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Artificial Intellegences in video game are important things that can challenge game player. One of them is creating character or NPC Follower (Non-player character Follower) inside the video game, such as real human/animal attitude. Artificial Intelligences have some techniques in which pathfinding is one of Artificial Intellegence techniques that is more popular in research than other techniques. The ability to do dynamic pathfinding is Dynamic Particle Chain (DPC) algorithm. This algorithm has the ability of flocking behavior called boid to explore the environment. But, the algoritm method moves from one boid’s point to another according to the nearest radius, then it will be able to increase computation time or needed time toward the target. To finish higher computation problem in dynamic pathfinding, the researcher suggests an algorithm that is able to handle dynamic pathfinding process through attractive potential field function of Artificial Potential Field to start pathfinding toward the target and flocking behavior technique to avoid the obstacle. Based on the test result by simulation of moving environment and complex, the computation time of algorithm is faster than comparison algorithms, DPC and Astar. It concludes that the suggested method can be used to decrease computation level in dynamic pathfinding.

Game with strategy genre is a type of game that is quite popular among children and adults. The strategy game itself has various indirect benefits that will be experienced by the players, where players must think and analyze what steps or actions must be taken before taking action. As one of the key characters for building challenges in the game, the Finite State Machine method will be applied to NPCs and objects that will trigger traps. By using the FSM method, each state can apply a variety of algorithms that allow NPCs to perform various actions when there is an interaction with a player. The results of this study are in the form of a game application that can be run on the windows desktop platform and it can be concluded that almost all functions both on objects, main functions, and enemies can run well, and 86% of 10 respondents gave good responses. Thus the use of the finite state machine method is suitable to be applied to NPC games with the action strategy genre.

Bahasa Inggris merupakan bahasa internasional, dan di Indonesia juga bisa menjadi bahasa sehari-hari di dalam masyarakat. Hal tersebut menjadikan pembelajaran Bahasa Inggris sangat penting untuk dikenalkan semenjak kecil. Fokus dari penelitian ini adalah mengembangkan sebuah media pembelajaran Bahasa Inggris yang dapat menarik minat belajar anak. Bentuk dari media pembelajaran Bahasa Inggris berbasis game, karena seorang anak akan lebih tertarik jika media pembelajaran penuh dengan warna, bentuk dan karakter. Perancangan dari game ini memanfaatkan Finite State Machine yang merupakan sebuah pemodelan untuk membangun tingkah laku dari non player character (NPC). Di dalam media pembelajaran Bahasa Inggris ini, NPC menggambarkan karakter musuh sehingga game semakin menarik untuk dimainkan oleh anak – anak. Hasil dari penelitian ini adalah sebuah game yang dapat dimanfaatkan sebagai salah satu media untuk pengayaan materi, dalam proses belajar mengajar yang bertumpu pada perkembangan teknologi informasi. Pembelajaran Bahasa Inggris berbasis pada game juga dapat menarik minat anak, hingga dapat meningkatkan nilai rata – rata sebesar 81.9, dibandingkan saat belajar Bahasa Inggris secara konvensional yang rata – rata memperoleh nilai sebesar 67.1

Tic-Tac-Toe is one of the board games. It is played by filling the columns on the board with X or O in such a way as to form parallel lines vertically, horizontally and diagonally. This study aims to design Non-Player Characters (NPC) in the tic-tac-toe game with the minimax algorithm. The Tic-tac-toe game will be designed with two game modes: easy and minimum random modes. While in minimax NPC mode will determine the best step. The game development process of the tic-tac-toe application is based on a linear sequence process model. In the analysis phase, the NPC will be designed based on the concept of minimax. Software modeling was designed using Unified Modeling Language (UML), and coded with Visual Basic programming. Our tests show that NPCs with the Minimax algorithm can work well.

