This thesis investigates the level of difficulty of puzzles in the adventure games and the implications thereof. The thesis contains an in-depth background, and a brief history about the genre. It brings up the main problem of the genre and looks into both the cause and effect that follows. To support this process, an analysis has been made of design documents and a survey was issued on the subject of adventure game puzzles.

In today's competitive manufacturing environment, companies are constantly looking for ways to improve. Because of this, many companies are striving to become "lean" by implementing lean manufacturing, which is a difficult process. To aid in the implementation of lean manufacturing, simulation was used to reduce the trial-and-error period of lean manufacturing and find to optimum approach to implement the lean manufacturing principle. In this research, a case study of implementing level loading of the production schedule for BullFrog International, L.C. will be examined. To make it possible to implement level loading, the thermo-former machine at the beginning of the operations was improved to allow quick changeovers. The changeover time was reduced by 60% and with a few additional changes changeovers could be completely external. In order to be able to conduct simulation experiments to find the optimum production schedule, cycle times were gathered for each operation and a simulation model was developed of BullFrog International, L.C. current manufacturing operations. Historical data was gathered of previous month's sales orders and orders were divided into three different groups. Group 1 the spa orders are roughly 50% single-pump and 50% double-pump, group 2 the spa orders are roughly 60% or more single-pump spas and group 3 the spa orders are roughly 60% or more double-pump spas. Using historical data, level loading production schedules were developed using lean manufacturing principles by reducing lot sizes to the smallest possible and still preserving the correct ratios. All of these suggested production schedules were tested with the simulation model and through various experiments, the optimum production schedule were determined. The optimum production schedules were implemented and the results were recorded. The results were an average throughput increase of 49.1% in group 1, an average throughput increase of 58.7% in group 2 and an average throughput increase of 58.7% in group 3. These results support the hypothesis that level loading will increase throughput in a complex manufacturing system where there is a high mix and low volume production schedule. The results also support the hypothesis that the trial-and-error period was reduced by the use of simulation.

No description available.

Les robots opèrent dans le monde réel, dans lequel essayer quelque chose prend beaucoup de temps. Pourtant, les methodes d’apprentissage par renforcement actuels (par exemple, deep reinforcement learning) nécessitent de longues périodes d’interaction pour trouver des politiques efficaces. Dans cette thèse, nous avons exploré des algorithmes qui abordent le défi de l’apprentissage par essai-erreur en quelques minutes sur des robots physiques. Nous appelons ce défi “Apprentissage par renforcement micro-data”. Dans la première contribution, nous avons proposé un nouvel algorithme d’apprentissage appelé “Reset-free Trial-and-Error” qui permet aux robots complexes de s’adapter rapidement dans des circonstances inconnues (par exemple, des dommages) tout en accomplissant leurs tâches; en particulier, un robot hexapode endommagé a retrouvé la plupart de ses capacités de marche dans un environnement avec des obstacles, et sans aucune intervention humaine. Dans la deuxième contribution, nous avons proposé un nouvel algorithme de recherche de politique “basé modèle”, appelé Black-DROPS, qui: (1) n’impose aucune contrainte à la fonction de récompense ou à la politique, (2) est aussi efficace que les algorithmes de l’état de l’art, et (3) est aussi rapide que les approches analytiques lorsque plusieurs processeurs sont disponibles. Nous avons aussi proposé Multi-DEX, une extension qui s’inspire de l’algorithme “Novelty Search” et permet de résoudre plusieurs scénarios où les récompenses sont rares. Dans la troisième contribution, nous avons introduit une nouvelle procédure d’apprentissage du modèle dans Black-DROPS qui exploite un simulateur paramétré pour permettre d’apprendre des politiques sur des systèmes avec des espaces d’état de grande taille; par exemple, cette extension a trouvé des politiques performantes pour un robot hexapode (espace d’état 48D et d’action 18D) en moins d’une minute