Voices, Combat, and Music

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.

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 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.

This paper presents a system that builds on theoretical and experimental insights from linguistic pragmatics, uses novel techniques from computational linguistics and combines them with robust baseline technologies to provide intelligent Non Player Characters (NPCs), which naturally act and talk in a virtual world. Current NPCs still lack the necessary linguistic knowledge and methods to apply them to the numerous conversational application areas in virtual worlds. The system presented in this paper manages two NPCs, a barkeeper and a furniture sales agent, which highly depend on conversational abilities.

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.

Game is a form of interactivity where player and game world interact to each other. In a game, one of the elements which can be considered necessary to support the course and the reality of the game is how a NPC (Non-Player Character) in the game moves. A* (A-star) is an algorithm which can be used to perform pathfinding. In this case, A* will be used to find a shortest distance between the NPC and the player character. This research was performed to conduct experiments on the implementation of the A* algorithm in the 3D game. The research will be conducted by implementing an A* algorithm to a game, precisely to the enemy characters that exist in the game. Once the implementation was conducted, an experiment will performed using certain cases as a proving ground for the implementation. As a conclusion, from a hundred experiments, A* algorithm always can find the path for enemy to catch player and 73% of them are optimal paths.

The Entertainment Education Paradigm (EEP) offers a new way to think about education by blending entertainment with educational experiences. Video games provide an excellent format for entertainment education because of both the prevalence and enjoyment of playing video games and the ways in which individuals of today learn. Role-playing games are one of the better game genres for entertainment education. They provide both high levels of entertainment and a strong connection between player and game characters (models) which lead to an increase in learning. Based on the theories of parasocial interaction, identification, and social learning, this chapter offers a measurement for character attachment and introduces this new construct as a moderator for learning in role-playing video games.

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.

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.

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].

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.

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.

Video games are a means of entertainment for everyone, from children to adults. The genre of games now is also very diverse, ranging from adventure, puzzles to storytelling, and even many folk stories have been made into video games by several developers in Indonesia. Starting from folk tales with horror themes such as kuntilanak, legends such as cucumber mas, to folk tales that rarely sound like golden oranges. The folklore video game of buah jeruk emas is a video game that tells of a king who gets a whisper from the gods to get golden oranges. The king then held a competition to get the golden orange fruit. The player must be able to take the golden orange fruit from the enemy in the form of a Non Playable Character (NPC) who will chase the player. In making NPCs, algorithms are used to help play video games. Therefore, the author wants to apply the A * algorithm in the game of golden oranges so that npc can catch up to players according to the planned system. The main method used is A * and then the addition of the FSM method for other methods. The golden orange fruit is a video game using the A * algorithm and the FSM method after testing it can be concluded that it is enough to make the game run. With the results according to the planned system.