Artificial intelligence (AI) is already a fundamental component of computer games. In this context is emotions a growing part in simulating real life. The proposed emotional state module, provides a way for the game agents to select an action in real-time virtual environments. The modules function has been tested with the open-source strategy game ORTS. This thesis proposes a new approach for the design of an interacting network, similar to a spreading activation system, of emotional states that keeps track of emotion intensities changing and interacting over time. The network of emotions can represent any number of persisting states, such as moods, emotions and drives. Any emotional signal can affect every state positively or negatively. The states' response to emotional signals are influenced by the other states represented in the network. The network is contained within an emotional state module. This interactions between emotions are not the focus of much research, neither is the representation model. The focus tend to be on the mechanisms eliciting emotions and on how to express the emotions.

Purpose – Artificial Intelligence has seen important growth in the digital area in recent years. Our aim is to explore possible guidelines that make use of AI advances to design good user experiences for digital products. Method – The proposed methods to gather the necessary qualitative data to support our claim involve open-ended interviews with UX/UI Designers working in the industry, in order to gain a deeper understanding of their thoughts and experiences. In addition, a literature review is conducted to identify the knowledge gap and build the base of our new theory. Findings – Our findings suggest a need to embrace new technological developments in favour of enhancing UX designers’ workflow. Additionally, basic AI and ML knowledge is needed to utilise these capabilities to their full potential. Indeed, a crucial area of impact where AI can augment a designer’s reach is personalization. Together with smart algorithms, designers may target their creations to specific user needs and demands. UX designers even have the opportunity for innovation as mundane tasks are automated by intelligent assistants, which broadens the possibility of acquiring further skills to enhance their work. One result, that is both innovative and unexpected, is the notion that AI and ML can augment a designer’s creativity by taking over mundane tasks, as well as, providing assistance with certain graphics and inputs. Implications – These results indicate that AI and ML may potentially impact the UX industry in a positive manner, as long as designers make use of the technology for the benefit of the user in true human-centred practice. Limitations – Nevertheless, our study presents its own unique limitations due to the scope and time frame of this dissertation, we are bound to the knowledge gathered from a small sample of professionals in Sweden. Presented guidelines are a suggestion based on our research and not a definitive workflow.

Artificiell intelligence (AI) har blivit vanligare att använda för att stödja människor i deras beslutsfattande. Maskininlärning (ML) är ett delområde inom AI som har börjat användas mer inom hälso-och sjukvården. Patientdata ökar inom vården och ett AI-system kan behandla denna ökade datamängd, vilket vidare kan utveckla ett beslutsstöd som hjälper läkarna. AI-tekniken blir vanligare att använda inom radiologin och specifikt inom mammografin som ett beslutsstöd. Användning av AI-teknik inom mammografin medför fördelar men det finns även utmaningar som inte har något med tekniken att göra.Icke-tekniska utmaningar är viktiga att se över för att generera en lyckad praxis. Studiens syfte var därför att undersöka icke-tekniska utmaningar vid användning av AI som ett beslutsstöd inom mammografi ur ett radiologiskt perspektiv. Radiologer med erfarenhet av mammografi intervjuades i syfte att öka kunskapen kring deras syn på användningen.Resultatet från studien identifierade och utvecklade de icke-tekniska utmaningarna utifrån temana: ansvar, mänskliga förmågor, acceptans, utbildning/kunskap och samarbete. Resultatet indikerade även på att inom dessa teman finns icke-tekniska utmaningar med tillhörande aspekter som är mer framträdande än andra. Studien ökar kunskaperna kring radiologers syn på användningen och bidrar till framtida forskning för samtliga berörda aktörer. Forskning kan ta hänsyn till dessa icke-tekniska utmaningar redan innan tekniken är implementerad i syfte att minska risken för komplikationer. / Artificial intelligence (AI) has become more commonly used to support people when making decisions. Machine learning (ML) is a sub-area of AI that has become more frequently used in health care. Patient data is increasing in healthcare and an AI system can help to process this increased amount of data, which further can develop a decision support that can help doctors. AI technology is becoming more common to use in radiology and specifically in mammography, as a decision support. The usage of AI technology in mammography has many benefits, but there are also challenges that are not connected to technology.Non-technical challenges are important to consider and review in order to generate a successful practice. The purpose of this thesis is therefore to review non-technical challenges when using AI as a decision support in mammography from a radiological perspective. Radiologists with experience in mammography were interviewed in order to increase knowledge about their views on the usage.The results identified and developed the non-technical challenges based on themes: responsibility, human abilities, acceptance, education/knowledge and collaboration. The study also found indications within these themes that there are non-technical challenges with associated aspects that are more prominent than others. This study emphasizes and increases the knowledge of radiologists views on the usage of AI and contributes to future research for all the actors involved. Future research can address these non-technical challenges even before the technology is implemented to reduce the risk of complications.

