MODDING SKYRIM WITH GENERATIVE AI: EXPLORING IMAGINARIES AND PLAYER-NPC INTERACTIONS

The development of the gaming industry has provided an opportunity to combine entertainment with education. In this context, this study aims to implement the Jump Point Search (JPS) algorithm in the game "Prince of Walangsungsang" to provide a more interesting playing experience and introduce players to the history of Prince Walangsungsang journey. This study focuses on the action role-playing game (RPG) genre that combines game elements with historical stories. In making games, the development method used is the Game Development Life Cycle (GDLC) to ensure structured and efficient development steps. The JPS algorithm is implemented as an artificial intelligence (AI) agent on non-player characters (NPC) in games. By implementing the JPS algorithm, in-game NPCs can optimize their movements in a complex game environment, speed up finding the shortest path, and avoid unnecessary routes, as well as avoid dynamic obstacles. Games "Prince of Walangsungsang" provide a more realistic and challenging gaming experience for players, as well as introduce historical aspects through interactions with NPCs. The results of the study show that the implementation of the JPS algorithm in the game "Prince of Walangsungsang" improves the efficiency and intelligence of NPCs, so that players can experience a more dynamic and interesting playing experience. In addition, game-based introduction to the history of Prince Walangsungsang also gives players the opportunity to learn about and appreciate the cultural heritage associated with this historical figure.

Nowadays, video games conforms a huge industry that is always developing new technology. In particular, artificial intelligence techniques have been used broadly in the well-known non-player characters (NPC) given the opportunity to users to feel video games more real. This paper proposes the usage of the MaxQ-Q hierarchical reinforcement learning algorithm in non-player characters in order to increase the experience of the user in terms of naturalness. A case study of an NPC with the proposed artificial intelligence based algorithm in a first personal shooter video game was developed. Experimental results show that this implementation improves naturalness from the user’s point of view. In addition, the proposed MaxQ-Q based algorithm in NPCs allow to programmers a robust way to give artificial intelligence to them.

Creating the behavior for non-player characters in video games is a complex task that requires the collaboration among programmers and game designers.

Authoring the AI for non-player characters (NPCs) in modern video games is an increasingly complex task. Designers and programmers must collaborate to resolve a tension between believable agents with emergent behaviours and scripted story lines. Behaviour trees (BTs) have been proposed as an expressive mechanism that let designers create complex behaviours along the lines of the story they want to tell. However, BTs are still too complex for non-programmers. In this paper, we propose the use of plan traces to assist designers when building BTs. In order to make this approach feasible within state-of-the-art video game technology, we generate the planning domain through an extension of the component-based approach, a widely used technique for representing entities in commercial video games.

Abstract: Applications of fuzzy logic in game creation have proven useful, especially when it comes to modelling the behavior of non-player characters (NPCs) in video games. NPC behavior in video games has historically been governed by strict rule-based systems or scripted sequences, which frequently led to restricted and predictable interactions. On the other hand, fuzzy logic adds a degree of flexibility and ambiguity to the decision-making processes of NPCs. By improving the naturalness, adaptability, and emotional resonance of NPC behavior, fuzzy logic may greatly enhance player experiences in games. It provides players with progressively more engaging and dynamic gaming experiences as it continues to be incorporated into game production, opening up new possibilities for interactive narrative, gaming, and immersive virtual worlds. Based on Utku Kose's article, "Developing a Fuzzy Logic Based Game System," I have investigated the use of fuzzy logic in gaming contexts for NPC decision-making in this study.

This study explores the application of generative artificial intelligence (AI) in video games, focusing on improving content richness and personalized player experience. Traditional non-player character (NPC) systems have limitations in adaptability and interactivity. This study explores how generative AI can improve NPC behavior and dialogue. Then, this paper proposes a framework that combines generative agents, Transformer-based dialogue models, and reinforcement learning. Specifically, the generative agent simulates memory-driven planning, the Transformer model generates context-aware dialogues, and reinforcement learning supports adaptive interactions. This study is based on a generative agent dataset and a Role-playing game (RPG) dialogue corpus. The results show that the proposed method enhances the realism of NPCs, the coherence of game narratives, and the responsiveness of player interactions, and improves player immersion and the diversity of interactions. This provides practical insights for scalable intelligent game development and shows the potential of artificial intelligence in automating complex content creation and points out the direction for the future combination of games and artificial intelligence.

Video games, particularly role-playing games (RPGs), have become narrative and emotional spaces where players not only follow complex plots but also form affective bonds with non-player characters (NPCs). This article analyses such parasocial relationships through two perspectives: the Affectivity System (AS), which structures the bond between player and NPC, and the model of emotions in video games, which distinguishes among narrative (Fictional), aesthetic (Artefact), and interactive (Gameplay) emotions. The focus is on Clair Obscur: Expedition 33 (Sandfall Interactive, 2025), a European JRPG that integrates mechanics designed to build deep emotional relationships with NPCs. Here, affective interaction is not a byproduct but a central component of the design. The article opens with a reflection on the concept of para-sociality, showing how the interactivity of video games transforms the player into an active agent in the relationship. In this context, the ability to recognize emotions and intentions in virtual characters triggers genuine empathetic responses, making the experience deeply engaging.

Creating the behavior for non-player characters (NPCs) in video games is a complex task that requires the collaboration among programmers and game designers. Usually these game designers are responsible of configuring and fine tuning certain parameters of the behavior, while programmers write the actual code of those behaviors. That requires several iterations between them. In this paper, we present a new approach for creating the behavior of NPCs that gives more power to the game designer to create behavior without technical knowledge using program by demonstration but preserving the designer confident of the final behavior.