d’interaction. Enfin, nous avons exploré comment intégrer les contraintes de sécurité, améliorer la robustesse et tirer parti des multiple a priori en optimisation bayésienne. L'objectif de la thèse était de concevoir des méthodes qui fonctionnent sur des robots physiques (pas seulement en simulation). Par conséquent, tous nos approches ont été évaluées sur au moins un robot physique. Dans l’ensemble, nous proposons des méthodes qui permettre aux robots d’être plus autonomes et de pouvoir apprendre en poignée d’essais / Robots have to face the real world, in which trying something might take seconds, hours, or even days. Unfortunately, the current state-of-the-art reinforcement learning algorithms (e.g., deep reinforcement learning) require big interaction times to find effective policies. In this thesis, we explored approaches that tackle the challenge of learning by trial-and-error in a few minutes on physical robots. We call this challenge “micro-data reinforcement learning”. In our first contribution, we introduced a novel learning algorithm called “Reset-free Trial-and-Error” that allows complex robots to quickly recover from unknown circumstances (e.g., damages or different terrain) while completing their tasks and taking the environment into account; in particular, a physical damaged hexapod robot recovered most of its locomotion abilities in an environment with obstacles, and without any human intervention. In our second contribution, we introduced a novel model-based reinforcement learning algorithm, called Black-DROPS that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available. We additionally proposed Multi-DEX, a model-based policy search approach, that takes inspiration from novelty-based ideas and effectively solved several sparse reward scenarios. In our third contribution, we introduced a new model learning procedure in Black-DROPS (we call it GP-MI) that leverages parameterized black-box priors to scale up to high-dimensional systems; for instance, it found high-performing walking policies for a physical damaged hexapod robot (48D state and 18D action space) in less than 1 minute of interaction time. Finally, in the last part of the thesis, we explored a few ideas on how to incorporate safety constraints, robustness and leverage multiple priors in Bayesian optimization in order to tackle the micro-data reinforcement learning challenge. Throughout this thesis, our goal was to design algorithms that work on physical robots, and not only in simulation. Consequently, all the proposed approaches have been evaluated on at least one physical robot. Overall, this thesis aimed at providing methods and algorithms that will allow physical robots to be more autonomous and be able to learn in a handful of trials

Function Shift is the transference of functions, which involves responsibilities, assets and human resources (including their employment packages), from one department to the next. The Function Shift to which I refer in this study entails the shifting of functions from the former Adult Education and Training provincial directorates to the Department of Higher Education and Training (DHET). This process started in 2009 in terms of proclamation 48 of 2009. The purpose of my active research is to investigate in depth the experienced positive and negative consequences of Function Shift with the intention of exploring problematic features and challenges of Community Education and possibilities for addressing them. By ‘experienced’ consequences, I mean consequences that are not just imagined but were expressed by participants. I employed an 'active' qualitative research approach whereby I, as a researcher, am actively involved in the research process in trying to ensure that the research is bearing results for me as well as for the participants. I based the selection of Community Education and Training Colleges on the characteristics of the regions in which the colleges belonged. I clustered regions that portrayed similar characteristics and came out with 3 clusters. I selected one region and its respective college from each of the 3 clusters. From each of the selected regions and their corresponding colleges, I sampled a Regional manager, Curriculum Implementer or regional official, Principal, 1 Centre manager, 1 lecturer and 1 student. I collected data through first and second interview sessions, focus group discussion in 1 college and through evaluative discussion with 2 head office officials. To carry out data analysis, I used the principles of Atlas TI that encourages the coding, categorisation and thematising data collected from participants simultaneously with data from the reviewed