WRITING FOR EACH OTHER: DYNAMIC QUEST GENERATION USING IN SESSION PLAYER BEHAVIORS IN MMORPG

Anagram is a turn-based role-playing game where two players construct words by arranging given letters. A significant aspect of playing a game is the challenge. A good challenge comes from an opponent with a close ability. In a two-player game like Anagram, the second player can be a nonhuman player called Non-Playable Character (NPC). A balanced game is more engaging. Therefore, it is imperative to insert artificial intelligence (AI) into an NPC to make it possess a balance ability. This study investigates the AI algorithm that is the most appropriate to make a balance NPC for Anagram games. We tested three scenarios: Descending AI, Random AI, and AI with k-Nearest Neighbour (k-NN). Descending AI gets an Anagram solution by selecting a word with the highest score from all possible answers. Random AI picks a word randomly from the possible answers, while AI with k-NN chooses a word closest to one of the human players. The results show that Descending AI is the best algorithm to make the strongest NPC, which always gets the highest score, followed by Random AI and AI with k-NN. However, AI with the k-NN algorithm makes the constructed NPC has the highest number of turns at an average of 18, while Descending AI gets 14 turns and Random AI has 15 turns. Looking at the remaining lives at the end of the game, AI with k-NN makes the NPC has 25 lives left, while Descending AI has 59 lives, and Random AI has 48 lives. Less remaining lives suggest that NPC containing AI with the k-NN algorithm matches closer to the human player and therefore is more suitable for Anagram NPC.

Game ini berjenis role playing game (RPG) dengan tema Game Pemadam Kebakaran. Dalam role skenario, terdapat dua karakter pemain, yakni human player dan komputer player. Karakter human player memiliki dua misi yaitu menyelamatkan hutan yang terbakar dengan cara memadamkan dan mengalahkan karakter komputer player menggunakan senjata air, sedangkan misi komputer player kebalikannya. Khusus untuk komputer player, karakternya bergerak secara mandiri. Karakter komputer player berupa NPC (non-player character) dimana pada permainan ini berwujud karakter monster-api. Perilaku mandiri dari NPC dikendalikan oleh algoritma yang disematkan padanya. Pada artikel ini dijelaskan implementasi metode Algoritma Genetika (GA) sebagai metode yang melatarbelakangi perubahan perilaku pada NPC bersifat acak dipengaruhi berdasarkan kondisi parameter disekitarnya. Parameter tersebut diinisialisasikan sebagai variabel dalam struktur algoritma genetiknya. Metode ini berjalan dengan memaksimalkan nilai parameter yang disimpan dalam gen pada suatu kromosom suatu individu. Pada satu individu kromosom yang dibangkitkan, terdapat empat gen seperti kemampuan menyerang (Attack), kecepatan berpindah (Movement Speed), kecepatan menembak musuh (Attack speed) dan parameter ketahanan (Health point). Perilaku NPC pada karakter monster-api tergambarkan dalam diagram finite state machine (FSM). Pada FSM tampak perilaku dalam bentuk state, event, dan action dimana kondisi dapat berubah sesuai interaksi yang terjadi dan direncanakan dalam godot scene. Hasil ujicoba pada penelitian ini menunjukkan bahwa pembangkitan populasi pada GA sampai dengan generasi ke-5 mendapatkan nilai parameter fitness 134,87. Untuk pembangkitan populasi hingga generasi ke-10, nilai fitness 128,64. Pada setiap pembangkitan generasi akan memperoleh nilai parameter fitness yang optimum ditunjukkan dengan perolehan nilai gen pada kromosom populasi mendapat nilai yang tertinggi.

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.

Affective game engines could support game development by providing specialized emotion sensing (detection), emotion modeling, emotion expression and affective behavior generation explicitly tailored towards games. In this chapter we discuss the rationale for specialized emotional appraisal engines for games, analogous to having specialized physics engines. Such engines provide basic emotion modeling capabilities to generate emotions for Non Player Characters (NPCs), just like the Havok engine provides physics-related special purpose processing. In particular, such engines provide NPCs with an emotional state by simulating the emotional meaning of an event to an NPC in the context of the game’s storyline, the NPC’s personality, and relationships with other NPCs. We discuss why such engines are needed, present an example approach based on cognitive appraisal, and show how this appraisal engine has been integrated in a wide variety of architectures for controlling NPCs. We conclude with a discussion of novel gameplays possible by the more sophisticated emotion modeling enabled by an emotion appraisal engine.

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.

In modern Massively Multiplayer Online Games, Non Playing Characters (NPCs) moving on the battlefield play a key role in term of user experience: many time players are required -- alone or in groups -- to fight or avoid them in order to progress in experience. Unfortunately, standard NPCs behavior, i.e., patrolling between rally points, does not put a significant challenge to players once its deterministic movement pattern is discovered. This paper addresses the problem of defining a smart, more challenging, and natural movement model for NPCs.

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.

