Det moraliska i ansvar inom omvårdnad är en viktig del för vårdpersonal som är bristfälligt beforskat och diskuterat inom vårdvetenskapen. Moraliskt ansvar anses som självklart av vårdpersonal, därför reflekteras begreppet i denna humanvetenskapliga litteraturstudie för att få en djupare kunskap och förståelse. Syftet var att gestalta det moraliska i begreppet ansvar inom omvårdnad i nutid. Inspiration genom hermeneutiken användes för att tolka de åtta vetenskapliga artiklarna som ingick i resultatet. Studiens resultat påvisade en fördjupning om moraliskt ansvar där den fria viljan, determinism, skuld och moralisk agent framkom. Vårdpersonal är moraliska agenter där determinismen, den fria viljan och skulden påverkar det moraliska i ansvaret. Evidensbaserad forskning om det moraliska i ansvaret inom vårdvetenskapen är bristfällig. Ytterligare forskning inom området är önskvärt för att förtydliga begreppets position inom teoribildning och därmed profession och arbete.

abstract: Although women of color have increased their presence in the workplace, many obstacles restricting career opportunities still exist. It is important that mental health professionals contribute in providing interventions to increase career opportunities for women of color. The purpose of this research is to add to the repertoire of interventions by studying the irrational career beliefs of Black women. This research utilizes the Believe It! program, an online career development program that focuses on altering irrational/maladaptive career beliefs that can prevent young females from pursuing career opportunities. An early study of Believe It! found it to be effective for Caucasian females, however the effects for minority females were less clear. The current study re-examined the effectiveness of Believe It! for minorities by altering the appearance of the animated character within the program. It was hypothesized that young African American women interacting with African American animated agents would display greater rationality in terms of career beliefs compared to young African American women interacting with Caucasian animated agents. Forty-four African American girls between the ages of eleven to fifteen were pre-tested with a battery of assessment devices addressing the irrationality of the girls' career beliefs. The measures included the Career Myths Scale, the Career Beliefs Inventory, the Occupational Sex-role Questionnaire, and the Believe It! measure. Four to eight days later, participants engaged in the online Believe It! Program; they were randomly assigned to either a matched condition (viewing the program with an African American animated agent) or a mismatched condition (viewing the program with a Caucasian animated agent). After completion of the intervention, participants were post-tested with the same assessment battery. MANCOVA and ANCOVA analyses showed that participants in the matched condition consistently benefitted from the matched intervention. Implications for this research are discussed. / Dissertation/Thesis / M.C. Counseling 2010

Utilizing game theory, learning automata and reinforcement learning concepts, thesis presents a computational model (simulation) based on general equilibrium theory and classical monetary model. Model is based on interacting Constructively Rational agents. Constructive Ratio- nality has been introduced in current literature as machine learning based concept that allows relaxing assumptions on modeled economic agents information and ex- pectations. Model experiences periodical endogenous crises (Fall in both production and con- sumption accompanied with rise in unemployment rate). Crises are caused by firms and households adopting to a change in price and wage levels. Price and wage level adjustments are necessary for the goods and labor market to clear in the presence of technological growth. Finally, model has good theoretical background and large potential for further de- velopment. Also, general properties of games of learning entities are examined, with special focus on sudden changes (shocks) in the game and behavior of game's play- ers, during recovery from which rigidities can emerge. JEL Classification D80, D83, C63, E32, C73, Keywords Learning, Information and Knowledge, Agent-based, Reinforcement learning, Business cycle, Stochastic and Dynamic Games, Simulation, Modeling Author's e-mail...

Covert channel attacks utilize shared resources to indirectly transmit sensitive information to unauthorized parties. Many current operating systems, such as SELinux, rely on generating labels based on a file's security classification and system-wide security policies and then binding these labels to all such files in the filesystem. Enforcement of security policies in such systems occurs at the time of access to a file or resource. Such mechanisms are flawed, however, in that they do not adequately protect against information laundering by means of covert channels. One recent development, Colored Linux, serves as an extension to SELinux and utilizes watermarking algorithms to "color" the contents of files with their respective security classification in order to enhance resistance to information laundering attacks. In this thesis, a mobile agent-based approach to implementing Colored Linux is proposed to automate the process of detecting and coloring receptive hosts' filesystems and to provide monitoring of the colored filesystem for instances of potential information leakage. Implementation details and execution results are included to illustrate the merits of the proposed approach. An evaluation of the performance of this agent-based system is conducted over a single host as well as a local network of machines and detailed here as well. Finally, third-party analysis of the agent system using formal methods is discussed.

Multi-agent systems rely heavily on coordination and cooperation to achieve a variety of tasks. It is often assumed that these agents will be fully cooperative, or have reliable and equal performance among group members. Instead, we consider cooperation as a spectrum of possible interactions, ranging from performance variations within the group to adversarial agents. This thesis examines several scenarios where cooperation and performance are not guaranteed. Potential applications include sensor coverage, emergency response, wildlife management, tracking, and surveillance. We use geometric methods, such as Voronoi tessellations, for design insight and Lyapunov-based stability theory to analyze our proposed controllers. Performance is verified through simulations and experiments on a variety of ground and aerial robotic platforms. First, we consider the problem of Voronoi-based coverage control, where a group of robots must spread out over an environment to provide coverage. Our approach adapts online to sensing and actuation performance variations with the group. The robots have no prior knowledge of their relative performance, and in a distributed fashion, compensate by assigning weaker robots a smaller portion of the environment. Next, we consider the problem of multi-agent herding, akin to shepherding. Here, a group of dog-like robots must drive a herd of non-cooperative sheep-like agents around the environment. Our key insight in designing the control laws for the herders is to enforce geometrical relationships that allow for the combined system dynamics to reduce to a single nonholonomic vehicle. We also investigate the cooperative pursuit of an evader by a group of quadrotors in an environment with no-fly zones. While the pursuers cannot enter the no-fly zones, the evader moves freely through the zones to avoid capture. Using tools for Voronoi-based coverage control, we provide an algorithm to distribute the pursuers around the zone's boundary and minimize capture time once the evader emerges. Finally, we present an algorithm for the guaranteed capture of multiple evaders by one or more pursuers in a bounded, convex environment. The pursuers utilize properties of the evader's Voronoi cell to choose a control strategy that minimizes the safe-reachable area of the evader, which in turn leads to the evader's capture.

For over 40 years archaeologists have been using predictive modelling to locate archaeological sites. While great strides have been made in the theory and methods of site predictive modelling there are still unresolved issues like a lack of theory, poor data, biased datasets and poor accuracy and precision in the models. This thesis attempts to address the problems of poor model performance and lack of theory driven models through the development of a new method for predictive modelling, agent based modelling. Applying GIS and agent based modelling tools to a project area in southeaster New Mexico this new methodology explored possible behaviours that resulted in site formation such as access to water resources, travel routes and resource exploitation. The results in regards to improved accuracy over traditional methods were inconclusive as a data error was found in the previously created predictive models for the area that were to be used as a comparison. But, the project was more successful in providing explanatory reasons for site placement based on the models created. This work has the potential to open up predictive modelling to wider archaeology audiences, such as those based at universities. Additional findings also impacted other areas of archaeological investigation outside of predictive modelling, such as least cost path analyses and resource gathering analyses.

Three different multi-agent models are presented in this thesis, each with a different goal. The first model investigates the possible role migratory routes may have on song evolution and revolution. The second model investigates what social networks could theoretically facilitate song sharing in a population of whales. The third model implements a formal grammar algorithm in order to investigate how the hierarchal structure of the song may affect song evolution. Finally, the thesis attempts to reconnect the models with their origins and discusses how these models could potentially be adapted for composing music. Through the development of these different models, a number of findings are highlighted. The first model reveals that feeding ground sizes may be key locations where song learning from other population may be facilitated. The second model shows that small world social networks facilitate a high degree of agents converging on a single song, similar to what is observed in wild populations. The final model shows that the ability to recognise hierarchy in a sequence coupled with simple production errors, can lead to songs gradually changing over the course of time, while still retaining their hierarchal structure.

This overview report comprises two projects linked by the theme of the application of intelligent agents. The first project covers the development of RAPIDO, a rapid prototyping toolkit for the development and evaluation of agent applications. The papers and technical reports included for this project look at the implementation and application of the RAPIDO toolkit to a specific test case for evaluation purposes. The second project considers the use of an agent-based simulation of the Internet, called WebAgent, to explore agent-based solutions to improving network performance. The publications present the experimental methodology of WebAgent, and the results of evaluating Expl, an adaptive agent for intelligent control of dynamic caching strategies for web servers and clients. The knowledge gained during the course of these two projects has been published in refereed papers included within the accompanying portfolio. The production of Multi- Agent Systems (MAS), particularly Distributed Artificial Intelligence (DAI) applications, is dependent upon the underlying paradigm. The generic approach to the specification of agent applications, and the object-oriented rapid prototyping technique, have allowed different implementations of a specific problem domain to be evaluated in order to determine the best architecture. The application of agent-based simulation to the field of web document caching has both introduced a new tool for performing evaluations of such techniques and has helped towards the proposal of a new approach, based upon intelligent, adaptive agents. The future of such directions promises to offer far greater application of these ideas to the regulation and management of networks in general.

Motivated by shortcomings in recent natural disaster responses; this paper reports on a computational approach that offers techniques for matching social demands of a disaster type with the strengths of cultural traits among rescue teams.

Large lahars threaten communities living near volcanoes all over the world. Evacuations are a critical strategy for reducing vulnerability and mitigating a disaster. Hazard perceptions, transportation infrastructure, and transportation mode choice are all important factors in determining the effectiveness of an evacuation. This research explores the effects of population, whether individuals drive or walk, response time, and exit closures on an evacuation in a community threatened by a large lahar originating on Mount Rainier, Washington. An agent-based model employing a co-evolutionary learning algorithm is used to simulate a vehicular evacuation. Clearance times increase when the population is larger and when exits are blocked. Clearance times are reduced when a larger proportion of agents opt out of driving, and as the model learns. Results indicate evacuation times vary greatly due to spatial differences in the transportation network, the initial population distribution, and individual behaviors during the evacuation.