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In military training contexts, fast and long term decisions are intermixed where survival and security are prioritized. Simulation-based training, here applied to ground patrols in Afghanistan, can provide preparation for mission critical and life critical operations prior to exposure to real danger. Optimising the effectiveness of simulation-based training raises the need for more detailed representations of the competences required, both for simulation design and for evaluating simulation effectiveness. These needs are here considered in terms of three research questions . The first research question asks how objects trigger dialogue in observational tasks. Eye gaze tracking and recorded dialogue provide a foundation for proposing the cognitive operational structures behind how objects and dialogue are structured when people work together when collaborating in simulation-based training sessions. The objects are tracked along with related observational tasks and the communication between people in a team in ground vehicles and in the Tactical Operations Centre (TOC). The second research question asks how the results of simulation-based training for emergency situations can be described and evaluated. The last research question asks how debriefing and learning create and refine cognitive comprehension, the competency developed in a group. Low level visual cognition in a tactical environment is explored using an eye gaze tracking system integrated with a simulation environment. The integrated system has been evaluated, its accuracy characterized, and the system was then used to evaluate hypotheses related to visual queuing and target selection. The research questions are then explored more broadly based upon two exploratory field studies of simulation-based training sessions held for military staff before leaving for ISAF in Afghanistan. Study methods here include eye gaze tracking, video and audio recording, behavioral observation and retrospective questions. The field studies were conducted at the Swedish Life Guard Regiment sub-departments: International Training Unit(IntUtbE), pre-deployment training for Peace support operations, and Swedish Armed Forces International Centre (SWEDINT), with their Simulation, Modeling and Practical Platform. Based upon data obtained in the field studies, cognitive models of decision processes involved in operational task performance are developed to provide a basis for answering the research questions. Cognitive modelling begins with the Belief, Desire and Intension (BDI) model. This model is then modified in several steps to cover different levels of decision making revealed by the field studies, including an intrapersonal and organizational layer, an educational layer, a layer where objects are build into the algorithm as a basis for purposive behavior, and finally a team competency layer built largely during debriefing sessions. These models can be used to evaluate simulation-based training effectiveness, to provide feedback both in real time and retrospectively to trainees and teams, and potentially could be used in operational systems to provide real-time information about individual and group state during operations, for decision enhancement, and potentially as elements of the implementation of automated operational forces.

In order to cope with the challenges in industry today, such as changes in product diversity and production volume, manufacturing companies are forced to react more flexibly and swiftly. Furthermore, in order for them to survive in an ever-changing market, they also need to be highly competitive by achieving near optimal efficiency in their operations. Production scheduling is vital to the success of manufacturing systems in industry today, because the near optimal allocation of resources is essential in remaining highly competitive. The overall aim of this study is the advancement of research in manufacturing scheduling through the exploration of more effective approaches to address complex, real-world manufacturing flow shop problems. The methodology used in the thesis is in essence a combination of systems engineering, algorithmic design and empirical experiments using real-world scenarios and data. Particularly, it proposes a new, web services-based, industrial scheduling system framework, called OPTIMISE Scheduling System (OSS), for solving real-world complex scheduling problems. OSS, as implemented on top of a generic web services-based simulation-based optimisation (SBO) platform called OPTIMISE, can support near optimal and real-time production scheduling in a distributed and parallel computing environment. Discrete-event simulation (DES) is used to represent and flexibly cope with complex scheduling problems without making unrealistic assumptions which are the major limitations of existing scheduling methods proposed in the literature. At the same time, the research has gone beyond existing studies of simulation-based scheduling applications, because the OSS has been implemented in a real-world industrial environment at an automotive manufacturer, so that qualitative evaluations and quantitative comparisons of scheduling methods and algorithms can be made with the same framework. Furthermore, in order to be able to adapt to and handle many different types of real-world scheduling problems, a new hybrid meta-heuristic scheduling algorithm that combines priority dispatching rules and genetic encoding is proposed. This combination is demonstrated to be able to handle a wider range of problems or a current scheduling problem that may change over time, due to the flexibility requirements in the real-world. The novel hybrid genetic representation has been demonstrated effective through the evaluation in the real-world scheduling problem using real-world data.

