The demand for collaborative, easy to use robots has increased during the last decades in hope of incorporating the use of robotics in smaller production scales, with easier and faster programming. Artificial intelligence (AI) and Machine learning (ML) are showing promising potential in robotics and this project has attempted to automatically solve a specific assembly task with Behavior trees (BTs). BTs can be used to elegantly divide a problem into different subtasks, while being modular and easy to modify. The main focus is put towards developing a Genetic algorithm (GA), that uses the fundamentals of biological evolution to produce BTs that solves the problem at hand. As a comparison to the GA result, a so-called Automated planner was developed to solve the problem and produce a benchmark BT. With a realistic physics simulation, this project automatically generated BTs that builds a tower of Duplo-like bricks and achieved successful results. The results produced by the GA showed a variety of possible solutions, a portion resembling the automated planner's results but also alternative, perhaps more elegant, solutions. As a conclusion, the approach used in this project shows promising signs and has many possible improvements for future research.

Modelling and simulating entities in virtual environments are tools commonly used by companies to test, validate and verify their products in close to real scenarios; effectivelyreducing the cost, time and effort compared to real life testing. This is especially the case in the area of air combat where realistic behaviors are not only a necessity, but paramount to replace the costs of fuel and operation time. The behavior tree framework is a behavior model whichrepresents entity actions with regards to its perception of the world whilst being easy to manuallyvalidate through its intuitively structured nature. However, as different simulated scenarios require different behaviors, operators commonly has to manually craft new behavior trees at the cost of time and effort. In this thesis, the AI technique Genetic Algorithms (GA) is used to improve a previously crafted general behavior tree with regards to a given 4v4 beyond-visual-range air combat scenario. To this end, a select number of parameters within the behavior tree are optimized in two experiments where a) all parameters are optimized globally and b) the parameters are divided into blocks of sub-behaviors (Engage, Fire missile, etc.) which are then optimized individuallyand combined at a later stage. The agents in the GA are put against the base tree where the baseline is referred to as the base tree vs itself. As the problem proved too easy and resulted in an over-optimized behavior when a single scenario was used, the decision was made to increase the number of the scenarios to three; differing in positions and orientations. The former experimentresulted in a behavior capable of defeating all entities in the other team without any casualties in all three scenarios while the behavior in the latter experiment failed to find the cross-blockrelations, and thus, only achieved a slightly better result than that of the baseline. However, the parameters of highest importance are found to be highly correlated in both experiments and GA is concluded to be a satisfactory technique for the problem of generating improved behaviors with regards to given scenarios.

AI är något som blir allt viktigare inom dagens spel och får allt högre krav på att agera mänskligt och intelligent. Detta kandidatarbete undersöker vilken metod som är att föredra för att skapa en AI som kan lära sig av sina tidigare erfarenheter. Några av de metoder som undersöks är trädstrukturer, Artificial Neural Network och GoCap. Genom att skapa en applikation med en av metoderna samt göra en undersökning på hur AI:n i applikationen upplevdes fick vi resultat om denna metod var användbar. Utifrån detta diskuteras det ifall andra metoder hade varit mer effektiva, hur man hade kunnat förbättra AI:n samt hur framtiden för spel-AI skulle kunna se ut. / AI is something that has become more important in today’s games and gets higher pressure to act human and intelligent. This thesis examines which methods are preferred when creating an AI that can learn from its previous experiences. Some of the methods that are examined are tree structures, Artificial Neural Network and GoCap. By creating an application with one of the methods and a survey of how the AI in the application was perceived we got a result that showed us if the method was functional. From this we discuss if the other methods would have been more effective, how we could have improved the AI and what the future for game-AI holds.