Self-report assessment is important for research and game development, e.g., to gather data during play. Games can use dialogues with non-player characters (NPCs) to gather self-report data; however, players might respond differently to dialogues than questionnaires. Without guidance on how in-game assessment affects player perceptions and experiences, designers and researchers are in danger of making decisions that harm data quantity and quality, and perceptions of privacy. We conducted a user study to understand self-report collection from NPC dialogues and traditional in-game overlay questionnaires. Data quality and player experience measures autonomy, curiosity, immersion, and mastery did not differ significantly, although NPC dialogues enhanced meaning. NPC dialogues supported an increase in data quantity through voluntary 5-point scales but not via open responses; however, they also increased the perceived intimacy of shared information despite comparable objective intimacy. NPC dialogues are useful to gather quantitative self-report data. They enable a meaningful play experience but could facilitate negative effects related to privacy.

Penerapan kecerdasan buatan (AI) dalam sebuah game merupakan salah satu inovasi teknologi. Variasi game salah satunya adalah First Person Shooter (FPS). Kecerdasan buatan yang dikembangkan untuk merancang perilaku Non Player Character (NPC) dimana NPC dapat berperilaku secara bertahap dan berurutan sesuai state. Oleh sebab itu dikembangkan dengan menerapkan metode Finite State Machine (FSM) pada game “Zombie Colonial Wars”. Penerapan metode Finite State Machine (FSM) dalam game Zombie Colonial Wars berfungsi untuk melihat variasi respon perilaku pada NPC sesuai interaksi yang dilakukan oleh player. Hasil dari penelitian ini adalah game zombie colonial wars menghasilkan sebuah kecerdasan buatan pada perilaku NPC dengan mengimplementasikan metode finite state machine sehingga musuh dapat berperilaku berdasarkan interaksi yang telah dilakukan oleh player.

Game education is a game that aims for educational and entertainment. But the educational game is not popular to users. Therefore, it takes innovation and other ways to improve the educational game itself. Based on the current user survey, games that have dynamic elements such as artificial intelligence are sufficiently requested by users. For this reason, this study aims to build educational games that implement artificial intelligent (AI). In a game, AI is often implemented as Non-Player Character (NPC). The method used for NPCs in this game is based on the Natural Language Processing approach. This is so that the NPC can answer questions about English automatically. The results show that the educational game that has this NPC gets an average score above 75% of users. In addition, users also provide positive feedback on the game itself apart from the questionnaire. So it can be concluded that the presence of NPCs in educational games can increase user interest. With this new approach, it is hoped that it can also increase the popularity ranking of the educational game genre.

Video game non-player characters (NPCs) are a type of agent that often inherits emotion models and functions from ancestor virtual agents. Few emotion models have been designed for NPCs explicitly, and therefore do not approach the expressive possibilities available to live-action performing actors nor hand-crafted animated characters. With distinct perspectives on emotion generation from multiple fields within narratology and computational cognitive psychology, the architecture of NPC emotion systems can reflect the theories and practices of performing artists. This chapter argues that the deployment of virtual agent emotion models applied to NPCs can constrain the performative aesthetic properties of NPCs. An actor-centric emotion model can accommodate creative processes for actors and may reveal what features emotion model architectures should have that are most useful for contemporary game production of photorealistic NPCs that achieve cinematic acting styles and robust narrative design.

The aim of this project is to compare two popular machine learning methods, a non-gradient-based algorithm such as neuro-evolution with a gradient-based reinforcement learning on an irregular task of training a car to self-drive around 3D circuits with varying complexity. A series of 3D circuits with a physics based car model were modeled using the Unity game engine. The data collected during evaluation show that neuro-evolution converges faster to a solution when compared to the reinforcement learning approach. However, when the reinforcement learning approach is allowed to train for long enough, it outperforms the neuro-evolution in terms of car speed and lap times achieved by the trained model of the car.

