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This thesis investigates the use of static, adaptive, adaptable and mixed-initiative approaches to the personalisation of content and graphical user interfaces (GUIs). This empirical study consisted of three experimental phases. The first examined the use of static, adaptive, adaptable and mixed-initiative approaches to web content. More specifically, it measured the usability (efficiency, frequency of error occurrence, effectiveness and satisfaction) of an e-commerce website. The experiment was conducted with 60 subjects and was tested empirically by four independent groups (15 subjects each). The second experiment examined the use of adaptive, adaptable and mixed-initiative approaches to GUIs. More specifically, it measured the usability (efficiency, frequency of error occurrence, effectiveness and satisfaction) in GUI control structures (menus). In addition, it investigated empirically the effects of content size on five different personalised menu types. In order to carry out this comparative investigation, two independent experiments were conducted, on small menus (17 items) and large ones (29 items) respectively. The experiment was conducted with 60 subjects and was tested empirically by four independent groups (15 subjects each). The third experiment was conducted with 40 subjects and was tested empirically by four dependent groups (5 subjects each). The aim of the third experiment was to mitigate the drawbacks of the adaptive, adaptable and mixedinitiative approaches, to improve their performance and to increase their usability by using multimodal auditory solutions (speech, earcons and auditory icons). The results indicate that the size of content affects the usability of personalised approaches. In other words, as the size of content increases, so does the need of the adaptive and mixed-initiative approaches, whereas that of the adaptable approach decreases. A set of empirically derived guidelines were also produced to assist designers with the use of adaptive, adaptable and mixed-initiative approaches to web content and GUI control structure.

Procedural content generation (PCG) can be a useful tool for aiding creativityand efficiency in the process of designing game levels. Mixed-initiative level genera-tion tools where a designer and an algorithm collaborate to iteratively generate gamelevels have been used for this purpose – taking advantage of the combination of com-putational efficiency and human intuition and creativity. However, it can be difficultfor designers to work with tools that do not respond to the common language of games:game design patterns.It has been demonstrated that game design patterns can be integrated into PCGalgorithms, but formally-defined and hierarchically-arranged game design patternshave not yet been used as a means of increasing gameplay-based control in mixed-initiative dungeon generators. We present a method for evolving dungeon rooms usingmulti-level game design patterns in the objective function of a genetic algorithm, aswell as an instantiation of this method in a mixed-initiative dungeon design tool. Ourresults show that we are able to control the frequency and type of design patterns ingenerated rooms using pattern-related input parameters, enabling us to create dungeonrooms containing a wide variety of patterns on different levels of abstraction.Results from a small-scale user study of professional game developers suggest thatthe use of game design patterns in mixed-initiative level design tools can be a promisingway of providing a good starting point when designing a level, as well as offeringmeaningful gameplay related feedback throughout the design process. We also identifychallenges that will need to be faced if game design pattern-based mixed-initiative leveldesign tools are to become a part of the game designer’s toolkit.

Writing is a notoriously time-consuming and challenging activity that is difficult to avoid during the development of a game, and the steady increase in complexity behind producing games is putting pressure on the industry to cut unnecessary costs and streamline processes. With recent breakthroughs in Neural Network research the capabilities of causal language models like the GPT models made by OpenAI have reached a level where they could be used to assist with creative assignments that previously only could be done to an acceptable level of quality by a human writer. This paper aims to combine the power of a language model with the versatility and control of the Mixed-Initiative Co-Creation approach. In order to limit the scope of the artifact to a manageable size the focus will be to generate a shorter biography with backstory for characters and items in a level made in the Evolutionary Dungeon Designer by Alvarez et al. The artifact was evaluated with a user study in which both quantitative ratings and qualitative feedback was collected. The results suggest that the artifact has potential as it has the ability to generate compelling narratives and users attested that it had a positive effect on their work.

