Research of macroinvertebrates is currently an important part of the assessment of stream quality. Basic characteristics influencing macroinvertebrates communities in the streams are elements from the external environment influencing streams, specifically basic geographic parameters of the site, land-use in the catchment and characteristics of the banks. These elements have a considerable influence on effect of the basic parameters in the stream, which are flow, sediment structure, input of organic material and pollution. Research part of the thesis is about the parameters influencing macroinvertebrates communities. Research area of this work includes three different catchments in the upper part of Blanice catchment area near Prachtice. There is one nature stream with forest in it's catchment, stream with a predominantly meadow basin and last stream is characterized by two distinct stretches - a revitalized section and a section with concrete bottom. The subject of this diploma thesis is evaluation of diversity of macroinvertebrates communities in all streams in the research area. Thesis evaluate communities of macroinvertebrate using a wide range of indexes (BMWP score, ASTP index, EPT index a diversity indexes) and compares their diversity by using rank-abundance graph. All results are summarized...

One of the main purposes of the fields of Robotics and Artificial Intelligence is to develop solutions that can autonomously solve problems. An important part of this is synthesising behaviours of robots. Behaviour Trees are a tree structure that enables combining existing lower level behaviours into a high level behaviour through task switching. However, designing appropriate Behaviour Trees can be prohibitive due to time and knowledge requirements. One way of automating the creation of Behaviour Trees is through Genetic Programming, which evolves solutions through mutations and combinations akin to biological evolution. This Masters thesis explores how Genetic Programming can be used to generate Behaviour Trees in an automatic fashion. More specifically, whether so-called Quality Diversity can be used to improve the search. Quality Diversity describes a field of algorithms that combine both performance and novelty of behaviour to evaluate solutions. By including a novelty aspect the search space is more thoroughly explored, and deceptive local optima may be more easily avoided. In this thesis three Quality Diversity algorithms are implemented and evaluated in different settings: Novelty Search, Novelty Search with Local Competition, and Multi-dimensional Archive of Phenotypic Elites. The results show that Quality Diversity has potential to both increase the speed at which solutions are found and decrease the likelihood of premature convergence due to local optima. However, we also find that care must be taken in how behaviours are defined, and how some common techniques of Genetic Programming need to be adapted for Quality Diversity algorithms. / Ett av huvudsyftena med robotik och artificiell intelligens är att skapa system som självständigt kan lösa problem. En viktig del av detta är att skapa robotars beteenden. Beteendeträd är en trädstruktur som gör det möjligt att kombinera befintliga beteenden på lägre nivå till ett beteende på hög nivå. Att utforma lämpliga beteendeträd kan dock kräva både mycket tid och kunskap. Ett sätt att automatisera skapandet av beteendeträd är genom genetisk programmering, som utvecklar lösningar genom mutationer och kombinationer i likhet med biologisk evolution. Detta examensarbete undersöker hur genetisk programmering kan användas för att automatiskt generera beteendeträd. Mer specifikt undersöks om kvalitetsmångfald (Quality Diversity) kan användas för att förbättra sökningen. Kvalitetsmångfald beskriver en familj av algoritmer som kombinerar både prestanda och innovation i en lösnings beteende för att utvärdera lösningar. Genom att inkludera en innovationsaspekt blir sökområdet mer noggrant utforskat och vilseledande lokala optima kan lättare undvikas. I detta examensarbete implementeras och utvärderas tre kvalitetsmångfaldsalgoritmer i olika miljöer: Novelty Search, Novelty Search with Local Competition, och Multi-dimensional Archive of Phenotypic Elites. Resultaten visar att kvalitetsmångfald har potential att både öka hastigheten med vilken lösningar hittas och minska sannolikheten för tidig konvergens på grund av lokala optima. Vi konstaterar dock också att man måste vara försiktig med hur beteenden definieras och hur vissa vanliga tekniker för genetisk programmering måste anpassas för algoritmer med kvalitetsmångfald. / Eén van de belangrijkste doelstellingen van het vakgebied Robotica en Kunstmatige Intelligentie is het ontwikkelen van oplossingen die autonoom problemen kunnen oplossen. Een belangrijk onderdeel hiervan is het synthetiseren van gedragingen van robots. Gedragsbomen zijn een boomstructuur waarmee bestaande gedragingen van een lager niveau kunnen worden gecombineerd tot een gedrag van een hoger niveau door middel van taakwisseling. Het ontwerpen van geschikte gedragsbomen is echter soms niet haalbaar haalbaar vanwege de benodigde tijd en kennis. Een manier om het maken van gedragsbomen te automatiseren is door middel van genetisch programmeren, dat oplossingen ontwikkelt door mutaties en combinaties, vergelijkbaar met biologische evolutie. Deze masterproef onderzoekt hoe genetische programmering kan worden gebruikt om automatisch gedragsbomen te genereren. Meer specifiek of kwaliteitsdiversiteit (Quality Diversity) kan worden gebruikt om het zoeken te verbeteren. Kwaliteitsdiversiteit beschrijft een gebied van algoritmen die zowel prestaties als nieuwheid van een oplossingsgedrag combineren om oplossingen te evalueren. Door een nieuwheidsaspect te introduceren wordt de zoekruimte grondiger verkend en kunnen bedrieglijke lokale optima gemakkelijker worden vermeden. In deze masterproef worden drie algoritmen voor kwaliteitsdiversiteit toegepast en geëvalueerd in verschillende omgevingen: Novelty Search, Novelty Search with Local Competition en Multi-dimensional Archive of Phenotypic Elites. De resultaten tonen aan dat kwaliteitsdiversiteit het potentieel heeft om zowel de snelheid waarmee oplossingen worden gevonden te verhogen als de kans op voortijdige convergentie als gevolg van lokale optima te verminderen. Wij stellen echter ook vast dat zorgvuldigheid geboden is bij de definitie van gedragingen en dat sommige gebruikelijke technieken van genetisch programmeren moeten worden aangepast voor algoritmen met kwaliteitsdiversiteit.

As AI develops, grows, and expands, the more benefits we can have from it. AI is used in multiple fields to assist humans, such as object recognition, self-driving cars, or design tools. However, AI could be used for more than assisting humans in their tasks. It could be employed to collaborate with humans as colleagues in shared tasks, which is usually described as Mixed-Initiative (MI) paradigm. This paradigm creates an interactive scenario that leverage on AI and human strengths with an alternating and proactive initiative to approach a task. However, this paradigm introduces several challenges. For instance, there must be an understanding between humans and AI, where autonomy and initiative become negotiation tokens. In addition, control and expressiveness need to be taken into account to reach some goals. Moreover, although this paradigm has a broader application, it is especially interesting for creative tasks such as games, which are mainly created in collaboration. Creating games and their content is a hard and complex task, since games are content-intensive, multi-faceted, and interacted by external users.  Therefore, this thesis explores MI collaboration between human game designers and AI for the co-creation of games, where the AI's role is that of a colleague with the designer. The main hypothesis is that AI can be incorporated in systems as a collaborator, enhancing design tools, fostering human creativity, reducing their workload, and creating adaptive experiences. Furthermore, This collaboration arises several dynamic properties such as control, expressiveness, and initiative, which are all central to this thesis. Quality-Diversity algorithms combined with control mechanisms and interactions for the designer are proposed to investigate this collaboration and properties. Designer and Player modeling is also explored, and several approaches are proposed to create a better workflow, establish adaptive experiences, and enhance the interaction. Through this, it is demonstrated the potential and benefits of these algorithms and models in the MI paradigm.