Die Künstliche Intelligenz (KI) mit ihren untergeordneten Forschungsgebieten wie maschinelles Lernen (ML), Spracherkennung oder Robotik ist in aller Munde. Die Leistungsfähigkeit und Stabilität von Anwendungen, die im weiteren Sinne KI zur Aufgabenbearbeitung einsetzen, sind gestiegen und durchdringen die Gesellschaft immer mehr. Weltweit wird die KI als eine Schlüsseltechnologie wahrgenommen, die in den nächsten Jahren weiter an Bedeutung gewinnt (Bitkom, DFKI 2017). So zielt auch die Ausschreibung des Bundesministeriums für Wirtschaft und Energie von 02/2019 darauf ab, KI als Schrittmachertechnologie für „[…] volkswirtschaftlich relevante Ökosysteme“ zu fördern (BMWi 2019). Mit der zunehmenden Ausstattung der Produktionsmittel mit Sensoren und der gleichzeitig steigenden Vernetzung dieser, steigt auch die Menge verfügbarer Daten, die für die Generierung von Wissen genutzt werden können (Fraunhofer 2018). Davon profitiert besonders das ML als Teilgebiet der KI. So unterschiedlich die gewonnenen Daten sind, so unterschiedlich sind die Aufgaben, die innerhalb des Maschinenbaus mit diesen bewältigt werden können. Ziele, die mit dem Einsatz von ML verbunden werden, sind beispielsweise selbst optimierende Produktionssysteme oder die bedarfsgerechte Instandhaltung von Anlagen auf Grund einer möglichst genauen Prognose des Ausfallzeitpunktes der Komponenten. Ebenso wie jede andere Technologie bedarf der Einsatz von ML Ressourcen, die in den Unternehmen nur begrenzt vorhanden sind. Die Entscheidung für oder gegen einen Einsatz von ML in Maschinenbauprodukten ist derzeit ganz klar eine strategische und bedingt die Einbeziehung verschiedener Fachbereiche bis hin zum Management des Unternehmens (Saltz et al. 2017). Daher wird ein strategisches Diskussions- und Entscheidungswerkzeug benötigt, welches ein Projekt aus technologischer und wirtschaftlicher Sicht darstellen und fachübergreifend genutzt werden kann sowie ein strukturiertes Vorgehen ermöglicht. Die Autoren schlagen zur Entscheidungsfindung die Nutzung des hier eingeführten Analysekompetenz-Marktpriorität-Portfolios vor, welches speziell auf die Fragestellung des ML Einsatzes im Maschinenbau zugeschnitten ist. Es werden Bewertungstabellen vorgestellt und deren Nutzung erläutert, welche sich an den zu bearbeitenden Prozessschritten für komplexe Datenanalysen (Shearer 2000, Klement et al. 2018) orientiert. Die Ableitung von Normstrategien wird anhand der finalen Darstellung des Portfolios diskutiert. [... aus der Einleitung]

The rapid development of artificial intelligence (AI) has led to significant changes in the business environment and academic discussions. AI boosts productivity and positively impacts the competitive advantage of organisations. However, it also has its dark sides, such as prejudice, non-transparent processes, and people's fears that AI will be able to take their jobs in the future. The successful implementation of AI in organisations depends on several factors, including geopolitical, cultural, ecosystem, organisational, and individual factors. Geopolitical context and cultural differences can play an important role in the adoption and implementation of AI in organisations. This study examines the influence of geopolitical and cultural contexts on the decision-making process for the adoption and implementation of AI by managers from the fashion retail industry in Sweden and India. Given the extensive scope of these contexts, the authors narrowed their focus on specific factors. In the cultural context, the authors consider selected dimensions of the GLOBE project that reflect national culture. Within the Geopolitical context, particular attention is given to aspects such as data access and control, as well as the regulatory framework. In the course of this study, semi-structured interviews were conducted, and additional secondary data was studied. The study showed that the specifics of data access and control, as well as governmental legislative regulation, directly affect the decision-making process regarding the adoption and implementation of AI. As for the cultural context, here the degree of influence is heterogeneous, and decision-making on the implementation of AI is not always subject to the direct influence of the national cultural factors.

