Scientists interested in uncovering associations between DNA variants and complex traits such as personality characteristics and cognitive abilities often face the constraint that the research requires very large samples to be successful. One reason very large samples are difficult to assemble is that the outcome variables are not always measured in a consistent way in different data sets, and sometimes they are not measured at all. In such cases, traditional measurement techniques (such as in-house interviews or surveys) are not always feasible. In this project, I sought to develop two “serious games” which can be used to measure short-term memory. The study shows that an individual’s performance in both of these games is moderately predictive of measures of short-term memory used by psychologists. The study also finds that users rate the serious games as more enjoyable than the conventional measures. The results suggest that ongoing effort to gather outcome data via nonstandard techniques such Mobile apps may turn out to be a useful complement to conventional approaches toward data collection.

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Villkorsbaserad programmering är en programmeringsparadigm som används vid problemlösning som planering och schemaläggning. Problemlösningen sker genom att skapa en modell i en lösare som sedan söker efter lösningar. Idag finns få realtidsapplikationerbaserade på villkorsbaserad programmering. Det här arbetet utreder hur villkorsbaserade program presterar i en interaktiv miljö där krav finns att det ska vara i realtid. En webbapplikation har skapats med ett klient-server förhållande där en villkorlösare implementerats på servern. Mätningar har sedan gjorts på servern för att mäta responstiden. Resultatet var en funktion som visar responstiden beroende på antal villkor i modellen. Gränsen för antalet villkor låg runt 1400. Till detta så visas hur responstiden kan minskas genom tekniker som villkor-lösare erbjuder.

Att kunna klassificera en körstil implicerar klassificering av körbeteende, vilket ligger till grunden för miljö- och säkerhetsklassificering för körningar. I det här arbetet har vi låtit två förare köra en bil med en förhoppning att kunna klassificera vem det var som körde bilen. Målet var att kunna förutspå föraren med en korrekthet på 80-90% givet endast hastighet samt varvtal som samlas genom ODB:II-porten via CAN-bussen i fordonet. Angreppsättet på detta arbete liknar det för textklassificering, nämligen att använda två vanliga klassificeringsmetoder från just textklassificering — Multinominal och Gaussisk Naive Bayes tillsammans med N-gram samt diskretisering. Vi fann genom att använda Multinominal Naive Bayes med 4-gram samt icke-diskretiserade respektive diskretiserade hastighet- och varvtalsvärden kunde klassificera förare med 91.48% korrekthet. / To be able to classify a driving style implies that you classify a driving behaviour, which is the foundation of safety and environmental driving classification. In this thesis we have let two drivers drive a car in an attempt to classify, with a desired accuracy of around 90%, which one of us drove the car. This was done by exclusively using speed and rpm data values provided from the OBD:II port of the car via the CAN-bus. We approched this problem like you would a text classification one, thus using two common models of Naive Bayes — Multinominal and Gaussian Naive Bayes together with N-gram and discretization. We found that using Multinominal Naive Bayes consisting of 4-gram resulted in an avarage accuracy of 91.48% in predicting the driver, non-discretized speed and discretized rpm values.

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Producing many varying instances of the same type of graphical resource for games can be of interest, such as trees or foliage. But when randomly generating graphical resources, you can often end up with many similar looking results or perhaps results that doesn't look like what it is meant to look like. This work investigates whether genetic algorithms can be applied to produce greater varying results when generating graphical resources by basing the fitness of each individual for each genetic generation on how similar the graphical resource is to previously generated resources. This work concludes from the limited work that was performed that while it seems possible that the use of genetic algorithms might be able to produce visually different graphical resources, Blender currently doesn't seem to be able to produce enough results in a reasonable time frame for this to be usable on a large scale.