literature. It emerged that all participants agreed that a multilevel change management system is suitable for Function Shift as opposed to the traditional rational/linear model and that Function Shift is a potential solution to Adult Education and Training challenges. The prevailing challenge was insufficient consultation, which resulted in some transitional challenges that could have been identified and mitigated against. My concluding recommendation is that the oral or print input made by members on the ground including the assessment of the real and practical situation in Community Learning Centres must drive the development of policies that are still cascaded by the DHET. Consultation must be characterised by dialogue, not announcements of deadlines. / Ku susumetiwa ka mintirho swi vula ku susiwa ka vutihlamuleri endzawuleni yinwana byi yisiwa endzawuleni yin’wani. Vutihlamuleri lebyi byi katsa tinhundu, timali, vatirhi ni miholo ya vona ni hikwaswo leswi fambelanaka ni xiyenge xexo. Ndzavisiso lowu wu vulavula hi ku susiwa ka vutihlamuleri bya dyondzo ni vudzaberi/vuthwaseli bya vatswatsi (Adult Education and Training) e mindzawuleni ya dyondzo ya le hansi ya swifunda (Provincial Department of Basic Education) ku yisiwa e ndzawuleni ya le henhla ya dyondzo ni vudzaberi (Department of Higher Education and Training). Nghingiriko lowu wa ku cinciwa ka vutihlamuleri wu sungurile hi lembe ra 2009. Makungu ya ndzavisiso lowu wa mahika I ku lavisisa hi vuxokoxoko vumbhoni bya switandzaku (mbuyelo lowunene ni lowu wu nga tsakisiki) leswi vangiwanga hi ku cinciwa ka vutihlamuleri, hi xikongomelo xo paluxa swirhalanganyi swa dyondzo ya vaaki (Community Education) ni ku ololoxa swirhalanganyi leswi. Loko ni ku vumbhoni bya switandzaku, ndzi vula switandzaku leswi swi nga kumbeteriwiki, kambe leswi vahlamuri (participants/respondents) va nyikaka vumbhoni bya leswi va nga swi vona ni ku switwa. Ndzi endlile vulavisisi bya mahika (active research), laha mina tani hi mulavisisi ndzi tlangeke xiyenge xa ku endla leswaku vulavisisi lebyi byi va ni mbuyelo lowu nga ta pfuna mina xikan’we na muhlamuri. Ndzi hlawurile tilholichi ta dyondzo ni vudzaberi ta vaaki ku ya hi tindhawu /tirhijini laha tikholichi leti ti kumekaka kona. Ndzi longoloxile tirhijini hinkwato, ndzi ti katsakanya hi timpawu ta tona, ivi ndzi huma na mintlawa minarhu. Ndzi hlawule kholeji yin’we eka ntlawa wun’wani ni wun’wani ni tirhijini ta tona. Eka rhijini yin'wana na yin'wana ndzi hlawurile no tihlanganisa na vanhu lava landzelaka: mufambisi wa rhijini, mukamberi/museketeri wa dyondzo a rhijinini, nhloko ya kholeji, mufambisi wa sentara, mudzaberi na xichudeni. Eka Kholeji yo sungula ni ya vumbirhi, ndzi hlengeletile mahungu hi ku burisana ni vahlamuri hi wun’we ha wun’we. Eka Kholeji ya vunharhu, ndzi hlengelete mahungu hi mbhurisano wa hlengeletano ya murhangeri wa senthara, vadzaberi va nharhu ni machudeni mambirhi. Ku kuma voxokoxoko ni nhlavutelo wa mahungu lawa ndzi wa hlengeleteke, ndzi tirhisile maendlelo ya "Atlas Tl" yaku hlohlotela ku kuma vuxokoxoko hi ku tirhisa tekinoloji, ku longoloxa ku ya hi swiyimo ni ku endla vulavisisi eka tibuku tin'wana. Vahlamuri va pfumelelanile leswaku mafambiselo ya ku cinca loku khumbhaka swiyenge swo hambana-hambana (Multilevel change management) hi nkarhi wun’we hi wona lama fanelaka ku susumetiwa ka vutihlamuleri. Nakambe vahlamuri va pfumelelanile leswaku ku susmetiwa ka vutihlamuleri swi nga tisa xintshuxo eka ku tikeriwa loku a ku ri kona e ka dyondzo ni vudzaberi bya vatswatsi. Ndzi heta hi ku vula leswaku swibumabumelo leswi tsariweke ni ku vuriwa hi milomo ya vaaki, ni ku xopaxopela xiyimo lexi xi nga etisenthareni ta dyondzo ya vaaki, hi swona leswi fanelaka ku va makombandlela ya ku tumbuluxiwa ni ku hangalasiwa ka milawu (policies) leyi ya ha endliwaka hi ndzawulo ya le henhla ya dyondzo ni vudzaberi. Njhenhjekisano wa miehleketo exikarhi ka varhangeri ni vaaki hi yona ndlela ya kahle yaku tihlanganisa (consultation) na vanhu. / Phetišetšo ya mošomo ke go fetišetša mešomo, yeo e amago maikarabelo, dithoto le methopo ya batho (go akaretšwa ditshwanelo tša bona tša mošomo), go tloga go kgoro ye nngwe go ya go ye nngwe. Phetišetšo ya mošomo yeo ke bolelago ka yona ka mo dinyakišišong e ama go fetišetša mešomo ya Thuto ya Batho ba Bagolo le Tlhahlo go tloga go diofisi tša bolaodibogolo bja diprofense tša Thuto ya Batho ba Bagolo le Tlhahlo tša pele go ya go go Thuto ya Godimo le Tlhahlo (DHET). Tshepetšo ye e thomile ka 2009 go ya ka pego ya 48 ya 2009. Nepo ya dinyakišišo tša ka tša go rarolla bothata ke go nyakišiša go tsenelela dipoelo tše dibotse le tše dimpe tša maitemogelo tša Phetišetšo ya Mošomo