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Non-Player Character (NPC) behaviors in todays computer games are mostly generated from manually written scripts. The high cost of manually creating complex behaviors for each NPC to exhibit intelligence in response to every situation in the game results in NPCs with repetitive and artificial looking behaviors. The goal of this research is to enable NPCs in computer games to exhibit natural and human-like behaviors in non-combat situations. The quality of these behaviors affects the game experience especially in story-based games, which rely heavily on player-NPC interactions. Reinforcement Learning has been used in this research for BioWare Corp.s Neverwinter Nights to learn natural-looking behaviors for companion NPCs. The proposed method enables NPCs to rapidly learn reasonable behaviors and adapt to the changes in the game environment. This research also provides a learning architecture to divide the NPC behavior into sub-behaviors and sub-tasks called decision domains.

Digital games incorporate systems that allow players to customise and develop their controllable in-game representative (avatar) over the course of a game. Avatar customisation systems represent a point at which the goals and values of players interface with the intentions of the game developer forming a dynamic and complex relationship between system and user. With the proliferation of customisable avatars through digital games and the ongoing monetisation of customisation options through digital content delivery platforms it is important to understand the relationship between player and avatar in order to provide a better user experience and to develop an understanding of the cultural impact of the avatar. Previous research on avatar customisation has focused on the users of virtual worlds and massively multiplayer games, leaving single-player avatar experiences. These past studies have also typically focused on one particular aspect of avatar customisation and those that have looked at all factors involved in avatar customisation have done so with a very small sample. This research has aimed to address this gap in the literature by focusing primarily on avatar customisation features in single-player games, aiming to investigate the relationship between player and customisation systems from the perspective of the players of digital games. To fulfill the research aims and objectives, the qualitative approach of interpretative phenomenological analysis was adopted. Thirty participants were recruited using snowball and purposive sampling (the criteria being that participants had played games featuring customisable avatars) and accounts of their experiences were gathered through semi-structured interviews. Through this research, strategies of avatar customisation were explored in order to demonstrate how people use such systems. The shortcomings in game mechanics and user interfaces were highlighted so that future games can improve the avatar customisation experience.

Most story-based games today have manually-scripted non-player characters (NPCs) and the scripts are usually simple and repetitive since it is time-consuming for game developers to script each character individually. ScriptEase, a publicly-available author-oriented developer tool, attempts to solve this problem by generating script code from high-level design patterns, for BioWare Corp.'s role-playing game Neverwinter Nights. The ALeRT algorithm uses reinforcement learning (RL) to automatically generate NPC behaviours that change over time as the NPCs learn from the successes or failures of their own actions. This thesis aims to provide a new learning mechanism to game agents so they are capable of adapting to new behaviours based on the actions of other agents. The new on-line RL algorithm, ALeRT-AM, which includes an agent-modeling mechanism, is applied in a series of combat experiments in Neverwinter Nights and integrated into ScriptEase to produce adaptive behaviour patterns for NPCs.

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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.

Climbing in video games is a game mechanic that, after the introduction of three-dimensional movement, became more and more common and extended the movement options of the player character. Climbing, as used in this thesis, is a very specific action that can only be performed by interacting with a climbable gameplay element in the game world. A visual analysis is applied to investigate how objects in action-adventure games depict climbable gameplay elements: specifically, whether the way the games communicate climbability through visual communications is consistent or not. Four games are included in this study and the results point towards an overarching consistency for each of the games' use of visual cues to communicate climbability. However, the consistency is affected by the gameplay experience of each game. The linear games use very clear and controlled visual cues to describe the climbable gameplay elements, and the player often only interacts with a climbable gameplay element once per section, similar to an obstacle course. The games that emphasize exploration of a gameplay area, akin to a playground, place climbable gameplay elements around the environment for the player to freely interact with and have less clear visual cues. Climbable gameplay elements, like ledges, are found to be unique in the interaction they provide, and this is proven by the many different methods of visual communication that are used to convey the affordance of climbing. / Klättrande i dataspel är en spelmekanik som efter introduktionen av tredimensionell rörelse, blivit allt vanligare och utvidgat möjligheterna för spelarkaraktärens rörelser. Klättrande, som använt i denna uppsats, är en mycket specifik handling som endast kan utföras genom att interagera med ett klättringsbart spelelement i spelvärlden. En visuell analys appliceras för att undersöka hur objekt i action-äventyrsspel avbildar klättringsbara spelelement. Specifikt, huruvida sättet spelen kommunicerar klättringsbarhet genom visuell kommunikation är konsekvent eller ej. Fyra spel är inkluderade i denna studie och resultaten pekar mot att det finns en övergripande följdriktighet för varje spels användande av visuella signaler för att kommunicera klättringsbarhet. Följdriktigheten är dock påverkad av spelupplevelsen i varje spel. Dem linjära spelen använder mycket tydliga och kontrollerade visuella signaler för att beskriva klättringsbara spelelement, och spelaren interagerar oftast endast med ett klättringsbart spelelement en gång per sektion, likt en hinderbana. Spel som betonar utforskande av ett spelområde, likt en lekplats, har mindre tydliga visuella signaler. Klättringsbara spelelement, såsom avsatser, upptäcks vara unika i interaktionen de förmedlar, och detta påvisas av de många olika metoderna för visuell kommunikation som används för att förmedla klättringsbarhet.