Introduction: Simulation-based learning is a teaching methodology, which provides the students with a learning opportunity in an environment that simulates the clinical settings, where mistakes and learning can happen without any risk of patient harm. However, despite of its importance, there is a lack of empirical studies about simulation-based learning in the United Arab Emirates. Aim: To explore and describe the perceptions of the nurse educators regarding simulation-based learning in the undergraduate nursing education and the hindering and facilitating factors for effective simulation-based learning in the United Arab Emirates in order to develop strategies for effective simulation-based learning. Research design: A complex mixed method design. Methods: The study design comprised four phases. • Phase one: qualitative exploratory, descriptive, and contextual design, in which purposive sampling was used to collect data from 18 nurse educators working in two nursing colleges in the United Arab Emirates through individual interviews. Data was analysed using thematic analysis with the assistance of NVivo QSR software version 12. • Phase two is a descriptive quantitative design, in which sampling for the entire population was used to collect data from 45 nurse educators working in two nursing colleges in the United Arab Emirates using a modified Simulation Culture Organizational Readiness Survey. Data was analysed with the assistance of the Statistical Package for Social Sciences Software using descriptive and inferential statistics. • Phase three is meta-inferences: where mixing of data from both phase one and phase two was done to guide the process of phase four. The first three phases represent the exploratory sequential mixed method. • Phase four is a modified e-Delphi technique: Purposive sampling was used among academic leaders and the nurse educators in the two nursing colleges, to develop a consensus on the strategies for effective simulation-based learning in the undergraduate nursing education. In this phase, data was collected through emails and online questionnaires. One hundred percent agreement on the proposed strategies granted, which indicated that the participants had reached the consensus. Results and conclusion: Based on the nurse educators and academic leaders’ needs and perceptions, the SBL strategic recommendation developed, these recommendations that are aligned with the international simulation-based learning recommendation and practices. There was evidence between the perceived influencing factors and the utilization of simulation-based learning in the nursing colleges. This study evolved in many implications and recommendation in regard to the strategies for effective simulation-based learning to benefit nursing education. Key terms / concepts: Simulation, Simulation-based learning, Undergraduate nursing education, Strategies, Nurse educators, Effectiveness, Academic leaders. / Thesis (PhD)--University of Pretoria, 2021. / Nursing Science / PhD / Unrestricted

A Department of Defense (DoD) M&S education task force is in the process of studying the Modeling and Simulation (M&S) education of the acquisition workforce. Historically, DoD acquisition workforce education is not referred to as education, but rather what the Defense Acquisition University (DAU) refers to as "practitioner training, career management, and services." The DAU is the organization primarily responsible for training the DoD acquisition corps in conjunction with service schools and strategic partners in the civilian sector. DAU programs primarily focus on program management, contracting, and management of logistics across the system life cycle. Further, the examples and cases used in the training are primarily DoD centric. Only select DoD employees are exposed to Harvard Business School (HBS) perspectives. The use of M&S to improve system acquisition is only delivered in three courses. Further, Simulation-Based Acquisition (SBA) as a strategy in development of various systems is not explicitly taught. The general notion for this research is that exposure of actual or potential defense acquisition students to the rich civilian literature on M&S across the enterprise life cycle and SBA in particular may be beneficial to DoD. To further this general notion, this research investigates content in courses whose curriculum, while still more than 50% DoD, contains HBS SBA and other M&S related case studies. While abbreviated for the purpose of this abstract, the overall hypothesis of this dissertation is that M&S and HBS case studies make a positive contribution to DoD or potential DoD employees. To investigate this hypothesis, this research conducted both internal and external evaluations to determine the level to which the course makes a positive contribution to the student ability to "Understand the concepts of SBA across the entire program life cycle, in order to reduce the time, resources, and risks associated with the acquisition pr This was identified by the task force as a key element in the Education Skills Requirement (ESR) that this curriculum intends to address. The internal evaluation used inferential statistics to consider the validity of the course topics, content, evaluation methods, and case study delivery method through student evaluations of a live class. Among other variables, this research tracks class participants' responses (self-assessment) and performance (subject matter expert objective assessment) demographically to include current and potential DoD employees. With the graying of DoD workforce, potential DoD employees are important to the DoD community too. The external evaluation likewise considers the validity of the course topics and content through a survey of acquisition professionals external to the class. External acquisition professionals are drawn from across DoD as well as include former DoD acquisition employees. The combination of the internal and external evaluations provides insight into these and other issues related to the course topics, content, evaluation methods, and case study delivery methods and make recommendations on these and other issues for future course offerings.

