types d'incertitude dans la sphère temporelle et d'évaluer leur impact sur la performance. La tâche dynamique utilisée est le jeu informatise « Save the Whale » (Porter, 1991) dans lequel l'incertitude se situe dans l'occurrence des évènements critiques. Les résultats montrent que les participants confrontes uniquement a KU obtiennent une meilleure performance et que pour l'ensemble des variables et des conditions expérimentales, la performance s'améliore avec la pratique indépendamment du niveau et du type d'incertitude. Ceci supporte l'existence empirique de différents types d 'incertitude temporelle, KU et DU, ayant un impact distinct et additif sur la performance en prise de décision dynamique

Automated Reasoning with Uncertainties

In this work we assume that uncertainty is a multifaceted concept and present a system for automated reasoning with multiple representations of uncertainty. We present a case study on developing a computational language for reasoning with uncertainty, starting with a semantically sound and computationally tractable language and gradually extending it with specialised syntactic constructs to represent measures of uncertainty, preserving its unambiguous semantic characterisation and computability properties. Our initial language is the language of normal clauses with SLDNF as the inference rule, and we select three specific facets of uncertainty for our study: vagueness, statistics and degrees of belief. The resulting language is semantically sound and computationally tractable. It also admits relatively efficient implementations employing ff \Gamma fi pruning and caching