Estudos empíricos para a construção do teste Maps

O presente trabalho é composto por dois estudos empíricos que contribuem para a criação do Teste MAPS (Memória e Aprendizagem através de Pistas Seletivas). O primeiro estudo investigou, de forma exploratória, a estrutura fatorial e a repetição de ensaios de um instrumento análogo ao MAPS que avalia os mesmos construtos através do procedimento de recordação seletiva livre e com pistas. Os resultados apontaram para uma estrutura de três fatores e a possibilidade de redução do número de ensaios. O segundo estudo apresentou o processo de criação e normatização de estímulos pictóricos para o Teste MAPS. Os resultados apontaram para um alto nível de concordância conceitual indicando a qualidade dos estímulos desenvolvidos. Os dados normativos da concordância de nomeação, da familiaridade com o conceito e complexidade visual são próximos aos observados em estudos internacionais. Esses dados servirão de parâmetro para a tomada de decisão sobre quais figuras serão selecionadas para o instrumento. Ambos os estudos fornecem importante subsídios para o desenvolvimento do Teste MAPS, seja pela melhor definição dos construtos avaliados pelo procedimento de recordação seletiva e com pistas, seja por fornecer dados que permitam a seleção de estímulos pictóricos adequados ao instrumento. Ao fim da dissertação há uma comunicação breve que mostra o estado atual do Projeto MAPS e a seleção preliminar dos estímulos a partir dos critérios psicolingüísticos criados.This research consists of two empirical studies that contribute to the development of the MAPS Test (Memory and Learning with Selective Cues). The first study investigated the factorial structure and repeated trials in another instrument that assess the same constructs with the free and cued selective reminding procedure. The results indicated a three-factor structure and the possibility to reduce the number of trials. The second study presented the process of development and standardization of pictorial stimuli for the MAPS Test. The results showed a high level of conceptual agreement indicating the quality of pictorial stimuli developed. Normative data of naming agreement, conceptual familiarity and visual complexity are similar to those observed in other studies. These attributes will be used as parameters to the process of decision-making to select pictorial stimuli. Both studies provide an important support to the quality of the MAPS Test, clarifying which constructs were assessed by the free and cued selective reminding and which pictures are the best for the test. At the end of the dissertation is a brief communication that shows the current status of Project MAPS and preliminary selection of stimuli from psycholinguistic criteria

The Integration of Explanation-Based Learning and Fuzzy Control in the Context of Software Assurance as Applied to Modular Avionics

A Modular Power Management System (MPMS) is an energy management system intended for highly modular applications, able to adapt to changing hardware intelligently. There is a dearth in the literature on Integrated Modular Avionics (IMA), which has previously not addressed the implications for software operating within this architecture. Namely, the adaptation of control laws to changing hardware. This work proposes some approaches to address this issue. Control laws may require adaptation to overcome hardware degradation, or system upgrades. There is also a growing interest in the ability to change hardware configurations of UASs (Unmanned Aerial Systems) between missions, to better fit the characteristics of each one. Hardware changes in the aviation industry come with an additional caveat: in order for a software system to be used in aviation it must be certified as part of a platform. This certification process has no clear guidelines for adaptive systems. Adapting to a changing platform, as well as addressing the necessary certification effort, motivated the development of the MPMS. The aim of the work is twofold. Firstly, to modify existing control strategies for new hardware. This is achieved with generalisation and transfer earning. Secondly, to reduce the workload involved with maintaining a safety argument for an adaptive controller. Three areas of work are used to demonstrate the satisfaction of this aim. Explanation-Based Learning (EBL) is proposed for the derivation of new control laws. The EBL domain theory embodies general control strategies, which are specialised to form fuzzy rules. A method for translating explanation structures into fuzzy rules is presented. The generation of specific rules, from a general control strategy, is one way to adapt to controlling a modular platform. A fuzzy controller executes the rules derived by EBL. This maintains fast rule execution as well as the separation of strategy and application. The ability of EBL to generate rules which are useful when executed by a fuzzy controller is demonstrated by an experiment. A domain theory is given to control throttle output, which is used to generate fuzzy rules. These rules have a positive impact on energy consumption in simulated flight. EBL is proposed, for rule derivation, because it focuses on generalisation. Generalisations can apply knowledge from one situation, or hardware, to another. This can be preferable to re-derivation of similar control laws. Furthermore, EBL can be augmented to include analogical reasoning when reaching an impasse. An algorithm which integrates analogy into EBL has been developed as part of this work. The inclusion of analogical reasoning facilitates transfer learning, which furthers the flexibility of the MPMS in adapting to new hardware. The adaptive capability of the MPMS is demonstrated by application to multiple simulated platforms. EBL produces explanation structures. Augmenting these explanation structures with a safetyspecific domain theory can produce skeletal safety cases. A technique to achieve this has been developed. Example structures are generated for previously derived fuzzy rules. Generating safety cases from explanation structures can form the basis for an adaptive safety argument