Learning and planning are two fundamental problems in artificial intelligence. The learning problem can be tackled by reinforcement learning methods, such as temporal-difference learning, which update a value function from real experience, and use function approximation to generalise across states. The planning problem can be tackled by simulation-based search methods, such as Monte-Carlo tree search, which update a value function from simulated experience, but treat each state individually. We introduce a new method, temporal-difference search, that combines elements of both reinforcement learning and simulation-based search methods. In this new method the value function is updated from simulated experience, but it uses function approximation to efficiently generalise across states. We also introduce the Dyna-2 architecture, which combines temporal-difference learning with temporal-difference search. Whereas temporal-difference learning acquires general domain knowledge from its past experience, temporal-difference search acquires local knowledge that is specialised to the agent's current state, by simulating future experience. Dyna-2 combines both forms of knowledge together. We apply our algorithms to the game of 9x9 Go. Using temporal-difference learning, with a million binary features matching simple patterns of stones, and using no prior knowledge except the grid structure of the board, we learnt a fast and effective evaluation function. Using temporal-difference search with the same representation produced a dramatic improvement: without any explicit search tree, and with equivalent domain knowledge, it achieved better performance than a vanilla Monte-Carlo tree search. When combined together using the Dyna-2 architecture, our program outperformed all handcrafted, traditional search, and traditional machine learning programs on the 9x9 Computer Go Server. We also use our framework to extend the Monte-Carlo tree search algorithm. By forming a rapid generalisation over subtrees of the search space, and incorporating heuristic pattern knowledge that was learnt or handcrafted offline, we were able to significantly improve the performance of the Go program MoGo. Using these enhancements, MoGo became the first 9x9 Go program to achieve human master level.

When we survey hardware design groups, we can find that it is now dedicated to verification between 60 to 80 percent. According to the instruction set architecture information should be a feasible and reasonable way for generating the test pattern to verify the function of a microprocessor. In this these, we¡¦ll present an instruction test pattern (for microprocessors) generation method based on the instruction set architecture. It can help the users to generate the instruction test pattern efficiently. The generation flow in this thesis contains three major flows: individual instruction, instruction pair, and manual generation. They are used for different verification cases. The ¡§individual instruction¡¨ could be used for verifying the functions of each implemented instructions. The ¡§instruction pair¡¨ could be used for verifying the interaction of instruction execution in a pipeline for a HDL implementation of a microprocessor. The ¡§manual generation¡¨ could be used to verify some corner cases (behaviors) of the microprocessor. As the quality of our test pattern, we generate some patterns for 32-bits instruction (ARM instruction sets and SPARC instruction sets) and use them to verify a synthesizable RTL core. With some handwriting test pattern (34.7%), our automatic generation method can approach 100% HDL code coverage of the microprocessor design. We use the HDL code coverage as the reference of test pattern quality. Because our generation method is based on the instruction field, we can describe most instruction set for the generator. Hence, our generation method can retarget to most instruction set architecture without modifying the generator. Besides the RISC instructions, even the CISC instructions could be generated.