Представлено вирішення завдання удосконалення моделі поведінки неігрового персонажу, що виступає в якості інтелектуального агенту, за рахунок оптимізації вхідних параметрів на основі генетичного алгоритму. Запропонований підхід включає розробку моделі неігрового персонажу: скелету, твердих тіл та реалізація динамічної моделі персонажу, заснованої на алгоритмі Фізерстоуна, та моделювання поведінки персонажу на основі генетичного алгоритму. Запропоновано формування моделі поведінки із використанням генетичного алгоритму, що моделює фізичні властивості персонажу з урахуванням його дій. Етапи роботи генетичного алгоритму включають: створення початкової популяції, оцінка пристосовуваності, відбір, схрещування та мутації. За результатами проведених експериментів визначено вхідні параметри моделі поведінки неігрового персонажу, що максимізують сукупну оцінку пристосовності, яка виступає в якості оцінки винагороди та які можуть бути використані як початкові вхідні значення при проведенні подальших експериментів. A solution of improving the behavior model of a non-player character as an intelligent agent by optimizing input parameters based on a genetic algorithm is presented. The proposed approach includes the development of a non-player character model: a skeleton, rigid bodies, the implementation of a dynamic model based on the Featherstone algorithm, and modeling of the character's behavior based on a genetic algorithm. The formation of a behavior model using a genetic algorithm that simulates the physical properties of a character, taking into account his actions, is proposed. The stages of the genetic algorithm include creating an initial population, fitness score, selection, crossing and mutation. Based on the results of the experiments, the input parameters of the non-player character behavior model were determined, maximizing the cumulative fitness score, which acts as an estimate of the reward, which can be used as initial values for further experiments.

Video game designers express a need for a tool to create a human-like Non-Player Character (NPC) that makes contextually correct decisions against inputs performed at unforeseen times. This tool must be able to integrate with the designer’s current game engine, such as the Unreal Engine. To make an NPC more human-like, we use psychology theories, specifically, emotions in decision making, such as the ideas proposed by A. Damasio. This approach allows an NPC’s psychology to connect directly to the underlying intrinsic motivations that drive players. This paper presents the “Artificial Psychosocial Framework” (APF) and defines a new class of NPC known as an “Affective NPC” (ANPC). APF utilizes an ANPC’s personality, perceptions, and actions to generate emotions, which influences the social relationship they have with other ANPCs. Additionally, APF considers the generation of emotions as an ambivalent hierarchy and classifies the representation of a personality and social relation. APF can work in tandem with existing AI techniques, such as Fuzzy Logic, Behavior Trees, Utility Theory, and Goal-Oriented Action Planning, to provide an interface for developers to build emotion-centric gameplay. APF is a library for designers to use as a plug-in to a familiar game engine, such as the Unreal Engine. On the macro-level, APF uses the “Cognitive Appraisal Theory” (CAT) of emotions. On the micro-level, psychological models are chained together to determine the emotional state and social relation. The personality of an ANPC is represented by the Big Five: “Openness, Consciousness, Extroversion, Agreeableness, and Neuroticism,” also called the OCEAN model. The emotions of an ANPC are described by the “Ortony, Clore, and Collins” (OCC) model. The social relation between ANPCs uses the “Liking, Dominance, Solidarity, and Familiarity” (LDSF) model. This paper demonstrates APF by simulating a scenario from the “Dennis the Menace” setting, where each Dennis is an ANPC with a different personality mapped to a Myers-Briggs type indicator. The results demonstrate how a different personality profile will cause varying emotional states, responses, and actions for the two simulated ANPCs in the same simulated scenario.

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.

Can intelligent non-player game characters (NPCs) increase children's creativity during collaborative gameplay? Children's creativity is influenced by collaborative play with creative peers through social emulation. In this paper, we study children's emulation of an AI-enabled social Non-Player Character (NPC) as a new type of game mechanism to elicit creative expression. We developed Magic Draw, a collaborative drawing game designed to foster children's figural creativity that allows us to investigate the efficacy of an NPC's creativity demonstration in enhancing children's creativity in the resulting drawings. The NPC is an emotively expressive social robot that plays Magic Draw with a child as a peer-like playmate. We present the results of a study in which participants co-draw figures with a social robot that demonstrates different levels of figural creativity, to understand whether an NPC's creativity in its own contributions stimulates figural creativity in children. 78 participants (ages 5--10) were randomly assigned to a non-creative robot control condition (C-) and a creative robot condition (C+). Participants who interacted with the creative robot generated significantly more creative drawings, and hence exhibited higher levels of figural creativity. We infer that the social robotic peers' demonstration of figural creativity in a collaborative drawing game is emulated by young children. We discuss a new game design principle grounded in the social learning mechanism of emulation, specifically, that social and intelligent NPCs in games should demonstrate creative behavior to foster the same in human players.

