Non-player Characters Research Articles

Machine learning methods for control of non-playable characters behaviour in multiplayer RPG

This article covers the problem of developing a control system for non-player characters in a multiplayer RPG. Commercial projects in the field of videogames and RPG (Role-Playing Game) projects in particular seldom use machine learning models for the implementation of character behaviour. The most common approach is to use primitive preprogrammed rules, or to implement a finite state machine. Such approaches ruin the immersion of playing with real creatures, since various predefined rules make the characters predictable. A good game AI is supposed to give the player an impression of interacting with real characters, that make various decisions, sometimes unpredictable. To achieve this goal, this article covers an approach with using various machine learning models in conjunction with a traditional finite state machine. A videogame developed earlier is used as the basis for problem solution. The article conducts an analysis of the existing works in the field of videogame AI. Next, the implementation of control system is described. This system utilizes a couple selected and successfully trained machine learning models. A multitude of models were tested, eventually a decision tree model and a neural network were selected, since they yielded the best results. The process of development and implementation of a control system involving machine learning models is then described. The approaches of teaching such models are described, and finally the achieved results are analyzed. To gauge the results, different models were compared against each other in test battles. The decision tree model showed results slightly better than the traditional finite state machine. Meanwhile, the neural network performed significantly better, beating other models far more often. Achieved results can be developed further, utilizing more complex models and improving training methods, which will result in even more sophisticated characters. Presence of such characters in the game will qualitatively improve the gameplay. Obtained system was integrated into the videogame, that may potentially become a commercial product.

Implementasi Algoritma Jump Point Search Pada Game Prince Of Walangsungsang

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.

Application Of 3-Dimensional Modeling In Android-Based Adventure Game Applications

Role Playing Game (RPG) is a game that has complex story elements and acting that makes a person feel like a character in the game. One of the RPG editing engines is RPG Maker MV, where a 2D RPG can be a standalone program that can be played directly without the help of this program or other programs. Quests are assigned to players by non-player characters (NPCs) that the player cannot control and interact with them through dialogue. In the game mode each player can collect things into an inventory which contains the things needed to solve certain tasks during gameplay. In addition, game objects that represent learning objects have been assigned locations which can be NPCs or other objects. In addition, game objects that represent learning objects have been assigned locations which can be NPCs or other objects. Can apply 3-dimensional modeling to role-playing game applications by placing objects on characters that can walk up, down, left and right. NPC objects can be applied to weapon objects, namely axes, if gold points are collected then the NPC object will appear. It is hoped that this implementation can be a reference not only for 3-dimensional modeling in complex role-playing game applications but also for all circles as the development of Android and IOS-based RPG games.

The Development of Interactive 3D Animal Non-Player Characters: A study of Research framework and Implementation of Framework at Actual Research Site

This is a study about the research framework and the implementation of the framework toward fieldwork, most importantly data collection and visual literature preparation for VR Longhouse: The walkthrough. The research team getting ready in the pre-research stage with the sharing and discussion of the research framework. From the very beginning stage until the finalized version. As the process of understanding the framework took place for 3 months, the research team also tried to study the references which be able to support the field work after the pre-research stage. After getting the framework the team went for the preliminary trial stage to test the usability of the framework at 2 dummy sites before embarking to Sarawak for the real fieldwork at the research site in Saribas. Testing and follow-up with modification. The process can confirm a final framework for the fieldwork in Sarawak. The team implemented the framework on the research site. The team managed to collect the visual data to support the VR longhouse: The walkthrough project. The entire process is to determine the usability of the research frame on the actual research project.