In this thesis we present an algorithm that is designed to improve the collaborative capabilities of agents that operate in real-time multi-agent systems. Furthermore, we study the coalition formation and task assignment problems in the context of real-time strategy games. More specifically, we design and present a novel anytime algorithm for multi-agent cooperation that efficiently solves the simultaneous coalition formation and assignment problem, in which disjoint coalitions are formed and assigned to independent tasks simultaneously. This problem, that we denote the problem of collaboration formation, is a combinatorial optimization problem that has many real-world applications, including assigning disjoint groups of workers to regions or tasks, and forming cross-functional teams aimed at solving specific problems. The algorithm's performance is evaluated using randomized artificial problems sets of varying complexity and distribution, and also using Europa Universalis 4 – a commercial strategy game in which agents need to cooperate in order to effectively achieve their goals. The agents in such games are expected to decide on actions in real-time, and it is a difficult task to coordinate them. Our algorithm, however, solves the coordination problem in a structured manner. The results from the artificial problem sets demonstrates that our algorithm efficiently solves the problem of collaboration formation, and does so by automatically discarding suboptimal parts of the search space. For instance, in the easiest artificial problem sets with 12 agents and 8 tasks, our algorithm managed to find optimal solutions after only evaluating approximately 0.000003% of the possible solutions. In the hardest of the problem sets with 12 agents and 8 tasks, our algorithm managed to find a 80% efficient solution after only evaluating approximately 0.000006% of the possible solutions. / I denna uppsats presenteras en ny algoritm som är designad för att förbättra samarbetsförmågan hos agenter som verkar i realtidssystem. Vi studerar även koalitionsbildnings- och uppgiftstilldelningsproblemen inom realtidsstrategispel, och löser dessa problem optimalt genom att utveckla en effektiv anytime-algoritm som löser det kombinerade koalitionsbildnings- och uppgiftstilldelningsproblemet, inom vilket disjunkta koalitioner formas och tilldelas uppgifter. Detta problem, som vi kallar samarbetsproblemet, är en typ av optimeringsproblem som har många viktiga motsvarigheter i verkligheten, exempelvis för skapandet av arbetsgrupper som skall lösa specifika problem, eller för att ta fram optimala tvärfunktionella team med tilldelade uppgifter. Den presenterade algoritmens prestanda utvärderas dels genom att använda simulerade problem av olika svårighetsgrad, men också genom att använda verkliga problembeskrivningar från det kommersiella strategispelet Europa Universalis 4, vilket är ett spel som agenter måste samarbeta i för att effektivt uppnå deras mål. Att koordinera agenter i sådana spel är svårt, men vår algoritm åstadkommer detta genom att systematiskt söka efter de optimala agentgrupperingarna för ett antal givna uppgifter. Resultaten från de simulerade problemen visar att vår algoritm effektivt löser samarbetsproblemet genom att systematiskt sålla bort suboptimala delar av sökrymden. I dessa tester lyckas vår algoritm generera högkvalitativa anytime-lösningar. Till exempel, i de enklaste problemen med 12 agenter och 8 uppgifter lyckas vår algoritm hitta den optimala lösningen efter det att den endast utvärderat 0.000003% av de möjliga lösningarna. I de svåraste problemen med 12 agenter och 8 uppgifter lyckas vår algoritm hitta en lösning som är 80% från den optimala lösningen efter det att den endast utvärderat 0.000006% av de möjliga samarbetsstrukturerna.

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Software development is a demanding task where developers face great challenges. Development tools are expected to be user-friendly. However, little research has been done to understand developers’ needs and priorities when it comes to such tools, so called Integrated Development Environments (IDEs). It is important to gain a better insight into developers’ prioritization of IDE qualities, in order to be able to make such tools more efficient and better tailored to their users’ real needs. The objective of this thesis is to gain a deeper understanding of software developers’ perspective regarding IDEs with the goal of facilitating further improvements of these tools. This thesis report presents the results of a quantitative survey, responded by twenty professional software developers, which focuses on developers’ prioritization of qualities in adopted IDEs and their perceived satisfaction with the qualities themselves and the IDEs overall. Moreover, we investigate whether limiting developers’ choice of IDE also affects their overall satisfaction of chosen IDEs. By analyzing the results, we conclude that developers perceive reliability, ease of use and efficiency as, on one hand the most important qualities of IDEs, and on the other hand those most in need of improvements. The results also suggest that developers’ satisfaction with their IDEs is not significantly affected by limiting their choice of development tools.