This paper reports on the first steps toward the construction and evaluation of a novel methodological approach to the study of non-player characters in games, NPC Ethnography. In this approach, ethnographic data collection and analysis methods are applied to the study of game NPCs. The proposed approach has been tested in a single case study, an exploration of the appearance of “independent lives” among NPCs in the computer roleplaying game Fallout 4. This case study was motivated by the suggestion that the appearance of NPC autonomy is a key contributing factor to the commonly desired qualities of realism and believability. A total of 273 hours of ethnographic fieldwork were conducted, and ethnographic analysis of the resulting data led to an emergent framework of evidence for the appearance or illusion of NPCs leading independent lives. In this paper I discuss and evaluate the applicability of real-world ethnographic methods to NPCs as virtual human subjects, as explored through the case study. The ability to apply most of the tested methods to NPCs without significant modification to textbook examples of real-world applications supports NPC Ethnography as a valid methodological approach. The generation of useful results through the preliminary Fallout 4 case study illustrates the potential utility of NPC Ethnography as a methodology for applied NPC studies in games and virtual environments.

The creation of believable characters is one of the most challenging problems in the interactive entertainment industry. Although there are different tools available for designers and programmers to define the behavior of non-player characters, it remains a complex and error prone process that requires a high level of technical knowledge. Learning from Demonstration is a promising field that studies how to build intelligent agents that are able to replicate behaviors, learning from demonstration of human experts. This approach is interesting for developers who do not have a computer science background, alleviating the need of representing tasks and knowledge in a formal way. In this work we present an online and case-based reasoning agent that learns how to imitate real players of Pac-Man using an interactive approach in which both the human player and the computational agent take turns controlling the main character. In our previous work, the agent was in complete control of the learning process so it decided when to give up or regain control of the character. Now the system have been improved so the player can also regain control of the character and go back in time to correct improper behaviors manifested by the agent whenever they are detected. We also present an evaluation of the system performed by three professional video game designers, followed by the main insights we have gained.

Previous research has shown that adaptive training (AT) is an effective tool for improving training outcomes relative to non-adaptive approaches. Taking a value-added perspective, in this study we sought to determine whether the presence of an embedded non-player character (NPC) served to improve performance outcomes relative to AT alone. To support this research, we utilized the Submarine EW Adaptive Trainer (SEW-AT) as our testbed, which is a scenario-based AT system that simulates a trip to periscope depth. The submarine EW operator’s role is to monitor the radio frequency signals in the environment and submit reports of the contact picture at prescribed intervals (i.e., scheduled reports) and irregular intervals as the environment changes (i.e., unscheduled reports) to the Officer of the Deck (OOD). Sixty-eight U.S. Navy EW operators completed training with one of two versions of SEW-AT: one with an NPC OOD and one without. The NPC OOD was designed carefully to provide realistic immediate feedback to the trainee while also minimizing distraction from the task. In general, all EW operators improved their performance using SEW-AT, but those using SEW-AT with the NPC OOD displayed significantly greater improvement in scheduled report timeliness than those who used SEW-AT without the NPC OOD. These results suggest that the addition of the NPC OOD to provide immediate feedback added an overall benefit over AT alone.

Avatare und NPCs in Computerspielen haben ihre eigene feste Aufgabe und Rolle innerhalb des vom Spiel festgelegten Universums. Sie lassen sich nicht physisch greifen und weiterverarbeiten, wie etwa die Figuren eines Monopoly-Spiels. Dennoch lässt sich beobachten, dass Fans bestimmter Computerspiel-Reihen, wie etwa DRAGON AGE, die Charaktere des Spiels aus ihrem Kontext nehmen und sie weiterverarbeiten. Dies geschieht nicht nur in Form von Fanfiction – sondern ebenfalls in Form von kurzen Comics und Fandiskussionen, welche zum Teil sogar eine Abneigung gegenüber dem eigentlichen Spiel ausdrücken. Charaktere müssten vor dem Computerspiel sprichwörtlich gerettet werden. Vor allem die Form der von den Fans produzierten Kunst, welche sich mit den Charakteren auseinandersetzt, wird mitunter kommerziell vertrieben. In einer ethnografischen Beobachtung soll sich die Art und Weise der Spieler*innen angesehen werden in welcher sie NPCs und Avatare weiterverarbeiten, sowie die Gründe und Vorgehensweisen des kommerziellen Vertriebs von Fans für Fans.

