Testing oral proficiency is an important, but often neglected part of the foreign language classroom. Currently accepted methods in testing oral proficiency are timely and expensive. Some work has been done to test and implement new assessment methods, but have focused primarily on English or Spanish (Graham et al. 2008). In this thesis, I demonstrate that the processes established for English and Spanish elicited imitation (EI) testing are relevant to French EI testing. First, I document the development, implementation and evaluation of an EI test to assess French oral proficiency. I also detail the incorporation of the use of automatic speech recognition to score French EI items. Last, I substantiate with statistical analyses that carefully engineered, automatically scored French EI items correlate to a high degree with French OPI scores.

The Missionary Training Center (MTC), affiliated with the Church of Jesus Christ of Latter-day Saints, needs a reliable and cost effective way to measure the oral language proficiency of missionaries learning Spanish. The MTC needed to measure incoming missionaries' Spanish language proficiency for training and classroom assignment as well as to provide exit measures of institutional progress. Oral proficiency interviews and semi-direct assessments require highly trained raters, which is costly and time-consuming. The Elicited Imitation (EI) test is a computerized, automated test that measures oral language proficiency by having the participant hear and repeat utterances of varying syllable length in the target language. It is economical, simple to administer, and rate. This dissertation outlined the process of creating and scoring an EI test for the MTC. Item Response Theory (IRT) was used to analyze a large bank of EI items. The best performing 43 items comprise the final version MTC Spanish EI test. Questions about what linguistic features (syllable length, grammatical difficulty) contribute to item difficulty were addressed. Regression analysis showed that syllable length predicted item difficulty, whereas grammar difficulty did not.

Optimal control for multicopters is difficult in part due to the low processing power available, and the instability inherent to multicopters. Deep imitation learning is a method for approximating an expert control policy with a neural network, and has the potential of improving control for multicopters. We investigate the performance and reliability of deep imitation learning with trajectory optimization as the expert policy by first defining a dynamics model for multicopters and applying a trajectory optimization algorithm to it. Our investigation shows that network architecture plays an important role in the characteristics of both the learning process and the resulting control policy, and that in particular trajectory optimization can be leveraged to improve convergence times for imitation learning. Finally, we identify some limitations and future areas of study and development for the technology. / Optimal kontroll för multikoptrar är ett svårt problem delvis på grund av den vanligtvis låga processorkraft som styrdatorn har, samt att multikoptrar är synnerligen instabila system. Djup imitationsinlärning är en metod där en beräkningstung expert approximeras med ett neuralt nätverk, och gör det därigenom möjligt att köra dessa tunga experter som realtidskontroll för multikoptrar. I detta arbete undersöks prestandan och pålitligheten hos djup imitationsinlärning med banoptimering som expert genom att först definiera en dynamisk modell för multikoptrar, sedan applicera en välkänd banoptimeringsmetod på denna modell, och till sist approximera denna expert med imitationsinlärning. Vår undersökning visar att nätverksarkitekturen spelar en avgörande roll för karakteristiken hos både inlärningsprocessens konvergenstid, såväl som den resulterande kontrollpolicyn, och att särskilt banoptimering kan nyttjas för att förbättra konvergenstiden hos imitationsinlärningen. Till sist påpekar vi några begränsningar hos metoden och identifierar särskilt intressanta områden för framtida studier.

The purpose of this thesis was to compare the performance of three different imitation learning algorithms with human experts, with limited expert time. The central question was, ”How should one implement imitation learning in a simulated car racing environment, using human experts, to achieve the best performance when access to the experts is limited?”. We limited the work to only consider the three algorithms Behavior Cloning, DAGGER, and HG-DAGGER and limited the implementation to the car racing simulator TORCS. The agents consisted of the same type of feedforward neural network that utilized sensor data provided by TORCS. Through comparison in the performance of the different algorithms on a different amount of expert time, we can conclude that HGDAGGER performed the best. In this case, performance is regarded as a distance covered given set time. Its performance also seemed to scale well with more expert time, which the others did not. This result confirmed previously published results when comparing these algorithms. / Målet med detta examensarbete var att jämföra prestandan av tre olika algoritmer inom området imitationinlärning med mänskliga experter, där experttiden är begränsad. Arbetets frågeställning var, ”Hur ska man implementera imitationsinlärning i en bilsimulator, för att få bäst prestanda, med mänskliga experter där experttiden är begränsad?”. Vi begränsade arbetet till att endast omfatta de tre algoritmerna, Behavior Cloning, DAGGER och HG-DAGGER, och begränsade implementationsmiljön till bilsimulatorn TORCS. Alla agenterna bestod av samma sorts feedforward neuralt nätverk som använde sig av sensordata från TROCS. Genom jämförelse i prestanda på olika mängder experttid kan vi dra slutsatsen att HG-DAGGER gav bäst resultat. I detta fall motsvarar prestanda körsträcka, givet en viss tid. Dess prestanda verkar även utvecklas väl med ytterligare experttid, vilket de övriga inte gjorde. Detta resultat bekräftar tidigare publicerade resultat om jämförelse av de tre olika algoritmerna.

