Computer-aided architectural design (CAAD) programs represent architectural design at a low level of spatial abstraction. While this representation model allows CAAD programs to capture the precise spatial characteristics of a design, it means that CAAD programs lack the underlying computational apparatus necessary to reason about design at a conceptual level. This thesis is a first step towards building a framework that bridges the gap between the conceptual aspects of a design and its low level CAAD-based spatial representation. Specifically, this thesis presents a new framework, referred to as the Conceptual Requirements Reasoner (CRR), which provides an architect with a framework to validate conceptual design requirements. The CRR will demonstrate how qualitative spatial representation and reasoning techniques can be used as the link between a design's conceptual requirements and its underlying quantitative spatial representation. A museum case study is presented to demonstrate the application of the CRR in a real world design context. It introduces a set of museum design requirements identified in research and shows how these requirements can be validated using the CRR. The results of the case study shows that the CRR is an effective tool for conceptual requirements reasoning.

<p>Real-time strategy (RTS) games constitute one of the largest game genres today and have done so for the past decade. A central feature of real-time strategy games is opponent AI which is suggestively the “last frontier” of game development because the focus of research has primarily been on other components, graphics in particular. This has led to AI research being largely ignored within the commercial game industry but several methods have recently been suggested for improving the strategic ability of AI agents in real-time strategy games.</p><p>The aim of this project is to evaluate how a method called qualitative spatial reasoning can improve AI on a tactical level in a selected RTS game. An implementation of an AI agent that uses qualitative spatial reasoning has been obtained and an evaluation of its performance in an RTS game example monitored and analysed.</p><p>The study has shown that qualitative spatial reasoning affects AI agent’s behaviour significantly and indicates that it can be used to deduce a rule-base that increases the unpredictability and performance of the agent.</p>

Autonomous systems require a lot of information about the environment in which they operate in order to perform different high-level tasks. The information is made available through various sources, such as remote and on-board sensors, databases, GIS, the Internet, etc. The sensory input especially is incrementally available to the systems and can be represented as streams. High-level tasks often require some sort of reasoning over the input data, however raw streaming input is often not suitable for the higher level representations needed for reasoning. DyKnow is a stream processing framework that provides functionalities to represent knowledge needed for reasoning from streaming inputs. DyKnow has been used within a platform for task planning and execution monitoring for UAVs. The execution monitoring is performed using formula progression with monitor rules specified as temporal logic formulas. In this thesis we present an analysis for providing spatio-temporal functionalities to the formula progressor and we extend the formula progression with spatial reasoning in RCC-8. The result implementation is capable of evaluating spatio-temporal logic formulas using progression over streaming data. In addition, a ROS implementation of the formula progressor is presented as a part of a spatio-temporal stream reasoning architecture in ROS. / Collaborative Unmanned Aircraft Systems (CUAS)

Real-time strategy (RTS) games constitute one of the largest game genres today and have done so for the past decade. A central feature of real-time strategy games is opponent AI which is suggestively the “last frontier” of game development because the focus of research has primarily been on other components, graphics in particular. This has led to AI research being largely ignored within the commercial game industry but several methods have recently been suggested for improving the strategic ability of AI agents in real-time strategy games. The aim of this project is to evaluate how a method called qualitative spatial reasoning can improve AI on a tactical level in a selected RTS game. An implementation of an AI agent that uses qualitative spatial reasoning has been obtained and an evaluation of its performance in an RTS game example monitored and analysed. The study has shown that qualitative spatial reasoning affects AI agent’s behaviour significantly and indicates that it can be used to deduce a rule-base that increases the unpredictability and performance of the agent.

In the last two decades, qualitative spatial representation and reasoning, and in particular spatial logics, have been the subject of an increased interest from the Artificial Intelligence community. By a spatial logic, we understand a formal language whose variables range over subsets of a fixed topological space, called regions, and whose non-logical primitives have fixed geometric meanings. A spatial logic for reasoning about regions in a Euclidean space is called a Euclidean spatial logic. We consider first-order and quantifier-free Euclidean spatial logics with primitives for topological relations and operations, the property of convexity and the ternary relation of being closer-than. We mainly focus on the computational properties of such logics, but we also obtain interesting model-theoretic results. We provide a systematic overview of the computational properties of firstorder Euclidean spatial logics and fill in some of the gaps left by the literature. We establish upper complexity bounds for the (undecidable) theories of logics based on Euclidean spaces of dimension greater than one, which yields tight complexity bounds for all but two of these theories. In contrast with these undecidability results, we show that the topological theories based on one-dimensional Euclidean space are decidable, but non-elementary. We also study the computational properties of quantifier-free Euclidean spatial logics, and in particular those able to express the property of connectedness. It is known that when variables range over regions in the Euclidean plane, one can find formulas in these languages satisfiable only by regions with infinitely many connected components. Using this result, we show that the corresponding logics are undecidable. Further, we show that there exist formulas that are satisfiable in higher-dimensional Euclidean space, but only by regions with infinitely many connected components. We finish by outlining how the insights gained from this result were used (by another author) to show the undecidability of certain quantifier-free Euclidean spatial logics in higher dimensions.

