NON-PLAYER CHARACTER IN FIRE FIGHTER GAMES USING GENETIC ALGORITHM

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.

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.

This research develops an AI-powered traffic simulation using the Unity Engine, leveraging finite state machines (FSM) to enable adaptive and responsive non-player characters (NPCs). The integration of FSM with advanced pathfinding algorithms, such as A*, allows NPCs to dynamically adjust their behavior based on traffic conditions, obstacles, and environmental changes. The experimental results indicate a 25% improvement in route optimization and a 30% reduction in path conflicts compared to conventional static models, demonstrating the robustness of the proposed approach. Optimized navmesh deployment further enhances navigation fluidity, ensuring efficient agent movement in high-density scenarios without compromising system performance. The findings establish the effectiveness of the FSM-driven NPC behavior in simulating realistic traffic environments, contributing both to the advancement of AI applications in game development and urban planning. By providing an interactive platform for traffic management, this simulation offers a practical tool to study congestion patterns and test intervention strategies. In addition, it improves player engagement by fostering emergent gameplay through dynamic NPC interactions. Future work could explore the integration of real-time procedural generation or multiplayer functionality to enrich simulation depth and scalability. This study provides a comprehensive framework that bridges AI-based mechanics with simulation technology, providing significant insights for researchers and practitioners in game design, artificial intelligence, and urban planning.

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.

The game with the Role Playing Game (RPG) genre is a competitive game, between players against other players or enemies in the form of a Non-Player Character (NPC). Many game developers in making the game itself still use manual methods in determining gameplay attributes for player or enemy characters. In this research, several approaches such as k-NN, Normal Distribution, and Naive Bayes will be used in the program to calculate the growth of gameplay attributes automatically. The player attribute calculation distribution is done twice with the same inputs to find the maximum level limit of each attribute increase. It proves that the attribute increase and distribute to the levels are correspondents to the input variable. The enemies are in a normal distribution with the highest appearance probability between level 0 and 100. For enemy types, The input variables for enemy distribution by type fits with the output.

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.

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.

In current state-of-the-art commercial first person shooter games, computer controlled bots, also known as nonplayer characters, can often be easily distinguishable from those controlled by humans. Tell-tale signs such as failed navigation, “sixth sense” knowledge of human players' whereabouts and deterministic, scripted behaviors are some of the causes of this. We propose, however, that one of the biggest indicators of nonhumanlike behavior in these games can be found in the weapon shooting capability of the bot. Consistently perfect accuracy and “locking on” to opponents in their visual field from any distance are indicative capabilities of bots that are not found in human players. Traditionally, the bot is handicapped in some way with either a timed reaction delay or a random perturbation to its aim, which doesn't adapt or improve its technique over time. We hypothesize that enabling the bot to learn the skill of shooting through trial and error, in the same way a human player learns, will lead to greater variation in game-play and produce less predictable nonplayer characters. This paper describes a reinforcement learning shooting mechanism for adapting shooting over time based on a dynamic reward signal from the amount of damage caused to opponents.

Popular games often have a high-quality graphic design but quite simple-minded non player characters (NPC). Recently, Computational Intelligence (CI) methods have been discovered as suitable methods to revive NPC, making games more interesting, challenging, and funny. We present a fairly large study of human players on the simple arcade game Pac-Man, controlling the ghosts behaviors by simple strategies, neural networks or evolutionary algorithms. The playerpsilas fun is of course a subjective experience, but we presume that it is related to the psychological flow concept. We deal with the question whether flow is a more reliable measure than asking human players directly for the fun experienced during the game. In order to detect flow, we introduce a measure based on the interaction time fraction between the human-controlled Pac-Man and the ghosts, and compare the outcome to the results of a fun measure suggested by Yannakakis and Hallam [1].

This paper presents a framework for adapting game elements to the player’s affective state and the integration of the framework in a virtual reality environment for moral development. These game elements include gestural and facial expressions of avatars during dialogues with the player, background music, the score, game mechanics, aesthetics and learning. The framework BARGAIN (Behavioral Affective Rule-based Games Adaptation Interface) is an authoring tool for affective game design providing a visual interface based on finite state machine (FSM) technique to represent the affective rules as state transitions graph dependent on the player emotional state assessed using facial expression recognition system based on electroencephalography (EEG) data. We conducted a user study (n = 29) examining the effects of the resulting affective virtual reality game on players’ experience using the Game experience Questionnaire (GEQ) (IJsselsteijn et al. in The game experience questionnaire, Technische Universiteit Eindhoven, Eindhoven, 2013). The results show significant correlation between the GEQ dimensions and the player's facial expressions during his interaction with the Non-Player Characters (NPCs) within the VR game. These findings highlight that adapting games to user's emotions enhance the players’ experience.

This research develops Android games by applying the FSM Algorithm (Finite State Machine) for the movement of NPC (Non-Player Character) to produce dynamic movements. The popular and legendary game with the appearance of side scroller is Super Mario Bross and Shovel Knight, the game is the most preferred game of its time to the present, but the game does not apply AI (Artificial Intelligence) so the game seems less challenging. The game is a concept game that is currently rarely used. Based on this, a side scroller-based survival game was created by implementing AI on NPCs on the Android platform. The AI used is the FSM Algorithm which functions to regulate enemy movements. The method used in developing multimedia products of this game uses MDLC (Multimedia Development Life Cycle). Based on the tests that have been carried out, the alpha test results are functionally appropriate and the results of beta testing using UserAcceptence Test (UAT) obtained a value of 86.40% which is declared feasible to use and can be developed. The advantages of this game are the presence of shock elements in the form of landslides, bursts of water, rock rain, and attacks by eagles