Artificial Intelligence (AI) holds transformative potential for many fields including the finance sector. However, identifying suitable tasks for artificial intelligence implementation remains a challenge. This study proposes the artificial intelligence readiness task assessment tool, empowering finance professionals to assess task suitability for AI implementation from a bottom-up perspective. Artificial intelligence adoption often encounters barriers such as costs, compatibility, and skill gaps. The proposed tool addresses these challenges by allowing finance professionals to gauge artificial intelligence suitability for specific tasks without requiring extensive AI knowledge. The tool follows a design science research approach, ensuring it is user-friendly and effectively addresses real world challenges. The proposed tool is comprised of three sections: task framing, task assessment, and results interpretation. Unlike existing methodologies that focus on organization wide artificial intelligence readiness, the proposed tool centers on task specific readiness. This innovative approach provides practical guidance for finance professionals seeking to leverage artificial intelligence and helps organizations realize the potential of AI more effectively.

Denna studie besvarar vilka möjligheter och hinder som i dagsläget finns för teknologin generativ design (GD) inom företag. Frågeställningen besvarades genom en insamling av befintlig kunskap i form av en litteraturstudie som senare kombinerades med dagens kunskap i branschen genom kvalitativa intervjuer, semi-strukturerade intervjuer, med personer som kommit i kontakt med teknologin. GD:s utveckling på senare år har präglats av framgångar, och kommer oundvikligen att påverka flertalet industrier i framtiden. Det som dock visade sig är att den nuvarande kunskapsnivån är för låg hos företagen vilket gör att de inte kan använda tekniken till sin fulla potential. Detta kombinerat med systemets komplexa lösningar, vars strukturer kräver dyra tillverkningsmetoder, leder ofta till att företag anser att systemet inte är nödvändiga att implementera i dagsläget. För tillfället utnyttjar företag systemen främst för konceptframtagning då de arbetar med banbrytande design. Detta gör även att vissa industrier har större användning av teknologin än andra. Däremot finns en optimism om att teknologin kommer att sprida sig till andra industrier i framtiden. / This study answers what opportunities and obstacles currently exist for the technology generative design (GD) in companies. The question was answered through a collection of existing knowledge in the form of a literature study which was later combined with today's knowledge in the industry through qualitative interviews, semi-structured interviews, with people who have come into contact with the technology. GD's development in recent years has been marked by success, and will inevitably affect most industries in the future. What turned out, however, is that the current level of knowledge is too low at the companies, which means that they cannot use the technology to its full potential. This, combined with the system's complex solutions, whose structures require expensive manufacturing methods, often leads companies to consider that the system is not necessary to implement at the present time. At the moment, companies use the systems primarily for concept development when they work with ground-breaking designs. This also means that some industries have greater use of the technology than others. However, there is optimism that the technology will spread to other industries in the future.

Cloud services are one of the fast expanding fields of today. Companies such as Amazon, Google, Microsoft and IBM offer these cloud services in various forms. As this field progresses, the natural question occurs ”What can you do with the technology today?”. The technology offers scalability for hardware usage and user demands, that is attractive to developers and companies. This thesis tries to examine the applicability of cloud services, by combining it with the question: ”Is it possible to make an automated thesis examiner?” By narrowing down the services to IBM Watson web services, this thesis main question reads ”Is it possible to make an automated thesis examiner using IBM Watson?”. Hence the goal of this thesis was to create an automated thesis examiner. The project used a modified version of Bunge’s technological research method. Where amongst the first steps, a definition of an software thesis examiner for student theses was created. Then an empirical study of the Watson services, that seemed relevant from the literature study, proceeded. These empirical studies allowed a deeper understanding about the services’ practices and boundaries. From these implications and the definition of a software thesis examiner for student theses, an idea of how to build and implement an automated thesis examiner was created. Most of IBM Watson’s services were thoroughly evaluated, except for the service Machine Learning, that should have been studied further if the time resources would not have been depleted. This project found the Watson web services useful in many cases but did not find a service that was well suited for thesis examination. Although the goal was not reached, this thesis researched the Watson web services and can be used to improve understanding of its applicability, and for future implementations that face the provided definition. / Molntjänster är ett av de områden som utvecklas snabbast idag. Företag såsom Amazon, Google, Microsoft och IBM tillhandahåller dessa tjänster i flera former. Allteftersom utvecklingen tar fart, uppstår den naturliga frågan ”Vad kan man göra med den här tekniken idag?”. Tekniken erbjuder en skalbarhet mot använd hårdvara och antalet användare, som är attraktiv för utvecklare och företag. Det här examensarbetet försöker svara på hur molntjänster kan användas genom att kombinera det med frågan ”Är det möjligt att skapa en automatiserad examensarbetesrapportsgranskare?”. Genom att avgränsa undersökningen till IBM Watson molntjänster försöker arbetet huvudsakligen svara på huvudfrågan ”Är det möjligt att skapa en automatiserad examensarbetesrapportsgranskare med Watson molntjänster?”. Därmed var målet med arbetet att skapa en automatiserad examensarbetesrapportsgranskare. Projektet följde en modifierad version av Bunge’s teknologiska undersökningsmetod, där det första steget var att skapa en definition för en mjukvaruexamensarbetesrapportsgranskare följt av en utredning av de Watson molntjänster som ansågs relevanta från litteratur studien. Dessa undersöktes sedan vidare i empirisk studie. Genom de empiriska studierna skapades förståelse för tjänsternas tillämpligheter och begränsningar, för att kunna kartlägga hur de kan användas i en automatiserad examensarbetsrapportsgranskare. De flesta tjänster behandlades grundligt, förutom Machine Learning, som skulle behövt vidare undersökning om inte tidsresurserna tog slut. Projektet visar på att Watson molntjänster är användbara men inte perfekt anpassade för att granska examensarbetesrapporter. Även om inte målet uppnåddes, undersöktes Watson molntjänster, vilket kan ge förståelse för deras användbarhet och framtida implementationer för att möta den skapade definitionen.

M. Tech. (Department of Information Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / This research was initiated because of a global increase in congestion on roads and the consequent increase in the rate of fatalities on both national and international roads. Annually, 1.3 million people are killed on the roads globally, and millions are injured. It was estimated that 2.4 million people will be killed in road traffic accidents annually by 2030, and in South Africa, over 14 000 deaths were reported in 2016. A study undertaken by the American Automobile Association Foundation for Traffic Safety (AAAFTS), established in 1947 to conduct research and address growing highway safety issues, found that motorcar accidents, on average, cost the United States $300 billion per annum. In the same vain, the World Health Organisation (WHO) asserted in their 2013 Global Status Safety Report on Road Safety that by 2020, traffic accidents would become the third leading cause of death globally. In this organisation’s 2015 report, South Africa was listed as having one of the highest road fatality rates in the world, averaging 27 out of 100 000 people. Cognisance of these statistics that describe wanton loss of life and serious economic implications, among other reasons, led to the development of autonomous vehicles (AVs), such as Google and Uber’s driverless taxis and Tesla’s autonomous vehicle. Companies have invested in self-driving prototypes, and they bolster this investment with continuous research to rectify imperfections in the technologies and to enable the implementation of AVs on conventional roads. This research aimed to address issues surrounding the systems communication concept, and focused on a novel method of the routing facet of AVs by exploring the mechanisms of the virtual system of packet switching and by applying these same principles to route autonomous vehicles. This implies that automated vehicles depart from a source address and arrive at a pre-determined destination address in a manner analogous to packet switching technology in computer networking, where a data packet is allotted a source and destination address as it traverses the Open Systems Interconnection (OSI) model for open system interconnection prior to dissemination through the network. This research aimed to develop an IoT model that reduces road congestion by means of a cost effective and reliable method of routing AVs and lessen dependency on vehicle-to-vehicle (V2V) communication with their heavy and costly sensor equipment and GPS, all of which under certain conditions malfunction. At the same time, as safety remains the foremost concern, the concept aimed to reduce the human factor to a considerable degree. The researcher demonstrated this by designing a computer-simulated Internet of Things (IoT) model of the concept. Experimental research in the form of a computer simulation was adopted as the most appropriate research approach. A prototype was developed containing the algorithms that simulated the theoretical model of IoT vehicular technology. The merits of the constructed prototype were analysed and discussed, and the results obtained from the implementation exercise were shared. Analysis was conducted to verify arguments on assumptions to clarify the theory, and the outcome of the research (an IoT model encompassing vehicular wireless technologies) shows how the basic concept of packet switching can be assimilated as an effective mechanism to route large-scale autonomous vehicles within the IoT milieu, culminating in an effective commuter operating system. Controlled routing will invariably save the traveller time, provide independence to those who cannot drive, and decrease the greenhouse effect, whilst the packet switching characteristic offers greater overall security. In addition, the implications of this research will require a workforce to supplement new growth opportunities.

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.