Evolutionary Algorithm in Game – A Systematic Review

Research in the game field is increasingly numerous and challenging. The high interest in research on games is influenced by public awareness of the importance of games in developing ways of thinking, although it is undeniable that many people only pursue pleasure in playing games. In the past, not much games research has influenced into topics such as artificial intelligence, education, or other computer topics. But now games are having a tremendous impact on these topics. In fact, not infrequently games are used in various areas of life. Right now, artificial intelligence is an integral part of the game. If before, it was only used for creating an enemy. Right now artificial intelligence can affect various things, starting from assets, game difficulty levels, non-player characters (NPC), and even bots (AI agents) to run player characters. The complexity of artificial intelligence which is getting higher and higher requires a good optimization algorithm. The evolutionary algorithm is one of the optimization algorithms, even though it cannot find the best one, with the high speed it can find a good solution. Through this paper review, good conclusions are drawn regarding the use of evolutionary algorithms, representations made, fitness functions used, ways to prove a success, to what topics should be studied further.

Review on the application of deep learning algorithms in video game AI agent

AI algorithms have been applied in video games more than before. Since the early 2000s, the benchmark of the application of AI agent in video games has been put forward by researchers and engineers. This paper introduces the application of four different deep learning algorithms in video games, namely Component-based Hierarchical State Machine (CBHSM) algorithm, Monte Carlo Search Tree (MCTS) algorithm, Generative Adversarial Networks (GAN) algorithm, and the combination of A* algorithm and Q-learning algorithm. It can be summarized that both CBHSM and MCTS algorithms can modify and optimize the traditional Finite State Machine (FSM) algorithm applied in NPC (Non Player Characters) system. Additionally, A* algorithm can be combined with Q-learning algorithm to solve the problem of the difficulty on huge state space and limited time, and GAN algorithm, a model of unsupervised learning, can generate results by fewer training sets.

Modelling the ethical priorities influencing decision-making in cybersecurity contexts

Purpose Cybersecurity vulnerabilities are often due to human users acting according to their own ethical priorities. With the goal of providing tailored training to cybersecurity professionals, the authors conducted a study to uncover profiles of human factors that influence which ethical principles are valued highest following exposure to ethical dilemmas presented in a cybersecurity game.Design/methodology/approach The authors’ game first sensitises players (cybersecurity trainees) to five cybersecurity ethical principles (beneficence, non-maleficence, justice, autonomy and explicability) and then allows the player to explore their application in multiple cybersecurity scenarios. After playing the game, players rank the five ethical principles in terms of importance. A total of 250 first-year cybersecurity students played the game. To develop profiles, the authors collected players' demographics, knowledge about ethics, personality, moral stance and values.Findings The authors built models to predict the importance of each of the five ethical principles. The analyses show that, generally, the main driver influencing the priority given to specific ethical principles is cultural background, followed by the personality traits of extraversion and conscientiousness. The importance of the ingroup was also a prominent factor.Originality/value Cybersecurity professionals need to understand the impact of users' ethical choices. To provide ethics training, the profiles uncovered will be used to build artificially intelligent (AI) non-player characters (NPCs) to expose the player to multiple viewpoints. The NPCs will adapt their training according to the predicted players’ viewpoint.

Playful, Sociable, Cute, Quarantined – Interactions with Kawaii Characters in Animal Crossing: New Horizons During COVID-19

The implementation of social isolation policies in response to COVID-19 coincided with Nintendo’s video game Animal Crossing: New Horizons (also known as atsumare dōbutsu no mori) gaining popularity. At this time, the game served as a means of escape and comfort for those experiencing loneliness and anxiety. The kawaii animal characters that occupy the players’ island in this game are designed to elicit social responses from players as a central part of gameplay, through dialogue, animation, and character design. This article contributes to an emerging body of work that considers the social relations formed between kawaii characters and viewers, through a digital ethnography of English-speaking Animal Crossing fan practices on Twitter, Reddit and Instagram. A key appeal of Animal Crossing during COVID-19 lockdowns resided in its kawaii character design, which elicits caring and playful behaviours in players. Through design, the non-player characters shift between functioning as an object upon which players can act, to a simulated subject that encourages social interactions from the player and stimulates the imagination. Through this flexibility of appearing both ‘real’ and ‘unreal’, players could interact with characters according to their needs during social distancing.

