Declarative modeling based on knowledge

Les nouvelles technologies de l'image 3D permettent la création de mondes virtuels et des créatures qui les peuplent avec un tel niveau de détails, que pour les effets spéciaux de cinéma, il est difficile de distinguer les éléments sont générés par ordinateur. Cependant, cette technologie est dans les mains habiles de designers, artistes et programmeurs, pour lesquels il faut des semaines à plusieurs années pour se former aux outils et obtenir ces résultats. La Modélisation Déclarative est une méthode qui permet de créer des modèles en fournissant les propriétés donnant la description des composants du modèle. Appliquée à l’infographie, la modélisation déclarative est utilisée pour générer le monde virtuel, en déterminant le contexte nécessaire à l'animation et à la conception de la scène, en calculant la position de chaque objet relativement aux relations spatiales, et en générant le rendu de la scène, utilisé par une système d'animation et de visualisation. Ce mémoire présente les travaux de recherche consacrés à l'utilisation de la modélisation déclarative pour créer des environnements virtuels, en tirant partie des connaissances sur le contexte de la scène. Les connaissances sont utilisées afin de faciliter la tâche de description, en automatisant ce qui peut être déduit, comme les usages et les fonctionnalités habituelles. Elles sont également fondamentales pour que le résultat produit corresponde le mieux possible à ce qui est attendu par le concepteur à partir de la description fournie. Les connaissances sont enfin nécessaires pour faciliter la transition entre le modèle de données et l'architecture qui aura la charge d'animer et de faire évoluer la scène.Modern technology has allowed the creation and presentation or VirtualWorlds and creatures with such a high level of detail, that when used in movies, sometimes it is difficult to tell which elements arecomputer-generated and which not. Also, video-games had reached a level close to photographicrealism. However, such technology is at the hands of skillful designer, artists, and programmers, for whom ittakes from weeks to years to complete these results.Declarative modeling is a method which allows to create models specifying just a few properties for the model’s components. Applied to VW creation, declarative modeling can be used to construct theVW, establishing the layout for the objects, generating the necessary context to provide animation and scene design, and generate the outputs used by a visualization/animation system.This document present a research devoted to explore the use of declarative modeling to create VirtualEnvironments, using knowledge exploitation to support the process and ease the transition from the data model to an underlaying architecture which take the task of animating and evolving the scene

MPMQA: Multimodal Question Answering on Product Manuals

Visual contents, such as illustrations and images, play a big role in product manual understanding. Existing Product Manual Question Answering (PMQA) datasets tend to ignore visual contents and only retain textual parts. In this work, to emphasize the importance of multimodal contents, we propose a Multimodal Product Manual Question Answering (MPMQA) task. For each question, MPMQA requires the model not only to process multimodal contents but also to provide multimodal answers. To support MPMQA, a large-scale dataset PM209 is constructed with human annotations, which contains 209 product manuals from 27 well-known consumer electronic brands. Human annotations include 6 types of semantic regions for manual contents and 22,021 pairs of question and answer. Especially, each answer consists of a textual sentence and related visual regions from manuals. Taking into account the length of product manuals and the fact that a question is always related to a small number of pages, MPMQA can be naturally split into two subtasks: retrieving most related pages and then generating multimodal answers. We further propose a unified model that can perform these two subtasks all together and achieve comparable performance with multiple task-specific models. The PM209 dataset is available at https://github.com/AIM3-RUC/MPMQA

The evolution of grounded spatial language

This book presents groundbreaking robotic experiments on how and why spatial language evolves. It provides detailed explanations of the origins of spatial conceptualization strategies, spatial categories, landmark systems and spatial grammar by tracing the interplay of environmental conditions, communicative and cognitive pressures. The experiments discussed in this book go far beyond previous approaches in grounded language evolution. For the first time, agents can evolve not only particular lexical systems but also evolve complex conceptualization strategies underlying the emergence of category systems and compositional semantics. Moreover, many issues in cognitive science, ranging from perception and conceptualization to language processing, had to be dealt with to instantiate these experiments, so that this book contributes not only to the study of language evolution but to the investigation of the cognitive bases of spatial language as well

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming