The increasing interdependence of economies and the recent economic crisis has considerably strengthened the importance of exporting. It is recognised as promoting the survivability of companies as they are better able to diversify risks and generate multiple income streams. Thus, investigation of the determinants of export performance has become particularly important. Marketing decision-making has been identified as one of the core drivers of firms success. It is a process under the direct control of managers where significant changes can be introduced to improve it, and by extension, the ability to achieve successful outcomes. However, little is known about how export marketing decisions are made and what key decision-making approaches managers rely on to drive their performance. A literature review that span multiple disciplines (e.g. strategic management, organisation studies, marketing) helped to disentangle two key decision-making approaches, namely planning and improvisation. This is the first study examining the impact of both of these simultaneously on a firm s export performance. While planning is considered to be a unidimensional construct, improvisation is comprised of three facets: spontaneity, creativity and action-orientation. Based on decision theory, this research was conducted in two phases. The literature review informed phase 1: a qualitative exploratory study among export managers in the UK. A conceptual model was then derived from the results and tested in phase 2 through quantitative analysis utilising data generated from 200 respondent companies via a self-reported online questionnaires and the application of structural equation modelling. The results indicated that export customer performance was negatively affected by planning and positively influenced by action-orientation, whilst export financial performance was found to benefit from planning. All decision-making approaches (planning, spontaneity, creativity and action-orientation) were found to be positively related to responsiveness to environmental changes. Using moderator analysis, important insights were uncovered into combining decision-making approaches. The export function was found to benefit from a combination of planning and action-orientation, whereas spontaneity and creativity while having separate positive effects are not well combined with planning, producing negative moderation effects.

PhD / This thesis is concerned with planning and acting under uncertainty in partially-observable continuous domains. In particular, it focusses on the problem of mobile robot navigation given a known map. The dominant paradigm for robot localisation is to use Bayesian estimation to maintain a probability distribution over possible robot poses. In contrast, control algorithms often base their decisions on the assumption that a single state, such as the mode of this distribution, is correct. In scenarios involving significant uncertainty, this can lead to serious control errors. It is generally agreed that the reliability of navigation in uncertain environments would be greatly improved by the ability to consider the entire distribution when acting, rather than the single most likely state. The framework adopted in this thesis for modelling navigation problems mathematically is the Partially Observable Markov Decision Process (POMDP). An exact solution to a POMDP problem provides the optimal balance between reward-seeking behaviour and information-seeking behaviour, in the presence of sensor and actuation noise. Unfortunately, previous exact and approximate solution methods have had difficulty scaling to real applications. The contribution of this thesis is the formulation of an approach to planning in the space of continuous parameterised approximations to probability distributions. Theoretical and practical results are presented which show that, when compared with similar methods from the literature, this approach is capable of scaling to larger and more realistic problems. In order to apply the solution algorithm to real-world problems, a number of novel improvements are proposed. Specifically, Monte Carlo methods are employed to estimate distributions over future parameterised beliefs, improving planning accuracy without a loss of efficiency. Conditional independence assumptions are exploited to simplify the problem, reducing computational requirements. Scalability is further increased by focussing computation on likely beliefs, using metric indexing structures for efficient function approximation. Local online planning is incorporated to assist global offline planning, allowing the precision of the latter to be decreased without adversely affecting solution quality. Finally, the algorithm is implemented and demonstrated during real-time control of a mobile robot in a challenging navigation task. We argue that this task is substantially more challenging and realistic than previous problems to which POMDP solution methods have been applied. Results show that POMDP planning, which considers the evolution of the entire probability distribution over robot poses, produces significantly more robust behaviour when compared with a heuristic planner which considers only the most likely states and outcomes.

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PhD / This thesis is concerned with planning and acting under uncertainty in partially-observable continuous domains. In particular, it focusses on the problem of mobile robot navigation given a known map. The dominant paradigm for robot localisation is to use Bayesian estimation to maintain a probability distribution over possible robot poses. In contrast, control algorithms often base their decisions on the assumption that a single state, such as the mode of this distribution, is correct. In scenarios involving significant uncertainty, this can lead to serious control errors. It is generally agreed that the reliability of navigation in uncertain environments would be greatly improved by the ability to consider the entire distribution when acting, rather than the single most likely state. The framework adopted in this thesis for modelling navigation problems mathematically is the Partially Observable Markov Decision Process (POMDP). An exact solution to a POMDP problem provides the optimal balance between reward-seeking behaviour and information-seeking behaviour, in the presence of sensor and actuation noise. Unfortunately, previous exact and approximate solution methods have had difficulty scaling to real applications. The contribution of this thesis is the formulation of an approach to planning in the space of continuous parameterised approximations to probability distributions. Theoretical and practical results are presented which show that, when compared with similar methods from the literature, this approach is capable of scaling to larger and more realistic problems. In order to apply the solution algorithm to real-world problems, a number of novel improvements are proposed. Specifically, Monte Carlo methods are employed to estimate distributions over future parameterised beliefs, improving planning accuracy without a loss of efficiency. Conditional independence assumptions are exploited to simplify the problem, reducing computational requirements. Scalability is further increased by focussing computation on likely beliefs, using metric indexing structures for efficient function approximation. Local online planning is incorporated to assist global offline planning, allowing the precision of the latter to be decreased without adversely affecting solution quality. Finally, the algorithm is implemented and demonstrated during real-time control of a mobile robot in a challenging navigation task. We argue that this task is substantially more challenging and realistic than previous problems to which POMDP solution methods have been applied. Results show that POMDP planning, which considers the evolution of the entire probability distribution over robot poses, produces significantly more robust behaviour when compared with a heuristic planner which considers only the most likely states and outcomes.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xiv, 184 p.; also includes graphics. Includes bibliographical references (p. 153-184). Available online via OhioLINK's ETD Center

This master thesis is devoted to the elements and principles of management. The master thesis deals with the management of field workers of a selected company and subsequent optimization of this procedure. The main aim of master thesis is to optimize the selected decision problem. Based on the analysis of field workers activities is determined significant managerial decision problem. To solve this decision problem is compiled mathematical model and the individual alternative solutions are determined. Based on the individual steps of managerial decision the final recommendations of variant for solving this decision problem is made. The work is devided into two parts, theoretical and practical part. In the theoretical part we acquaint ourselves with the problems with the help of scientific literature. The practical part deals with the use of the scientific literature in practice in order to optimize decision-making problem and management field workers of a selected company.

<p dir="ltr">Vehicle weaving describes the lane-changing actions of vehicles, which is a critical aspect of traffic management and road design. This study focused on the weaving behavior of vehicles occurring between ramp merge and diverge areas. Weaving in these areas causes congestion and increases the risk of accidents, especially during heavy traffic. Redesigning such areas for enhanced safety requires a comprehensive analysis of the traffic conditions. Obtaining the weaving pattern is a challenge in the traffic industry. To address this challenge, we leveraged AI and image processing technology to develop algorithms for quantitative analysis of weaving using surveillance videos at the consecutive ramp merge and diverge areas. This approach can also determine the weaving patterns of passenger cars and trucks respectively. The experimental results captured the lane-based weaving behavior of around 30% of vehicles in the favorable areas. The captured weaving data is used as weaving data samples to derive an overall analysis of a weaving location. Remarkably, our approach can reduce the manual processing time for weaving analysis by more than 90%, making this highly practical for use.</p>

Autonomous driving requires tactical decision-making while navigating in a dynamic shared space environment. The complexity and uncertainty in this process arise due to unknown and tightly-coupled interaction among traffic users. This thesis work formulates an unknown navigation problem as a Markov decision process (MDP), supported by models of traffic participants and userspace. Instead of modeling a traditional MDP, this work formulates a Multi-policy decision making (MPDM) in a shared space scenario with pedestrians and vehicles. The employed model enables a unified and robust self-driving of the ego vehicle by selecting a desired policy along the pre-planned path. Obstacle avoidance is coupled within the navigation module performing a detour off the planned path and obtaining a reward on task completion and penalizing for collision with others. In addition to this, the thesis work is further extended by analyzing the real-time constraints of the proposed model. The performance of the implemented framework is evaluated in a simulation environment on a typical construction (quarry) scenario. The effectiveness and efficiency of the elected policy verify the desired behavior of the autonomous vehicle.

本研究嘗試從管理的規劃與決策面來探究創意的重要性，並以個案研究法探討個案對象國民戲劇創意人－吳念真，以瞭解此類型創意人的創意來源組成。在研究架構的選擇上，則是修正學者Amabile於1996年提出的創意要素架構(componential framework of creativity)，並加入過去的生命經驗(antecedent conditions)等強化創意來源組成的影響性。最後，以此理論架構為研究基礎並使用樣版式分析法進行資料分析。 透過本研究可瞭解，結合民眾情感從事創作的吳念真試圖打破走進戲院的固定族群，並吸引未曾進入戲院看戲的社會中下階層，因此他透過探討一般百姓生活中會遭遇的情景與情感做為作品主軸，讓目標族群在作品中找到共鳴而願意購票觀戲。然而，並非所有的創意人皆能勝任此類型創意素材的創作，他必須要經過長時間的生活歷練與細微的觀察來體驗民眾的情感為何、他必須樂於從一般百姓的口中獲取不同的生命歷程、他更要勇於嘗試這類作品可能面對的市場接受度。而在考量目標族群需求而架構故事的同時，創意人實已無形地選擇故事角色應由什麼樣的人來做詮釋最為真實、舞台設計應以何種走向來呈現故事背景、音樂調性應如何襯托故事的情感表達等。這一切皆是在架構故事雛形的同時所無形做出的配置與設計。 創意成形過程就如同一個專案執行前的規劃，需要選定目標客群、以客群的需求來設計產品，及如何配置執行工作的人員等，而創意成形需要仰賴創意人的創意來源組成元素等才能獲取特定類別的創意作品，因而其重要性及不可取代性更為顯著。

<p>While instance-dependent contextual bandits have been previously studied, their analysis<br> has been exclusively limited to pure disagreement-based learning. This approach lacks a<br> nuanced understanding of disagreement and treats it in a binary and absolute manner.<br> In our work, we aim to broaden the analysis of instance-dependent contextual bandits by<br> studying them under the framework of disagreement-based learning in sub-regions. This<br> framework allows for a more comprehensive examination of disagreement by considering its<br> varying degrees across different sub-regions.<br> To lay the foundation for our analysis, we introduce key ideas and measures widely<br> studied in the contextual bandit and disagreement-based active learning literature. We<br> then propose a novel, instance-dependent contextual bandit algorithm for the realizable<br> case in a transductive setting. Leveraging the ability to observe contexts in advance, our<br> algorithm employs a sophisticated Linear Programming subroutine to identify and exploit<br> sub-regions effectively. Next, we provide a series of results tying previously introduced<br> complexity measures and offer some insightful discussion on them. Finally, we enhance the<br> existing regret bounds for contextual bandits by integrating the sub-region disagreement<br> coefficient, thereby showcasing significant improvement in performance against the pure<br> disagreement-based approach.<br> In the concluding section of this thesis, we do a brief recap of the work done and suggest<br> potential future directions for further improving contextual bandit algorithms within the<br> framework of disagreement-based learning in sub-regions. These directions offer opportuni-<br> ties for further research and development, aiming to refine and enhance the effectiveness of<br> contextual bandit algorithms in practical applications.<br> <br> </p>