Exploring the effect of the Group Size and Feedback of non-player character spectators in virtual reality exergames.

Despite the widespread interest in leveraging non-player characters (NPCs) to enhance gameplay experiences, there is a gap in understanding of how NPC spectators (i.e., those virtual characters in the scene that watch users' actions) affect players. For instance, the impact of NPC spectators' presence and feedback on players' performance and experience has not been studied, especially in virtual reality (VR) exergames. This paper aims to fill this gap and reports two user studies that assess their effect on such games. Study 1 explored the impact of having NPC spectators present and their feedback available in a gesture-based VR exergame and found having NPC spectators and their feedback could improve players' game performance, experience, and exertion. Based on Study 1's results, we further explored two characteristics of the spectators-their group size (small/large) and their feedback (with/without). The results show that (1) a large spectator number is more helpful since it improves the overall game experience (higher competence, flow, immersion), increases AvgHR% (the average heart rate percentage divided by the maximum heart rate), and enhances performance (improved players' combo performance and increased gesture success rate for particular gesture); (2) spectator feedback is instrumental in improving players' performance (higher gesture success rates, more combos performed successfully, more monster's combos prevented), enhancing game experience (positive affect, competence, flow, and immersion), and reducing negative game experience, increasing exertion (AvgHR% and burned more calories). Based on the results, we derived two main design recommendations for VR exergames that could pave the way for improving gameplay performance and game experience, especially among young adults.

Between Game Facilitation and Performance: Interactive Actors and Non-Player Characters in Larps

The challenge of combining narrative and gameplay in live action role-playing games (larps) has been successfully negotiated with the use of runtime game mastering and interactive actors (ractors) performing non-player characters (NPC). Based on expert interviews six functions for the interactive actors (facilitating, content creation, character portrayal, entertaining, playing, safeguarding) are identified and explored. The paper also reviews existing literature on NPCs in larps, and goes on to offer design insights for runtime game mastering. In addition, certain practical aspects of separating non-player characters from the actors who perform them in pervasive games are considered

APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR CREATING NON-PLAYABLE CHARACTERS IN THE GAME

Artificial intelligence (AI) has revolutionized various industries, including the gaming industry. With the advent of AI, game developers can now create more sophisticated and engaging non-playable characters (NPCs) that can interact with players and adapt to their behavior. NPCs are an essential component of modern games as they provide a more immersive gaming experience by adding depth and complexity to the game world. AI techniques have enabled developers to create NPCs that exhibit more human-like behavior, making them more realistic and engaging for players. In this paper, we explore the application of AI techniques for creating NPCs in games. We discuss various AI approaches, including machine learning, for enhancing the capabilities of NPCs. Furthermore, we analyze the impact of AI on the gaming industry and discuss the potential future developments in this field. Overall, this paper aims to provide insights into the exciting and evolving world of AI in gaming and how it is transforming the way NPCs are created and utilized in games. Keywords: artificial intelligence, games, non-playable characters

Curriculum based Reinforcement Learning for traffic simulations

Non-Playable Characters (NPCs) are essential for creating a fully immersive video game experience. However, their behaviours are typically programmed using rigid methods such as Finite State Machines and Behaviour Trees. These methods are hard to maintain, struggle with implementing intricate cooperative behaviours, and patterns in NPC behaviour can easily be exploited by human players. Machine Learning and Reinforcement Learning (RL) methods in particular, offer a solution to these challenges by allowing dynamic and real-time NPC responses to human player actions and other characters in the game. This work describes and extends upon a Curriculum-based RL approach to train a single model for multiple agents representing vehicles to respect traffic regulations. These vehicles learn to navigate a road network which includes a traffic lights crossroad intersection and a roundabout junction. A comparison of our simulated data against datasets obtained from real world data is conducted; results demonstrate that the learned models have similar trends to the real-world data.