Non-player characters (NPCs) are a common feature in contemporary videogames, particularly role-playing games (RPGs). Evidence suggests player relationships with these fictional, digital characters can manifest as deeply emotional experiences that can ‘bleed’ off the screen and affect the daily lives of players. However, research in this area is still in its infancy, and as yet has not been given a thorough conceptual treatment. Applying the sociological phenomenology of Alfred Schütz, this paper will examine the structure of the experiences that players have with NPCs, and how these experiences manifest as meaningful social experiences. By a reconceptualization of the player-NPC relationship as a deeply mediated human-human relationship, this paper aims to build a foundation for further phenomenological study into how players engage with fictional characters in immersive videogame worlds.

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.

Non-Player Characters (NPCs) are a vital part of computer games. Their behaviour is usually pre determined by the developers before a computer game is released. While these NPCs exist in a virtual environment in which they aim to achieve their objectives, these objectives might involve certain actions which should be both believable and add realism to games. In internet gameplay, these may range from navigation of the environment, attack and defense of an objective or assisting other NPCs or human players to achieve their goals. Various forms of decision making process are now implemented to provide believability and realism to games. These techniques range from reinforcement learning, imitation learning, supervised and unsupervised learning for online and offline games. While these are widely implemented, a new genre of realism is being added to games especially first person shooter games, namely Smart Terrains with virtual destructive environments. Smart Terrain has the potential to improve the realism of games and the behavior of the NPC in games such as first person shooter games where gameplay is fast paced. The potential issue would be if NPCs are dynamic enough to recognize changes in the virtual environment or if the virtual environment provides the required information to assist the NPCs with their objectives or tasks. This paper presents an overview of Smart Terrain in enhancing Online Tactic Agent decision making.

Developing a virtual 3D environment by using game engine is a strategy to incorporate various multimedia data into one platform. The characteristic of game engine that is preinstalled with interactive and navigation tools allows users to explore and engage with the game objects. However, most CAD and GIS applications are not equipped with 3D tools and navigation systems intended to the user experience. In particular, 3D game engines provide standard 3D navigation tools as well as any programmable view to create engaging navigation thorough the virtual environment. By using a game engine, it is possible to create other interaction such as object manipulation, non playing character (NPC) interaction with player and/or environment. We conducted analysis on previous game engines and experiment on urban design project with Unity3D game engine for visualization and interactivity. At the end, we present the advantages and limitations using game technology as visual representation tool for architecture and urban design studies.

In massively multiplayer online games (MMOGs), complex social environments exist in which cooperation is central not only to playing the game but also to experiencing it as an individual player. The growth of multiplayer games that emphasise cooperative activities in computer-based environments has sparked academic interest in collaboration and its role in the field, engaging scholars from domains such as human–computer interaction and digital entertainment. This paper presents a systematic literature review (SLR) and bibliometric analysis of 70 peer-reviewed journal papers published between 2015 and 2024. This data is derived from the Web of Science and Scopus databases. This literature review contributes to the understanding of collaborative factors in MMOGs, which include task interdependence, communication, trust, leadership, and player behaviour. The review is in the field using bibliometrics. To present the findings, we construct an input–process–output (IPO) model that links game features (inputs) and interaction dynamics (processes) to team performance and player experience (outputs) in MMOGs. This review maps the field’s dominant factors (task interdependence, communication, trust, leadership, and player behaviour), pinpoints methodological priorities, and sets a concrete agenda for future research on team collaboration in MMOGs.

While gaming technologies are typically leveraged for entertainment purposes, our experience and aspiration is to use them to encourage engagement with global, politically-sensitive issues. This chapter focuses on our game design concerning the struggle of Uganda, a design that allows players to experience the atrocities and inhumane conditions and, by illuminating such values as peace and justice, helps them more generally to appreciate the moral complexity of a humane intervention. Rather than theoretical constructs to be debated in the abstract, the ethical struggles involved in determining a humane intervention in the game setting are grounded in different Non-Player Characters’ perspectives and operationalized within the underlying game dynamics. Beyond reporting on the designed game, the chapter draws the reader into the struggles of designing such an ethically contentious game.

Imagine a scenario in which players engage in conversations with non-playable characters (NPCs) in video games using a microphone, free from scripted responses or predefined constraints. With the advancement of Generative Artificial Intelligence (GAI), this possibility has become more feasible than ever. This article examines the Mantella mod for The Elder Scrolls V: Skyrim (Bethesda, 2011), which facilitates natural language interactions with NPCs, transforming player engagement within the game’s fictional universe. Skyrim is an action role-playing game (RPG) developed by Bethesda Game Studios. With over 60 million copies sold and releases spanning three generations of gaming consoles, Skyrim has cemented its place in gaming history. A significant factor contributing to its enduring relevance is its vibrant modding community, which has enabled players to modify and expand the game in myriad ways – ranging from graphical enhancements to the introduction of new areas and survival mechanics. This extensive capacity for customization has sustained the game’s popularity for over a decade since its initial release. The primary objective of this article is to investigate the creative appropriation of GAI by Skyrim's PC modding community. Specifically, it addresses the following question: What AI imaginaries emerge from Skyrim gameplay influenced by the Mantella mod? To explore this question, 13 YouTube videos were collected using ethnographic techniques, complemented by a formal gameplay analysis.

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.