The increasing ubiquity of networked mobile devices such as cell phones and PDAs has created new opportunities for the transmission and display of multimedia content. However, any mobile device has inherent resource constraints: low network bandwidth, small screen sizes, limited input methods, and low commitment viewing. Mobile systems that provide information display and access thus need to mitigate these various constraints. Despite progress in information retrieval and content recommendation, there has been less focus on issues arising from a network-oriented and mobile perspective. This dissertation investigates a coordinated design approach to networked multimedia on mobile devices, and considers the abovementioned system perspectives. Within the context of accessing news video on mobile devices, the goal is to provide a cognitively palatable stream of videos and a seamless, low-latency user experience. Mixed-initiative---a method whereby intelligent services and users collaborate efficiently to achieve the user's goals, is the cornerstone of the system design and integrates user relevance feedback with a content recommendation engine and a content- and network-aware video buffer prefetching technique. These various components have otherwise been considered independently in other prior system designs. To overcome limited interactivity, a mixed-initiative user interface was used to present a sequence of news video clips to the user, along with operations to vote-up or vote-down a video to indicate its relevance. On-screen gesture equivalents of these operations were also implemented to reduce user interface elements occupying the screen. Semantic relevancy was then improved by extracting and indexing the content of each video clip as text features, and using a Na"ive Bayesian content recommendation strategy that harnessed the user relevance feedback to tailor the subsequent video recommendations. With the system's knowledge of relevant videos, a content-aware video buffer prefetching scheme was then integrated, using the abovementioned feedback to lower the user perceived latency on the client-end. As an information retrieval system consists of many interacting components, a client-server video streaming model is first developed for clarity and simplicity. Using a CNN news video clip database, experiments were then conducted using this model to simulate user scenarios. As the aim of improving semantic relevancy sometimes opposes user interface tools for interactivity and user perceived latency, a quantitative evaluation was done to observe the tradeoffs between bandwidth, semantic relevance, and user perceived latency. Performance tradeoffs involving semantic relevancy and user perceived latency were then predicted. In addition, complementary human user subjective tests are conducted with actual mobile phone hardware running on the Google Android platform. These experiments suggest that a mixed-initiative approach is helpful for recommending news video content on a mobile device for overcoming the mobile limitations of user interface tools for interactivity and client-end perceived latency. Users desired interactivity and responsiveness while viewing videos, and were willing to sacrifice some content relevancy in order gain lower perceived latency. Recommended future work includes expanding the content recommendation to incorporate viewing data from a large population, and the creation of a global hybrid content-based and collaborative filtering algorithm for better results. Also, based on existing user behaviour, users were reluctant to provide more input than necessary. Additional user experiments can be designed to quantify user attention and interest during video watching on a mobile device, and for better definition and incorporation of implicit user feedback.

Searching for lost people in a Wilderness Search and Rescue (WiSAR) scenario is a task that can benefit from large numbers of agents, some of whom may be robotic. These agents may have differing levels of autonomy, determined by the set of tasks they are performing. In addition, the level of autonomy that results in the best performance may change due to varying workload or other factors. Allowing a supervisor and a searcher to jointly decide the correct level of autonomy for a given situation (“mixed initiative”) results in better overall performance than giving an agent absolute control over their level of autonomy (“adaptive autonomy”) or giving a supervisor absolute control over the agent's level of autonomy (“adjustable autonomy”).

This paper describes a modification to the mixed-initiative co-creative tool, the Evolutionary Dungeon Designer, and a study performed to evaluate and analyze the co-creative relationship between human designer and AI. The relationship between AI and human designer in creative processes is delicate, as adjusting the autonomy of the AI can negatively affect the user experience. To perfect this type of Human-AI collaboration, further research is needed. In this thesis, different degrees of initiative of the AI are explored, to gain further understanding of mixed-initiative co-creative tools. A user study was performed on the Evolutionary Dungeon Designer, with three varying degrees of AI-initiative. The study highlighted elements of frustration that the human designer experiences when using the tool, and the behaviour in the AI that led to possible strains on the relationship. The paper concludes with the identified issues and possible solutions, as well as suggested further research.

I denna uppsats presenteras ett verktyg för att assistera skapandet av spelbanor i dungeon-miljö. Skapandet av sådana banor är ofta resurskrävande under produktionen och har fokus på design. För att behålla designaspekten men underlätta arbetet är verktyget framtaget för att med tillgängliga parametrar producera grottgångar för sådana banor. Vi undersöker med användartester hur verktyget kan användas för att effektivisera en känd metod för att skapa spelbanor, men samtidigt skapa den kvalité som eftertraktas. Med detta avser vi inte bara att ta fram ett effektivt verktyg, utan även att demonstrera en metod för att använda procedurell generering av spelinnehåll för ett nytt ändamål inom speldesign. / In this article a tool for assisting the creation of game levels in a dungeon environment is presented. Creating such game levels often requires a large amount of resources during a game production and has focus on design. To keep the aspect of design but ease work, the tool is created to produce caverns for such game levels with accessible parameters. We examine with user tests how the tool can be used to make an existing method for creation of game levels more effective. But at the same time producing the quality that is coveted. With this we do not only hope to produce an effective tool, but also to demonstrate a method for using procedural generation for a new purpose in game design.