Background. Game developers are continuously searching for new ways of populating their vast game worlds with competent and engaging Non-Player Characters (NPCs), and researchers believe Deep Reinforcement Learning (DRL) might be the solution for emergent behavior. Consequently, fusing NPCs with DRL practices has surged in recent years, however, proposed solutions rarely outperform traditional script-based NPCs. Objectives. This thesis explores a novel method of developing an adversarial DRL NPC by combining Reinforcement Learning (RL) algorithms. Our goal is to produce an agent that surpasses its script-based opponents by first mimicking their actions. Methods. The experiment commences with Imitation Learning (IL) before proceeding with supplementary DRL training where the agent is expected to improve its strategies. Lastly, we make all agents participate in 100-deathmatch tournaments to statistically evaluate and differentiate their deathmatch performances. Results. Statistical tests reveal that the agents reliably differ from one another and that our learning agent performed poorly in comparison to its script-based opponents. Conclusions. Based on our computed statistics, we can conclude that our solution was unsuccessful in developing a talented hostile DRL agent as it was unable to convey any form of proficiency in deathmatches. No further improvements could be applied to our ML agent due to the time constraints. However, we believe our outcome can be used as a stepping-stone for future experiments within this branch of research.

A new branch of machine and deep learning models has evolved in constrained optimization, specifically in mixed integer programming problems (MIP). These models draw inspiration from earlier solver methods, primarily the heuristic, branch and bound. While utilizing the branch and bound framework, machine and deep learning models enhance either the computational efficiency or performance of the model. This thesis examines how imitating different variable selection strategies of classical MIP solvers behave on a state-of-the-art deep learning model. A recently developed deep learning algorithm is used in this thesis, which represents the branch and bound state as a bipartite graph. This graph serves as the input to a graph network model, which determines the variable in the MIP on which branching occurs. This thesis compares how imitating different classical branching strategies behaves on different algorithm outputs and, most importantly, time span. More specifically, this thesis conducts an empirical study on a MIP known as the facility location problem (FLP) and compares the different methods for imitation. This thesis shows that the deep learning algorithm can outperform the classical methods in terms of time span. More specifically, imitating the branching strategies resulting in small branch and bound trees give rise to a more rapid performance in finding the global optimum. Lastly, it is shown that a smaller embedding size in the network model is preferred for these instances when looking at the trade-off between variable selection and time cost. / En ny typ av maskin och djupinlärningsmodeller har utvecklats inom villkors optimering, specifikt för så kallade blandade heltalsproblem (MIP). Dessa modeller hämtar inspiration från tidigare lösningsmetoder, främst en heuristisk som kallas “branch and bound”. Genom att använda “branch and bound” ramverket förbättrar maskin och djupinlärningsmodeller antingen beräkningshastigheten eller prestandan hos modellen. Denna uppsats undersöker hur imitation av olika strategier för val av variabler från klassiska MIP-algoritmer beter sig på en modern djupinlärningsmodell. I denna uppsats används en nyligen utvecklad djupinlärningsalgoritm som representerar “branch and bound” tillståndet som en bipartit graf. Denna graf används som indata till en “graph network” modell som avgör vilken variabel i MIP-problemet som tas hänsyn till. Uppsatsen jämför hur imitation av olika klassiska “branching” strategier påverkar olika algoritmutgångar, framför allt, tidslängd. Mer specifikt utför denna uppsats en empirisk studie på ett MIP-problem som kallas för “facility location problem” (FLP) och jämför imitationen av de olika metoderna. I denna uppsats visas det att denna djupinlärningsalgoritm kan överträffa de klassiska metoderna när det gäller tidslängd. Mer specifikt ger imitation av “branching” strategier som resulterar i små “branch and bound” träd upphov till en snabbare prestation vid sökning av den globala optimala lösningen. Slutligen visas det att en mindre inbäddningsstorlek i nätverksmodellen föredras i dessa fall när man ser på avvägningen mellan val av variabler och tidskostnad.

