High-level functional (or behavioral) simulation models are difficult, time-consuming, and expensive to develop. We report on a method for automatically generating the program code for a high-level functional simulation model. The high-level model is produced directly from the program code for the circuit components' functional models and a netlist description of their connectivity. A prototype has been implemented in LISP for the SIMMER functional simulator.

No description available.

We put forth a multiple representation approach to deriving the behavioral model of a digital circuit automatically from its structure and the behavioral simulation models of its components. One representation supports temporal reasoning for composition and amplification, another supports simulation and a third helps to partition the translation problem. A working prototype, FUNSTRUX, is described.

Agile autonomous systems are emerging, such as unmanned aerial vehicles (UAVs), that must robustly perform tightly coordinated time-critical missions; for example, military surveillance or search-and-rescue scenarios. In the space domain, execution of temporally flexible plans has provided an enabler for achieving the desired coordination and robustness, in the context of space probes and planetary rovers, modeled as discrete systems. We address the challenge of extending plan execution to systems with continuous dynamics, such as air vehicles and robot manipulators, and that are controlled indirectly through the setting of continuous state variables.Systems with continuous dynamics are more challenging than discrete systems, because they require continuous, low-level control, and cannot be controlled by issuing simple sequences of discrete commands. Hence, manually controlling these systems (or plants) at a low level can become very costly, in terms of the number of human operators necessary to operate the plant. For example, in the case of a fleet of UAVs performing a search-and-rescue scenario, the traditional approach to controlling the UAVs involves providing series of close waypoints for each aircraft, which incurs a high workload for the human operators, when the fleet consists of a large number of vehicles.Our solution is a novel, model-based executive, called Sulu, that takes as input a qualitative state plan, specifying the desired evolution of the state of the system. This approach elevates the interaction between the human operator and the plant, to a more abstract level where the operator is able to “coach” the plant by qualitatively specifying the tasks, or activities, the plant must perform. These activities are described in a qualitative manner, because they specify regions in the plant’s state space in which the plant must be at a certain point in time. Time constraints are also described qualitatively, in the form of flexible temporal constraints between activities in the state plan. The design of low-level control inputs in order to meet this abstract goal specification is then delegated to the autonomous controller, hence decreasing the workload per human operator. This approach also provides robustness to the executive, by giving it room to adapt to disturbances and unforeseen events, while satisfying the qualitative constraints on the plant state, specified in the qualitative state plan.Sulu reasons on a model of the plant in order to dynamically generate near-optimal control sequences to fulfill the qualitative state plan. To achieve optimality and safety, Sulu plans into the future, framing the problem as a disjunctive linear programming problem. To achieve robustness to disturbances and maintain tractability, planning is folded within a receding horizon, continuous planning and execution framework. The key to performance is a problem reduction method based on constraint pruning. We benchmark performance using multi-UAV firefighting scenarios on a real-time, hardware-in-the-loop testbed. / SM thesis

Spatio-temporal reasoning is the area of automated reasoning about space and time and is important in the field of robotics. It is desirable for an autonomous robot to have the ability to reason about both time and space. ST0 is a logic that allows for such reasoning by, among other things, defining a formalism used to describe the relationship between spatial regions and a calculus that allows for deducing further information regarding such spatial relations. An extension of ST0 is ST1 that can be used to describe the relationship between spatial entities across time-points (inter-temporal relations) while ST0 is constrained to doing so within a single time-point. This allows for a better ability of expressing how spatial entities change over time. A major obstacle in using ST1 in practise however, is the fact that any observations made regarding spatial relations between regions is constrained to the time-point in which the observation was made, so we are unable to observe inter-temporal relations. Further complicating things is the fact that deducing such inter-temporal relations is not possible without a frame of reference. This thesis examines one method of overcoming these problems by considering the concept of rigid regions which are assumed to always be unchanging and using them as the frame of reference, or as landmarks. The effectiveness of this method is studied by conducting experiments where a comparison is made between various landmark ratios with respect to the total number of regions under consideration. Results show that when a high degree of intra-temporal relations are fully or partially known, increasing the number of landmark regions will reduce the percentage of inter-temporal relations to be completely unknown. Despite this, very few inter-temporal relations can be fully determined even with a high ratio of landmark regions.

No description available.

A lot of today's data is generated incrementally over time by a large variety of producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, making sense of these streams of data through reasoning is challenging. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in a physical environment. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and its refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this thesis, we integrate techniques for logic-based spatio-temporal stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over streaming data and the problem of robustly managing streaming data and its refinement. The main contributions of this thesis are (1) a logic-based spatio-temporal reasoning technique that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt in situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in the context of a case study on run-time adaptive reconfiguration. The results show that the proposed system – by combining reasoning over and reasoning about streams – can robustly perform spatio-temporal stream reasoning, even when the availability of streaming resources changes. / <p>The series name <em>Linköping Studies in Science and Technology Licentiate Thesis</em> is inocorrect. The correct series name is <em>Linköping Studies in Science and Technology Thesis</em>.</p> / NFFP6 / CENIIT

Denna uppsats granskar forskning som gjorts kring kopplingar mellan musik och matematik i en didaktisk kontext och diskuterar hur sådana kopplingar kan användas i musikundervisning i årskurs 1–6 för att förbättra elevers matematikkunskaper. Syftet är att belysa likheter mellan musik och matematik och ta reda om och i så fall hur en konkretisering av och ett fokus på dessa likheter kan gynna elevers lärande i matematik. Resultatet visar att det finns flera olika sätt på vilka musikundervisning kan leda till att elever presterar bättre i matematik. Tydligast effekt verkar uppstå för lågpresterande elever och när likheter mellan ämnena uttrycks explicit. Några av de granskade studierna har inte sett samma effekt på lärande, medan andra ser endast en korrelation mellan musikutövande och matematikförmågor, alternativt upptäcker andra förmågor som möjligen medierar en effekt från musikutövande till matematikförmågor. Med hjälp av det sociokulturella perspektivet och Gardners teori om multipla intelligenser diskuteras vad som kan göra att effekten uppstår. Diskussionen leder fram till en modell som gör anspråk på att förklara att musikutövande över tid tillsammans med någon ytterligare påverkan kan leda till ökade matematiska förmågor.

