The primary objective of this thesis is to evolve a method for assessing and developing features of a learning organization . To fulfill this, I approached the thesis by examining several research questions and using multiple research methodologies. The research questions were not all established at the outset. Rather, they evolved as features of a journey down a road less traveled. With this journey came the decision to write the thesis in the first person. The first research question was Q1: What will bridge the divide between organizational learning and the learning organization? By reviewing the extant literature on organizational learning and the learning organization, I developed a theoretical framework that linked these two streams. The framework suggests that the extent of divide between the two streams is determined by the extent of learning transfer. The learning transfer is affected by the learning barriers operating at the levels of learning (i.e., individuals, groups, and organizational). This led me to my second research question Q2: What are these barriers to learning transfer and how do they impact the levels of learning in the organization? I cumulated the dispersed literature on learning barriers, and synthesized the learning barriers into five key dimensions: Intrapersonal, relational, cultural, structural, and societal. I then used the Delphi technique on 17 individuals to investigate the impact of the learning barriers on the levels of learning. This generated two additional research questions. The third research question was Q3: How do individuals initiate a double-loop change? This deals with the little researched area of initiation of double-loop change whilst engaging with the interfaces at the levels of learning. I used multiple case studies to examine this question and found that individuals transit through four distinct stages when initiating double-loop change: 'embedded', 'embedded discomfited', 'scripted', and 'unscripted'. Once double-loop learning has been initiated at the individual level, it is important that it is transferred across the organization. Therefore, my fourth research question was Q4: How does a new shared understanding for a double-loop change develop across the organization? I did an in-depth, single case based investigation of an organization. Using Identity and Complexity theory perspectives, I tracked the evolving new shared understanding through four phases: de-identification phase, situated re-identification phase, transition phase, and identification with core ideology phase. The key insights from examining these research questions, particularly insights from examining Q3 and Q4, enabled me to suggest nine key organizational interventions necessary to overcome the learning barriers and develop a learning organization: Identifying, developing, and dispersing double-loop mastery; Enabling constructive contradictions; Creating a superordinate organizational identity; Building emotional intelligence (in individuals and groups); Ambidextrous leadership; Strategic support for experimentation; Promoting 'systems doing'; Accessibility of valid information; Institutionalizing scanning across industry boundaries. When these nine organizational interventions are implemented, they produce five new learning organization orientations: genetic diversity, organizational ideology, organizational dualism, organizational coupling, and strategic play. These five new learning organizational orientations provide the archetypes of the learning organization. I then developed an instrument to assess these five new orientations, and did a preliminary testing of the instrument. While aspects of my work overlaid with previous knowledge, new advances in knowledge were established by: Postulating a link between the streams of organizational learning and learning organization Synthesizing learning barriers into the five key dimensions, and investigating their impact on the levels of learning Understanding the stages of double-loop learning initiation by an individual, whilst engaging with the interfaces at the levels of learning Understanding the process of a new shared understanding evolving Postulating five new orientations of the learning organization

"We address the problem of performing complex tasks for a robot operating in changing environments. We propose two approaches to the following problem: 1) define task-specific cost functions for motion planning that represent path quality by learning from an expert's preferences and 2) using constraint-based representation of the task inside learning from demonstration paradigm. In the first approach, we generate a set of paths for a given task using a motion planner and collect data about their features (path length, distance from obstacles, etc.). We provide these paths to an expert as a set of pairwise comparisons. We then form a ranking of the paths from the expert's comparisons. This ranking is used as training data for learning algorithms, which attempt to produce a cost function that maps path feature values to a cost that is consistent with the expert's ranking. We test our method on two simulated car-maintenance tasks with the PR2 robot: removing a tire and extracting an oil filter. We found that learning methods which produce non-linear combinations of the features are better able to capture expert preferences for the tasks than methods which produce linear combinations. This result suggests that the linear combinations used in previous work on this topic may be too simple to capture the preferences of experts for complex tasks. In the second approach, we propose to introduce a constraint-based description of the task that can be used together with the motion planner to produce the trajectories. The description is automatically created from the demonstration by performing segmentation and extracting constraints from the motion. The constraints are represented with the Task Space Regions (TSR) that are extracted from the demonstration and used to produce a desired motion. To account for the parts of the motion where constraints are different a segmentation of the demonstrated motion is performed using TSRs. The proposed approach allows performing tasks on robot from human demonstration in changing environments, where obstacle distribution or poses of the objects could change between demonstration and execution. The experimental evaluation on two example motions was performed to estimate the ability of our approach to produce the desired motion and recover a demonstrated trajectory."

[pt] Demonstramos como aprender a função objetivo e as restrições de problemas de otimização enquanto observamos sua solução ótima no decorrer de múltiplas rodadas. Nossa abordagem é baseada em técnicas de Online Learning e funciona para funções objetivo lineares sob conjuntos viáveis arbitrários generalizando trabalhos anteriores. Os dois algoritmos, um para aprender a função objetivo e o outro par aprender as restrições, convergem a uma taxa de O (1 sobre raiz de T) que nos permitem produzir soluções tão boas quanto as ótimas em poucas observações. Finalmente, mostramos a eficácia e possíveis aplicações de nossos métodos em um amplo estudo computacional. / [en] We demonstrate how to learn the objective function and constraints of optimization problems while observing its optimal solution over multiple rounds. Our approach is based on Online Learning techniques and works for linear objective functions under arbitrary feasible sets by generalizing previous work. The two algorithms, one to learn objective function and other to learn constraints, converge at a rate of O (1 on t root) that allow us to produce solutions as good as the optimal in a few observations. Finally, we show the efficacy and possible applications of our methods in a significant computational study.