ka nepo ya go nyakišiša dibopego tša mathata le ditlhohlo tša Thuto ya Setšhaba le dikgonagalo tša go šogana le tšona. Ka ‘dipoelo tša maitemogelo’ ke bolela ka dipoelo tšeo di sa akanywego fela eupša di tšweletšwa ke bakgathatema. Ke šomišitše mokgwa wa dinyakišišo wa boleng wa “go rarolla bothata’ moo nna, bjalo ka monyakišiši, ke amana ka dinyakišišong ka mafolofolo go kgonthiša gore dinyakišišo di na le dipoelo tše dibotse go nna le go bakgathatema. Ke theile kgetho ya ka ya Thuto ya Setšhaba le Dikholetšhe tša Tlhahlo go dibopego tša dilete tšeo dikholetšhe tše di lego gona. Ke hlopile dilete tšeo di bontšhago dibopego tša go swana gomme ka tšweletša dihlopha tše tharo. Go tšwa go dilete tše dingwe le tše dingwe tšeo di kgethilwego le dikholetšhe tšeo di amanago le tšona, ke dirile sešupo ka molaodi wa Selete, Mophethagatši wa Lenaneothuto goba mohlankedi wa selete, Hlogo ya Sekolo, molaodi wa Senthara yo motee, mofahloši yo motee le moithuti yo motee. Ke kgobokeditše data ka dikopano tša mathomo le tša bobedi tša dipoledišano, dipoledišano tša sehlopha sa nepišo kholetšheng ye tee ka dipoledišano tša tekolo le bahlankedi ba babedi ba kantorokgolo. Go dira tshekatsheko ya data, ke šomišitše methopo ya Atlas TI ye e hlohleletšago go swaya, go hlopha le go kgetha data ye e kgobokeditšwego go tšwa go bakgathatema ka nako ye tee le data go tšwa go dingwalwa tšeo di sekasekilwego. Go tšweletše gore bakgathatema ka moka ba dumetše gore mokgwa wa taolo ya phetogo ya magato a mantši o loketše Phetišetšo ya Mošomo kgahlanong le mmotlolo wa tlwaelo/thwii wa mathomong le gore Phetišetšo ya Mošomo ke tharollo ye e kgonagalago ya ditlhohlo tša Thuto ya Batho ba Bagolo le Tlhahlo. Tlhohlo ye e tšwelelago e be e le therišano yeo e sa lekanago, yeo e feleditšego ka ditlhohlo tša phetišetšo tšeo di bego di utollotšwe gomme tša fedišwa. Tigelo ya ka ya go phetha ke dikgopolo tša molomo le tšeo di gatišitšwego tšeo di filwego ke maloko a mo fase go akaretšwa kelo ya maemo a nnete le a tiro mo Disenthareng tša Go ithuta tša Setšhaba di swanetše go eta pele tšweletšopele ya dipholisi tšeo di sa fetišwago ke DHET. Ditherišano di swanetše go bopša ke poledišano, e sego ditsebišo tša matšatši a mafelelo. / ABET and Youth Development / D. Ed. (Socio Education)

Cette thèse interroge, par la méthode et la conception design, les modalités permettant de construire des récits liés à l'imaginaire urbain. Ces derniers constituent des potentialités qui racontent au travers de la dimension sonore des faits mémoriels, histoires et vécus de groupes. Le « designer des sons de l'urbain » construit alors sa propre scénophonie urbaine, procédé permettant de raconter l'espace sonore, entre espaces du temps et temps de l'espace. Cette posture permet de rendre compte de la prégnance socio-culturelle du fait sonore dans la manière de raconter le monde. Il s'agit de déceler puis d'interroger les enjeux d'une société en pleine mutation à partir de l'utilisation d'enregistrements de terrain et de créations sonores de territoire, c'est-à-dire des narrations composées à partir de ces premières captures. Ce travail s'articule autour d'une étude théorique d'un corpus artistique (Tome 1) et d'une recherche-action menée dans le cadre d'un contrat Cifre au sein de la Direction de la Culture de la Ville de Strasbourg, pour le Shadok, fabrique du numérique, régie directe de l'Eurométropole en collaboration avec une association trandisciplinaire, Horizome et l'équipe d'accueil ACCRA de l'Université de Strasbourg (Tome 2). / This dissertation questions through a design approach the ways and means allowing the construction of stories related to the urban imaginary. The latter constitute possibilities of telling something through the sound dimensions of historical events, and both individual and collective experiences. The “designer of urban sounds” then builds their own urban scenophony, a method allowing the telling of the sound space — between the spaces of time and the times of space. Such a position allows to account for the socio-cultural weight of sound in the waythe world is told. This research is about detecting and then questioning what is at stakes in a changing society, using field recordings and “territory sound creations,” i.e. narratives built from the raw material of those field recordings. Two dimensions are involved in this work: a theoretical study of an artistic corpus (Tome I) and an action research born outof a city of Strasburg Cifre contract for the state-sponsored and controled Shadok,fabrique du numérique, in collaboration with a transdisciplinary association, Horizome,and the ACCRA team of the University of Strasburg (Tome II).