The joint anti-accident exercises between multilevel dispatching and control systems are using dispatcher training simulator (DTS). There are many problems with the existing DTS, such as communication difficulties, extraterritorial role simulation difficulties, uniformity of the training process, thus trainees cannot conduct self-training. This paper introduces non-player character (NPC) simulation technology into DTS to customize and intelligentize the training process. This paper firstly fused the NPC features of the game with the DTS system and established NPC model and behavior tree in DTS. Then simulation is conducted in behaviac software to realize the autonomous guidance and intelligent control of the simulation training process. The feasibility and effectiveness of NPC application in DTS are verified by comparison with the traditional DTS training process.

in a game, artificial intelligence plays a significant role, a lot of logic and algorithms are applied Therein, Including in an RPG game. Fuzzy logic is a branch of artificial intelligence systems (artificial intelligence) that emulates the ability of humans to think in the form of algorithms, which are then run by machines. Generally, each opponent and the player character has a health status as a life limit, and the opponent character has a fuzzy input parameter is the length of the NPC life limit, distance to the player and player health. Also, in this character output parameters are blurred, dodged, Attacked, and calm. From the input parameters of fuzzy logic will then use the which will choose one output parameter. From the results of the implementation of producing activities when the conditions are weak and strong

Racing game is one of the genre that’s still popular today. Unity is one of many game engines one can use to develop a racing game. At Unity Asset Store, there is a free template called Micro-Game Karting which can only be played alone. In order to play player versus enemy mode, an artificial intelligence (AI) is needed for directing non-player character (NPC) who acts as the opponent. In racing game, the AI requires the use of movement algorithm and decision making system. For this study, the movement algorithm will use pathfinding. The algorithm is used as a guiding path when NPC is moving and avoiding obstacles in the way. Pathfinding will use waypoint system and raycasting to accomplish it. The decision making technique that will be used is decision tree. It functions as decision maker for NPC so it can determine the correct action to be done at certain time. Result of black box and white box testing showed NPC is functional. As for FPS (frame per second) test, performance suffers 0.2-0.3 FPS decrease for every addition of 2 NPCs. According to lap time test, the developed NPC is faster than ML NPC and driving test showed favorable outcome.

In this study we aim to describe in what ways the behavior of non-player characters (NPCs) affects to what extent the player finds the game experience to be believable. To this end, we have conduct ...

Can social agents be assertive and persuade users? To what extent do the persuasion abilities of robots depend on the users' own traits? In this paper, we describe the results of a study in which participants interacted with robotic Non-Player Characters (NPC) displaying different levels of assertiveness (high and low), in a storytelling scenario. We sought to understand how the level of assertiveness displayed by the robots impacted the participants' decision-making process and game experience. Our results suggest that NPCs displaying lower levels of assertiveness evoke more positive emotional responses but are not more effective at influencing players' decisions when compared to NPCs displaying higher levels of this trait. However, NPCs displaying a personality trait are more effective persuaders than NPCs not displaying this feature. Overall, this paper highlights the importance of considering the player's personality and its relation to task-specific attributes during the process of game design.

Increasing the behavioral nuance and interactivity of non-player characters in story worlds comes with a growing cost in the time and effort expended by authors. This paper proposes a system, Cast Affinity Satisfiability Toolkit (CAST), which uses answer set programming to lessen this burden while supporting author autonomy. CAST uses user-defined constraints about personality facets and interests to generate a cast of characters and their relationships. Once salient personality facets and character interests are defined, additional constraints may be supplied about character similarity and affinity. This refines not only character personality but the social connections between characters. Rules about affinity, or the attraction characters feel towards each other, may be used to weight personality facets and interests to generate consistent characters and their relationships that consider the significance of these traits. Although the quality of output is contingent upon the quality of constraints, CAST respects all author supplied constraints and provides the framework to generate NPCs that are consistent and whose relationships are tailored to reflect what is significant in the story world.