This paper investigates the effectiveness of expressive range and controllability for 2-dimensional tile-based procedural content generation. Procedural content generation (PCG) is the automation of content, often in games, and tile-based PCG is when the generated content is constrained to a grid structure. Mixed-initiative PCG, which is the integration of user control into PCG, has been researched previously for tile-based PCG but the implementations have been limited by lack of breadth and user control over the algorithm. As a result, the expressive ranges and controllabilities of their algorithm have not been comprehensive, and in turn, the concepts of expressive range and controllability not thoroughly scrutinized. For the purpose of examining the concepts of expressive range and controllability, an implementation of declarative modelling, named Mixed-initiative Tile-based Designer (MTD), is made. The MTD combines the mission and shape grammar algorithm proposed by Dormans’ and the level generation system in Spelunky. Nine sets of input parameters, scenarios, are tested, each with 2000 levels generated. For each scenario, the expressive range of its output is examined using the standard evaluation metrics linearity and leniency. The results of the data sampling indicated that an analysis based on expressive range needs to be supported by additional analyses for the insights drawn to be more general. In particular, expressive range needs to be complemented by manual inspection, and linearity when applied to sufficiently complex levels needs to be complemented by additional evaluation metrics. On the other hand, controllability was found to have more significant limitations in its current form because of the normalization of the data that goes into the expressive range. One solution is instead to visualize the raw data of the expressive range and make liberal use of manual inspection. As a secondary study, the feasibility of implementing declarative modelling into 2-dimensional tile-based PCG is investigated through analyzing the MTD. In particular, the MTD’s user interface, procedural output and controllability is examined to determine whether declarative modelling is feasible for 2-dimensional tile-based PCG. / Denna artikel undersöker effektiviteten av uttrycksomfång och kontrollerbarhet för generering av 2-dimensionell rut-baserad procedurinnehåll. Procedurell innehållsgenerering (PCG) är automatisering av innehåll, ofta i spel, och rut-based PCG är när det genererade innehållet är begränsat till en rutnätsstruktur. Blandinitiativ PCG, som är integrationen av användarkontroll i PCG, har undersökts tidigare för rut-baserad PCG men implementeringarna har begränsats av brist på bredd och användarkontroll över algoritmen. Som ett resultat har uttrycksomfången och kontrollerbarheten av deras algoritm inte varit heltäckande, och i sin tur har begreppen uttrycksfullt omfång och kontrollerbarhet inte granskats noggrant. I syfte att undersöka begreppen uttrycksomfång och kontrollerbarhet görs en implementering av deklarativ modellering, benämnd Mixed-initiative Tile-based Designer (MTD). MTD:n kombinerar uppdrags-och formgrammatikalgoritmen som föreslagits av Dormans och nivågenereringssystemet i Spelunky. Nio uppsättningar ingångsparametrar, scenarier, testas, var och en med 2000 genererade nivåer. För varje scenario undersöks det uttrycksomfånget för dess utdata med hjälp av standardutvärderingsmåtten linjäritet och svårighet. Resultaten indikerade att en analys baserad på uttrycksomfång måste stödjas av ytterligare analyser för att insikterna ska bli mer generella. Speciellt måste uttrycksomfång kompletteras med manuell inspektion, och linjäritet när den tillämpas på tillräckligt komplexa nivåer måste kompletteras med ytterligare utvärderingsmått. Å andra sidan visade sig kontrollerbarhet ha mer betydande begränsningar i sin nuvarande form på grund av normaliseringen av data som går in i uttrycksomfånget. En lösning är istället att visualisera rådata från uttrycksomfånget och använda sig av manuell inspektion. Som en sekundär studie undersöks möjligheten att implementera deklarativ modellering i 2- dimensionell rut-baserad PCG genom att analysera MTD. I synnerhet undersöks MTD:s användargränssnitt, procedurutdata och kontrollerbarhet för att avgöra om deklarativ modellering är genomförbar för 2-dimensionell rut-baserad PCG.

The application of PCG to generate puzzles offers great value since their replayability is severely limited, requiring any game that employs them to produce many different puzzles. In this paper we propose a modified version of the progressive content generation approach to function as a mixed-initiative system, to create puzzles for the novel partially physics-based game \textit{Everyone Must Die}. Thus exploring the adaptability and usefulness of the progressive content generation approach for a unique type of puzzle game. Further the mixed-initiative system is explored in relation to how effectively it can generate puzzles with a specified difficulty, an issue many papers exploring puzzle generation neglect. This is explored by implementing and incorporating a PCG system by extending an existing puzzle editor featured in the game. The analysis is conducted with the help of a user study on the developers of the game by testing qualitative experiences with the system. The promising results are then discussed and concluded with suggestions for future work and improvements to the described system and its used approach.