This paper firstly seeks an answer to the question of whether Non Player Characters (NPCs) in Computer Games can also be viewed as game agents where reactivity, autonomy, being temporarily continuous and having goal-oriented behavior are taken as the features of being a game agent. Within this scope, we will try to assess whether naming NPCs as agents point to a desire that one day they would fulfill the requirements of being an agent or whether NPCs actually already fulfill these requirements. Secondly, the paper looks into the AI needs of video games. We present the AI methodologies that are either being used and under research for use in Game AI. The same methodologies are also likely to contribute to the increasing levels of autonomy and goal-directed behavior of NPCs and help them become more agent-like.

Americans are increasingly concerned about video games, presumably due to the amount and graphicness of violence they contain. Social Cognitive Theory suggests that people are more likely to imitate characters they see as attractive or similar to self. To date, however, little research has examined attributes of violent characters in video games related to this issue. This content analysis examined 10 minutes of play from 60 of the most popular video games. Adapting the coding scheme from the National Television Violence Study (Smith et al., 1998; Wilson et al., 1997, 1998), various demographic and contextual features of violent characters and violent interactions were assessed. The results show that violent game characters have attributes that are likely to increase the extent to which some players perceive them as attractive and similar. Moreover, these violent characters engage in aggression that is presented as justified or graphic.

Non-playable Character (NPC) is one of the important characters in the game. An autonomous and adaptive NPC can adjust actions with player actions and environmental conditions. To determine the actions of the NPC, the previous researchers used the Neural Network method but there were weaknesses, namely the action produced was not in accordance with the desired so the accuracy was not good. This study overcomes the problem of poor accuracy by using the Logarithmic Learning for Generalized Classifier Neural Network (L-GCNN) method with 6 input parameters, NPC health, distance from players, other NPCs involved, attack power, number of NPCs and NPC levels. While the output is to attack itself, attack in groups and move away. For testing, this study was tested on RPG games. From the results of the experiments conducted, it shows that the L-GCNN method has better accuracy than the 3 methods compared to 7% better than NN and SVM and 8% better than RBFNN because in the L-GCNN method there is an encapsulation process that is data have the same class will. Whereas the L-GCNN training time is 30% longer than the NN method because on L-GCNN one neuron consists of one data where there are fewer NNs in the hidden layer.

Non-Playable Characters (NPCs) are a subtype of virtual agents that populate video games by endorsing social roles in the narrative. To infer NPCs’ roles, players evaluate NPCs’ appearance and behaviors, usually by ascribing human traits to NPCs, such as intelligence, likability and morality. In particular, hostile NPCs in video games are essential to build the games’ inherent challenges. The three experiments reported here investigated the extent to which the perception of hostility in a military shooter game (including both threat of appearance and aggressiveness in behaviors) is influenced by the appearance and the behaviors of NPCs thanks to perceived intelligence, likability and morality-related questionnaires. Our results first show that hostility is efficiently conveyed through NPCs’ behaviors, but not significantly by their appearance. Second, our study allows identifying the main predictors of hostility perception, namely unfriendliness, knowledge and harmfulness.

The article presents the results of a study on the possibilities of building software behavioral components based on large language models (LLMs) and their use in gaming systems. The subject of the research is the behavioral component of non-player characters (NPCs) in computer games, integrated with an LLM. The aim is to explore and analyze the features of applying LLMs to simplify NPC development. The study examines game AI types – reactive, state-driven, and learning-based. Interaction with NPCs through LLMs for chat communication is considered, and analogues of such characters’ behavior and dialogue are explored. The outcome is a designed and implemented API service for interaction with LLMs and an external gaming system with a corresponding software behavioral component that provides functionality for LLM-based interaction. Testing has shown that this system works with 54 unique contextual properties, achieves a maximum system prompt generation time of up to 60 ms, a maximum LLM response parsing and validation time of up to 30 ms, and supports models with at least 14 billion parameters. Keywords: API, LLM, NPC, XML, contextual action, contextual property, software behavioral component.

Role-playing games (RPGs) rely on interesting and varied experiences to maintain player attention. These experiences are often provided through quests, which give players tasks that are used to advance stories or events unfolding in the game. Traditional quests in video games require very specific conditions to be met, and for participating members to advance them by carrying out pre-defined actions. These types of quests are generated with perfect knowledge of the game world and are able to force desired behaviors out of the relevant non-player characters (NPCs). This becomes a major issue in massive multiplayer online (MMO) when other players can often disrupt the conditions needed for quests to unfold in a believable and immersive way, leading to the absence of a genuine multiplayer RPG experience. Our proposed solution is to dynamically create quests from real-time information on the unscripted actions of other NPCs and players in a game. This thesis shows that it is possible to create logical quests without global information knowledge, pre-defined story-trees, or prescribed player and NPC behavior. This allows players to become involved in storylines without having to perform any specific actions. Results are shown through a game scenario created from the Panoptyk Engine, a game engine in early development designed to test AI reasoning with information and the removal of the distinction between NPC and human players. We focus on quests issued by the NPC faction leaders of several in-game groups known as factions. Our generated quests are created logically from the pre-defined personality of each NPC leader, their memory of previous events, and information given to them by in-game sources. Long-spanning conflicts are seen to emerge from factions issuing quests against each other; these conflicts can be represented in a coherent narrative. A user study shows that players felt quests were logical, that players were able to recognize quests were based on events happening in the game, and that players experienced follow-up consequences from their actions in quests.

Although barely explored so far, swarm intelligence can arguably have a profound impact on video games; for instance, as a simple yet effective approach for the realistic intelligent behavior of Non-Player Characters (NPCs). In this context, we describe the application of particle swarm optimization to the behavioral design of NPCs in a first-person shooter video game. The feasibility and performance of our method is analyzed through some computer experiments. They show that the proposed approach performs very well and can be successfully used in a fully automatic (i.e., without any human player) and efficient way.