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The purpose of this work is to provide simulation-based support for making optimal (or near-optimal) decisions in situations where decision makers are faced with imperfect information. We develop several novel techniques and algorithms for simulation-based optimization and decision support and apply them to two categories of problems: (i) Unmanned Aerial Vehicle (UAV) path planning in search operations, and; (ii) optimization of business process models. Common features of these two problems for which analytical approaches are not available, are the presence of imperfect information and their inherent complexity. In the UAV path planning problem, the objective is to define the path of a UAV searching for a target on a known road network. It is assumed that the target is moving toward a goal and we have some uncertain information about the start point of the target, its velocity, and the final goal of the target. The target does not take evasive action to avoid being detected. The UAV is equipped with a sensor, which may detect the target once it is in the sensor’s scope. Nevertheless, the detection process is uncertain and the sensor is subject to both false-positive and false-negative errors. We propose three different solutions, two of which are simulation-based. The most promising solution is an on-line simulation-based method that estimates the location of the target by using a Sequential Monte Carlo (SMC) method. During the entire mission, different UAV paths are simulated and the one is chosen that most reduces the uncertainty about the location of the target. In the optimization of the business process models, several different but related problems are addressed: (i) we define a measure of performance for a business process model based on the value added by agents (employees) to the process; (ii) we use this model for optimization of the business process models. Different types of processes are distinguished and methods for finding the optimal or near-optimal solutions are provided; (iii) we propose a model for estimating the performance of collaborative agents. This model is used to solve a class of Assignment Problems (AP), where tasks are assigned to collaborative agents; (iv) we propose a model for team activity and the performance of a team of agents. We introduce different collaboration strategies between agents and a negotiation algorithm for resolving conflicts between agents. We compare the effect of different strategies on the output of the team. Most of the studied cases are complex problems for which no analytical solution is available. Simulation methods are successfully applied to these problems. They are shown to be more general than analytical models for handling uncertainty since they usually have fewer assumptions and impose no restrictions on the probability distributions involved. Our investigation confirms that simulation is a powerful tool for providing decision-making support. Moreover, our proposed algorithms and methods in the accompanying articles contribute to providing support for making optimal and in some cases near-optimal decisions: (i) our tests of the UAV simulation-based search methods on a simulator show that the on-line simulation method has generally a high performance and detects the target in a reasonable time. The performance of this method was compared with the detection time when the UAV had the exact information about the initial location of the target, its velocity, and its path (minimum detection time). This comparison indicated that the online simulation method in many cases achieved a near-optimal performance in the studied scenario; (ii) our business process optimization framework combines simulation with the Hungarian method and finds the optimal solution for all cases where the assignment of tasks does not change the workflow of the process. For the most general cases, where the assignment of tasks may change the workflow, we propose an algorithm that finds near-optimal solutions. In this algorithm, simulation, which deals with the uncertainty in the process, is combined with the Hungarian method and hill-climbing heuristics. In the study of assigning tasks to collaborative agents we suggest a Genetic Algorithm (GA) that finds near-optimal solutions with a high degree of accuracy, stability, scalability and robustness. While investigating the effect of different agent strategies on the output of a team, we find that the output of a team is near-optimal, when agents choose a collaboration strategy that follows the principle of least effort (Zipf’s law) and use our suggested algorithm for negotiation and resolving conflicts. / QC 20111202

Background: Simulation-based education (SBE) has been shown to be an effective and reproducible learning tool. SBE is used widely internationally. The current state of SBE in South Africa is unknown. To the best of our knowledge this is the first survey that describes the use and attitudes towards SBE within South Africa. Methods: An online survey tool was distributed by email to: i) the South African Society of Anaesthesiologists (SASA) members; and ii) known simulation education providers in South Africa. The respondents were grouped into anaesthesia and non-anaesthesia participants. Descriptive statistics were used to analyse the data. Ethics approval was obtained: HREC REF 157/2017. Results: The majority of the respondents provide SBE and integrate it into formal teaching programmes. There is a will amongst respondents to grow SBE in South Africa, with it being recognised as a valuable educational tool. The user groups mainly targeted by SBE, were undergraduate students, medical interns, registrars and nurses. Learning objectives targeted include practical skills, medical knowledge, critical thinking and integrated management. Amongst anaesthesia respondents: the tool most commonly used to assess the quality of learner performance during SBE, for summative assessment, was ‘expert opinion’ (33%); the most frequent methods of evaluating SBE quality were participant feedback (42%) and peer evaluation (22%); the impact of SBE was most frequently assessed by informal discussion (42%) and learner feedback (39%). In anaesthesia SBE largely takes place within dedicated simulation facilities on site (47%). Most respondents report access to a range of SBE equipment. The main reported barriers to SBE were: finance, lack of trained educators, lack of equipment and lack of protected time. A limited number of respondents report engaging in SBE research. There is a willingness in both anaesthesia and non-anaesthesia groups (96% and 89% respectively) to collaborate with other centres. Conclusion: To the best of our knowledge this publication provides us with the first cross sectional survey of SBE in anaesthesia and a selection of non-anaesthetic respondents within South Africa. The majority of respondents indicate that SBE is a valuable education tool. A number of barriers have been identified that limit the growth of SBE within South Africa. It is hoped that with a commitment to ongoing SBE research and evaluation, SBE can be grown in South Africa.