Even though modern video games present rich storytelling and high graphical fidelity, they are still lacking in rich non-scripted NPC social interactions. In this work we present an extension of the Comme il Faut (CiF) and CiF-CK social interaction models, where we added emotions and a emotion appraisal process based on the OCC model of emotion and also added a belief system that models the social network values that the NPCs expect regarding the other NPCs' relationships. A version of the new model, which we named Comme il Faut - Exiles (CiF-Ex), was implemented in the AAA game Conan Exiles using their modding tools and validated. The results were noteworthy: the users enjoyed the game more and tended to spend more time near CiF-Ex enabled NPCs. The majority of the users preferred CiF-Ex NPCs, versus the vanilla NPCs. CiF-Ex NPCs were perceived as more believable and less predictable.

Giving presents can have a positive impact upon the giver's mental well-being [1-4]. We wonder whether giving a present to an AI agent could have the same effect. We investigate this question in the context of video games with non-player characters (NPCs), because the rapid expansion of the game industry makes NPCs one of the most common ways in which we experience AI agents. We seek to understand whether players benefit mentally by giving gifts to NPCs in video games. We intend to observe the behaviour of players and measure their psychological changes through a specially designed video game providing them with mechanisms for gifting to NPCs. We have taken an understanding of gifting from the real world and applied this understanding within a game. The game development has been an iterative process of design-build-playtest. Such a process helps us better understand the research space and to better describe the questions. This article reports the latest findings from the game development after playtesting with 12 participants.

Pendidikan merupakan kebutuhan pokok yang harus dimiliki semua orang. Terutama berawal dari anak usia dini sekarang cenderung menyukai game yang akhirnya membuatnya lupa dalam pembelajaran dan membuat anak menjadi lebih malas untuk belajar. Sudah banyak anak – anak sekolah dasar sekarang yang menggunakan gadget untuk bermain game. Banyak orang tua pula menginginkan anaknya agar tidak melupakan belajarnya meski dalam bermain. Berdasarkan permasalahan diatas penulis memiliki ide untuk membuat penelitian yang berisikan tentang pembelajaran mengenasi anak usia dini untuk SD kelas 1. Pada penelitian ini penulis menggunakan penelitian berupa pembuatan Game Edumatika yang dibuat dan diimplementasikan berbais Game 2D, yang menggunakan metode Fuzzy Tsukamoto dan metode Linear Congruential Generator (LCG). Penerapan metode Fuzzy Tsukamoto ke dalam game untuk menentukan kondisi dari NPC (Non Player Character) dan metode Linear Congruential Generator (LCG) untuk pengacakan kemunculan item. Hasil dari penelitian ini berupa Game Edumatika yang memiliki tampilan keseluruhan yang menarik dan berjalan dengan baik. Pada game ini dirancang bukan hanya sebagai bahan hiburan saja melainkan juga terdapat pembelajaran. Dimana orang tua tidak perlu mengawatirkan anaknya untuk lupa dengan belajar, karena didalam game ini juga terdapat media pembelajaran dengan di akhir sebuah permainan terdapat pembelajaran untuk anak usia dini. Sehingga anak tidak merasa jenuh dalam belajar melainkan juga ada hiburan dari game tersebut. Kata kunci : Game Edukasi,Platformer 2D Metode Tsukamoto, Metode LCG (Linear Congruential Generator)

In this paper, we discuss an on-going research carried out to create smart auditory based pervasive game environments by using open street map and social media data. Two examples are provided, the first showing how data from Twitter could be used to generate talking NPC (Non Player Character) as a way to represent the social presence of people who frequent different areas. In the second prototype, we show how a context aware storyteller could be developed to tell stories from books based on the characteristics of the physical world locations where the user is located. Natural Language processing and Machine learning techniques such as clustering and word embedding based modeling were used to develop the prototypes. The approaches discussed in this study would be particularly useful in developing pervasive games with serious purposes and we hope to apply these techniques to create pervasive games for mental healthcare and education.