Artificial Intelligence (AI) is used in many games and quite often the Non-Player Character(NPC)s simulate humans. To make the human NPCs believable and feel alive they need to be as human-like as possible in their behaviour. Three features commonly used to make an NPC human-like are needs, like eating or sleeping, social relationships and personalities. The objective of this thesis was to create an AI with different personalities that the NPCs may have in the game Future Happiness Challenge (FHC) and compare these personalities as well as try to find out whether personalities enhance the game experience. Three different personalities are implemented; Selfish, Selfless and Balanced. These are used as FHC presents the option to play either selfish or selfless. It can be played as a team or as an individualist that does not care about the others. This thesis tries to answer the question whether a player prefers a selfless NPC to a selfish in a game where this option is available. These extremes are also compared to a balanced NPC. When implementing the AI and the personalities, a Behaviour Tree (BT) was used and the main features of this implementation is presented to give an example of how personalities can be implemented in a game like FHC. The results suggest that personalities enhance the game experience and an interesting correlation can be seen between the players preference of an NPC and their own personality when playing the game. Rather than always preferring a selfish or a selfless NPC, the players seem to prefer the NPC which has a personality closely related to their own within the game. / Artificiell intelligens (AI) används i många spel och det är vanligt att datorstyrda karaktärer (Non-Player Characters) föreställer människor. För att göra dessa karaktärer trovärdiga och ge känslan av att leva så behöver deras beteenden göras så mänskliga som möjligt. Tre egenskaper som ofta används för att göra karaktärerna mänskliga är behov, såsom att äta eller sova, sociala relationer och personligheter. Målet med det här arbetet var att skapa en AI med olika personligheter som karaktärerna kan ha i spelet Future Happiness Challenge (FHC) och jämföra dessa personligheter samt försöka ta reda på om personligheter ökar spelupplevelsen. Tre olika personligheter implementerades; självisk, osjälvisk och balanserad. Dessa valdes då FHC ger spelaren möjligheten att spela antingen själviskt eller osjälviskt. Det kan spelas som ett lag eller som en egoist som inte bryr sig om de andra. Denna rapport försöker svara på frågan om spelaren föredrar en osjälvisk karaktär över en självisk i ett spel där denna möjlighet finns. Dessa extrema personligheter jämförs också med en balanserad. Under utvecklingen av AIn och personligheterna användes tekniken Behaviour Tree (BT) och större delen av implementationen är presenterad i detta arbete för att ge ett exempel på hur personligheter kan implementeras i ett spel som FHC. Resultaten föreslår att personligheter ökar spelupplevelsen och ett intressant samband kan ses mellan spelarnas preferens av NPC och spelarnas personligheter i FHC. Istället för att alltid föredra en självisk eller osjälvisk NPC, verkar spelarna föredra den NPC som har en personlighet lik sin egen i spelet.

Arbetet undersöker skillnader i prestanda mellan SOLID och mer hårdkodade algoritmer, i form av respektive beteendeträd och en enum-switch tillståndsmaskin. Testerna utförs i spelmotorn Unity genom en isolerad testmiljö där exekveringstiden mäts mellan i scenarion. Algoritmerna har implementerats så att de kan köra olika beteenden, dessa testas i olika scenarion för att få fram resultaten. Exekveringstid mäts genom profiler-verktyget i Unity som sedan analyseras och jämförs för att se hur mycket tid det tar att exekvera och om det finns skillnader mellan dem. Resultaten tyder på att den hårdkodade algoritmen presterade bättre än den som följde SOLID-principer, dock inte så markant att det utesluter användningen av den mer flexibla koden.

Abstract. Autonomous agents, also known as AI agents, are staples in modern video games. They take a lot of roles, everything from being quest-givers in roleplaying games, to opposing forces in action- and shooter games. Crafting an AI that is not only easy to create, but also retains humanlike and believable behavior, has always represented a challenge to the development industry, and has in several cases ended up with open world games using AI systems that limit the AI agents to simple moving patterns. In this thesis, a form of AI systems more commonly used in simulation games such as The Sims video game series, are taken and implemented in an environment that could possibly be seen in an open world game. After the implementation, a set of tests were performed on a group of testers which resulted in the insight that a majority of the testers, when asked to compare their experience to other games, found this implementation to feel more lifelike and realistic.