Um sistema artificial pode usar raciocínio espacial qualitativo para inferir informações sobre seu ambiente tridimensional a partir de imagens bidimensionais. Inferências realizadas com base em raciocínio espacial qualitativo devem ser capazes de lidar com incertezas. Neste trabalho investigamos a utilização de técnicas probabilísticas para tornar o raciocínio espacial qualitativo mais robusto a incertezas e aplicável a agentes móveis em ambientes reais. Investigamos uma formalização de raciocínio espacial com lógica de descrição probabilística em um subdomínio de tráfego. Desenvolvemos também um método que combina raciocínio espacial qualitativo com um filtro Bayesiano para desenvolver dois sistemas que foram aplicados na auto localização de um robô móvel. Executamos dois experimentos de auto localização; um utilizando a teoria de relações qualitativas percebíveis sobre sombra com filtro Bayesiano; e outro utilizando o cálculo de oclusão de regiões e o cálculo de direção com filtro Bayesiano. Ambos os sistemas obtiveram resultados positivos onde somente o raciocínio espacial qualitativo não foi capaz de inferir a localização do robô. Os experimentos com dados reais mostraram robustez aos ruídos e à informação parcial. / An artificial system can use qualitative spatial reasoning to obtain information about its tridimensional environment, from bi-dimensional images. Inferences produced by qualitative spatial reasoning must be able to deal with uncertainty. This work investigates the use of probabilistic techniques to make qualitative spatial reasoning more robust against uncertainty, and better applicable to mobile agents in real environments. The work investigates a formalization of spatial reasoning using probabilistic description logics in a traffic domain. Additionally, a method is presented that combines qualitative spatial reasoning with a Bayesian filter, to develop two systems that are applied to self-localization of mobile robots. Two experiments are described; one using the theory of perceptual qualitative relations about shadows; the other using occlusion calculus and direction calculus. Both systems are combined with a Bayesian filter producing positive results in situations where qualitative spatial reasoning alone cannot infer robot location. Experiments with real data show robustness to noise and partial information.

El objetivo de esta tesis es proponer mejoras en modelos existentes de razonamiento espacial cualitativo con relaciones cardinales, y proponer nuevos modelos y técnicas de razonamiento utilizando algunos resultados previos del razonamiento temporal cualitativo. Los modelos propuestos se basan en dos formalismos muy utilizados para razonamiento cualitativo: los Problemas de Satisfacción de Restricciones y las Lógicas Modales. / The main goal of this PhD Thesis is to propose improvements to existing models for qualitative spatial reasoning with cardinal direction relations, and to propose new models and reasoning techniques using some previous results from qualitative temporal reasoning. The proposed models are based on two widely used formalisms for Qualitative Reasoning: Constraint Satisfaction Problems and Modal Logics.

Um sistema artificial pode usar raciocínio espacial qualitativo para inferir informações sobre seu ambiente tridimensional a partir de imagens bidimensionais. Inferências realizadas com base em raciocínio espacial qualitativo devem ser capazes de lidar com incertezas. Neste trabalho investigamos a utilização de técnicas probabilísticas para tornar o raciocínio espacial qualitativo mais robusto a incertezas e aplicável a agentes móveis em ambientes reais. Investigamos uma formalização de raciocínio espacial com lógica de descrição probabilística em um subdomínio de tráfego. Desenvolvemos também um método que combina raciocínio espacial qualitativo com um filtro Bayesiano para desenvolver dois sistemas que foram aplicados na auto localização de um robô móvel. Executamos dois experimentos de auto localização; um utilizando a teoria de relações qualitativas percebíveis sobre sombra com filtro Bayesiano; e outro utilizando o cálculo de oclusão de regiões e o cálculo de direção com filtro Bayesiano. Ambos os sistemas obtiveram resultados positivos onde somente o raciocínio espacial qualitativo não foi capaz de inferir a localização do robô. Os experimentos com dados reais mostraram robustez aos ruídos e à informação parcial. / An artificial system can use qualitative spatial reasoning to obtain information about its tridimensional environment, from bi-dimensional images. Inferences produced by qualitative spatial reasoning must be able to deal with uncertainty. This work investigates the use of probabilistic techniques to make qualitative spatial reasoning more robust against uncertainty, and better applicable to mobile agents in real environments. The work investigates a formalization of spatial reasoning using probabilistic description logics in a traffic domain. Additionally, a method is presented that combines qualitative spatial reasoning with a Bayesian filter, to develop two systems that are applied to self-localization of mobile robots. Two experiments are described; one using the theory of perceptual qualitative relations about shadows; the other using occlusion calculus and direction calculus. Both systems are combined with a Bayesian filter producing positive results in situations where qualitative spatial reasoning alone cannot infer robot location. Experiments with real data show robustness to noise and partial information.