Temporal reasoning has been an active research area for over twenty years, with most work focussing on either enhancing the efficiency of current temporal reasoning algorithms or enriching the existing algebras. However, there has been little research into handling over-constrained temporal problems except to recognise that a problem is over-constrained and then to terminate. As many real-world temporal reasoning problems are inherently over-constrained, particularly in the scheduling domain, there is a significant need for approaches that can handle over-constrained situations. In this thesis, we propose two backtracking algorithms to gain partial solutions to over-constrained temporal problems. We also propose a new representation, the end-point ordering model, to allow the use of local search algorithms for temporal reasoning. Using this model we propose a constraint weighting local search algorithm as well as tabu and random-restart algorithms to gain partial solutions to over-constrained temporal problems. Specifically, the contributions of this thesis are: The introduction and empirical evaluation of two backtracking algorithms to solve over-constrained temporal problems. We provide two backtracking algorithms to close the gap in current temporal research to solve over-constrained problems; The representation of temporal constraint networks using the end-point ordering model. As current representation models are not suited for local search algorithms, we develop a new model such that local search can be applied efficiently to temporal reasoning; The development of a constraint weighting local search algorithm for under-constrained problems. As constraint weighting has proven to be efficient for solving many CSP problems, we implement a constraint weighting algorithm to solve under-constrained temporal problems; An empirical evaluation of constraint weighting local search against traditional backtracking algorithms. We compare the results of a constraint weighting algorithm with traditional backtracking approaches and find that in many cases constraint weighting has superior performance; The development of a constraint weighting local search, tabu search and random-restart local search algorithm for over-constrained temporal problems. We extend our constraint weighting algorithm to solve under-constrained temporal problems as well as implement two other popular local search algorithms: tabu search and random-restart; An empirical evaluation of all three local search algorithms against the two backtracking algorithms. We compare the results of all three local search algorithms with our twobacktracking algorithms for solving over-constrained temporal reasoning problems and find that local search proves to be considerably superior.

Desde su aparición a mediados de los años 60, la Teoría de Conjuntos Difusos se ha venido aplicando con éxito a la resolución de problemas en ámbitos muy diversos, que resultan difíciles de tratar con los métodos clásicos, principalmente por la presencia de incertidumbres no aleatorias en su descripción. En estos casos, el problema no tiene una solución cerrada en forma de expresión matemática, pero sí suele tenerla en forma de un conjunto de reglas expresadas en lenguaje natural y, por consiguiente, impreciso. Un ejemplo típico es el problema de conducir un automóvil.En el ámbito de la ingeniería, el núcleo de cualquier solución difusa actual es un sistema lógico difuso, encargado de obtener las salidas a partir de las entradas en un proceso de tres etapas: füzzificación, inferencia y desfuzzifícación. Hasta la fecha, la totalidad de sistemas difusos efectúan sus razonamientos basándose solamente en los valores actuales de las entradas. Ello ha dado como resultado que los sistemas de inferencia difusa sean, desde el punto de vista matemático, sistemas no lineales algebraicos. Este hecho contrasta fuertemente con el entorno en que dichos sistemas suelen emplearse. En efecto, la mayoría de aplicaciones se construyen y utilizan en entornos dinámicos, los cuales son capaces de presentar comportamientos mucho más complejos que los sistemas estáticos. Cabe entonces preguntarse si el uso de sistemas difusos dinámicos -es decir, aquellos en que sus salidas dependan no sólo de los valores presentes de las entradas sino también de los pasados-, aportaría mejoras respecto a las soluciones difusas actuales.En esta tesis se ha desarrollado una metodología para incorporar conceptos temporales difusos a los sistemas de inferencia difusa tradicionales. Para ello se ha propuesto una forma simple y eficaz de representar los citados conceptos en un entorno de ingeniería. Posteriormente se ha mostrado cómo introducirlos en las reglas difusas tradicionales, y se ha desarrollado un algoritmo para efectuar la inferencia en esta nueva situación. Se obtienen finalmente dos algoritmos distintos para dos casos diferenciados, pero ambas expresiones presentan la interesante propiedad de poder interpretarse como una convolución, tradicional en uno de los casos y una nueva forma que hemos denominado convolution difusa para el otro caso. Estas expresiones se pueden realizar por tanto de una forma muy elegante mediante circuitos analógicos o digitales.La metodología desarrollada requiere que los conceptos temporales difusos que se manejan deban realizarse mediante la respuesta impulsional de un circuito lineal. Ello remite al problema del diseño de filtros desde el punto de vista temporal, mucho menos estudiado que desde el punto de vista frecuencial. A resultas de ello se dedica una parte de la presente tesis a establecer las pautas a seguir en el proceso de diseño de dichos filtros, valiéndose de técnicas de aproximación y de optimización. Finalmente se presentan un ejemplo de aplicación, de interés tanto teórico como práctico. En él se presenta un sistema de reconocimiento simple de comandos verbales, basado en las técnicas propuestas en la presente tesis. Los resultados obtenidos han mostrado que con una estructura muy simple es posible obtener una discriminación más que suficiente entre las órdenes programadas, con la ventaja que presenta el realizar el sistema de forma totalmente analógica.