Against the theoretical background of evolutionary behavioral economics, this project analyzes bounded rationality and adaptive behaviour in organizational settings characterized by complexity and persistent uncertainty. In particular, drawing upon the standard NK model, two laboratory experiments investigate individual and collective decision-making in combinatorial problems of resource allocation featuring multiple dimensions and various levels of complexity. In the first study, investment horizons of different length are employed to induce a near or distant future temporal orientation, in order to assess the effects of complexity and time horizon on performance and search behaviour, examine the presence of a temporal midpoint heuristic, and inspect the moderating effects of deadline proximity on the performance-risk relationship. This is relevant for organizational science because the passage of time is essential to articulate many strategic practices, such as assessing progress, scheduling and coordinating task-related activities, discerning the processual dynamics of how these activities emerge, develop, and terminate, or interpreting retrospected, current, and anticipated events. A greater or lesser amount of time reflects then a greater or lesser provision of resources, thereby representing a constraint that can greatly affect the ability to maintain a competitive advantage or ensure organizational survival. In the second study, the accuracy of the imitative process is varied to induce a flawless or flawed information diffusion system and, congruently, an efficient or inefficient communication network, in order to assess the effects of complexity and parallel problem-solving on autonomous search behaviour, clarify the core drivers of imitative behaviour, control for the degree of strategic diversity under different communication networks, and evaluate individual as well as collective performance conditional to the interaction between the levels of complexity and the modalities of parallel problem-solving. This is relevant for organizational science because imitating the practices of high-performing actors is one of the key strategies employed by organizations to solve complex problems and improve their performance, thereby representing a major part of the competitive process. The project is intended to contribute grounding individual and collective behaviour in a more psychologically and socially informed decision-making, with a view to further the research agenda of behavioral strategy and sustain the paradigm shift towards an evolutionary-complexity approach to real economic structures.

No description available.

In this study I addressed three problems related to how sandhi-variation, the adjustments made by speakers to the speech stream, filters comprehension for second language listener processing. The first was the need to better understand proficiency problems encountered by L2 listeners as they decode the speech stream with the phonological features of sandhi-variation, elision and assimilation, by investigating the item difficulty hierarchy of the phenomena. The second was the scarcity of research on aural processing abilities of second language learners in relation to their understanding sandhi-variation in aural texts. The third concerns the lack of research investigating links between learners’ backgrounds and their ability to handle listening texts, especially variations in the speech stream in target aural texts. The purpose of this study was threefold. My first purpose was to investigate the item difficulty hierarchy of sandhi-variation types that learners have in relation to L2 listening proficiency. My second purpose was to evaluate links between aural input containing elision and assimilation and second language aural processing, to provide insight into how learners deal with sandhi-variation as they process such input. My third purpose was to investigate through the use of interviews the aural input that participants have encountered prior to the interventions of this study, to help explain which types of aural input can facilitate intake. Twenty-five first- and second-year Japanese university students participated in the current study. The participants completed a series of instruments, which included (a) a Test of English as a Foreign Language Paper-Based Test (TOEFL PBT), (b) a Listening Vocabulary Levels Test (LVLT), (c) a Modern Language Aptitude Test–Elementary (MLAT-E), (d) a Pre-Listening in English questionnaire, (e) an Elicited Imitation Test (EIT), and (f) a Background and Length of Residency interview. The EIT was used as a sandhi-variation listening test with two component parts (i.e., elision and assimilation) and two sub-component parts (e.g., two different utterance rates), using elicited imitation. Finally, the participants were interviewed about their language backgrounds to gauge their understanding and feelings about English. An empirical item hierarchy for elision and assimilation was investigated, along with the determinants of the hierarchy. Overall, the tendency was for items with elision and assimilation to be more difficult. Results also indicated that the two input rate variables combined with elision and assimilation affected the non-native participants’ listening comprehension. Moreover, the strength of the relationship between two measures of the participants’ language ability, proficiency and aptitude, and their comprehension of items with and without the phonological features of elision and assimilation, were investigated. The results confirmed a positive relationship between language aptitude as measured by the MLAT-E and the comprehension of the phonological features of elision and assimilation. Finally, the results indicated that there were no significant, positive correlations between English language proficiency scores and both the Pre-Listening Questionnaire, which measured the participants’ feelings about second language listening, and the Background and Length of Residency Interview. More research needs to be conducted to determine how learners’ backgrounds are related to listening comprehension in order to better prescribe aural input in second language listening classrooms. / Teaching & Learning

The use of imitation exercises—writing activities employing model texts and the modeling of writing-process behaviors—in the First Year composition classroom can have many benefits for both student writers and teachers, and offers practical solutions to some of the problems facing student writers in today's colleges. First Year writing students are often unaware that they are part of a larger academic community. They often lack exposure to and understanding of academic standards. They don't understand that "good" writing is not a blanket-concept but is determined on a situational basis, and they are frustrated by the vaguely expressed expectations of their writing teachers. These problems are interconnected and so are all addressed in this study, but because they offer so many potential avenues for discussion, the focus of this project will be limited to the benefits of clear expectations that the use of modeling activities in the classroom can bring about for both students and teachers. An in-depth look at the materials, methods, and results of student participation in the activities of a single semester of English 1105, the first course in Virginia Tech's First Year Writing Program will be the dominant component of the project; it will be supplemented by a review of literature and a contextual discussion of what Stephen M. North calls the "Practitioner" mode of inquiry—the gathering of pedagogical information through the active classroom application of educational theories and practices. / Master of Arts