Non-player character decision-making in computer games

Non-player character decision-making in computer games

Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education

Metaverse has gained increasing interest in education, with much of literature focusing on its great potential to enhance both individual and social aspects of learning. However, little work has been done to address the systems and technologies behind providing meaningful Metaverse learning. This article proposes a technical framework to address this research gap, where a hierarchical multiagent reinforcement learning approach with experience sharing is developed to augment the intelligence of nonplayer characters in Metaverse learning for personalization. The utility and benefits of the proposed framework and methodologies are demonstrated in Gridlock, a Metaverse learning game, as well as through extensive simulations.

Interacting with real-person non-player characters to learn history: development and playing behavior pattern analysis of a remote scaffolding-based situated educational game

ABSTRACT This study developed an online role-playing history education game with real-person NPCs (Non-Player Characters). The game features real people playing NPC characters, and provides a multi-dimensional scaffolding mechanism with realistic story and historical scenes, allowing learners to engage in strategic planning and problem solving during a historical event to facilitate their history empathy and further promote their understanding of historical figures and feelings about historical events. A total of 60 participants were recruited online to participate in this study, with 30 in the experimental group (learning by game) and 30 in the control group (learning by general case discussion). This study explored learning effectiveness, flow status, activity anxiety, history empathy, usefulness of scaffolding, game fidelity, and gaming behavioral patterns. The results showed that the experimental group of learners’ history knowledge improved significantly under the history educational game activities. The game also promoted learners’ history empathy. Both groups had high flow. In addition, multi-dimensional scaffolding was noticed in the game at a high rate, and there was a high degree of usefulness. This study also coded videos of learners’ activity processes and further analyzed the behavioral patterns of learners’ collaborative learning.

An Evidence-Based Serious Game App for Public Education on Antibiotic Use and Antimicrobial Resistance: Protocol of a Randomized Controlled Trial.

The misuse and overuse of antibiotics contribute to the acceleration of antimicrobial resistance (AMR), but public knowledge on appropriate antibiotic use and AMR remained low despite ongoing health promotion efforts. App gamification has gained traction in recent years for health promotion and to affect change in health behaviors. Hence, we developed an evidence-based serious game app "SteWARdS Antibiotic Defence" to educate the public on appropriate antibiotic use and AMR and address knowledge gaps. We aim to evaluate the effectiveness of the "SteWARdS Antibiotic Defence" app in improving the knowledge of, attitude toward, and perception (KAP) of appropriate antibiotic use and AMR among the public. The primary objective is to assess the changes in KAP of antibiotic use and AMR in our participants, while the secondary objectives are to assess the extent of user engagement with the app and the level of user satisfaction in using the app. Our study is a parallel 2-armed randomized controlled trial with a 1:1 allocation. We plan to recruit 400 participants (patients or their caregivers) aged 18-65 years from government-funded primary care clinics in Singapore. Participants are randomized in blocks of 4 and into the intervention or control group. Participants in the intervention group are required to download the "SteWARdS Antibiotic Defence" app on their smartphones and complete a game quest within 2 weeks. Users will learn about appropriate antibiotic use and effective methods to recover from uncomplicated upper respiratory tract infections by interacting with the nonplayer characters and playing 3 minigames in the app. The control group will not receive any intervention. The primary study outcome is the change in participants' KAP toward antibiotic use and AMR 6-10 weeks post intervention or 6-10 weeks from baseline for the control group (web-based survey). We will also assess the knowledge level of participants immediately after the participant completes the game quest (in the app). The secondary study outcomes are the user engagement level (tracked by the app) and satisfaction level of playing the game (via the immediate postgame survey). The satisfaction survey will also collect participants' feedback on the game app. Our proposed study provides a unique opportunity to assess the effectiveness of a serious game app in public health education. We anticipate possible ceiling effects and selection bias in our study and have planned to perform subgroup analyses to adjust for confounding factors. The app intervention will benefit a larger population if it is proven to be effective and acceptable to users. ClinicalTrials.gov NCT05445414; https://clinicaltrials.gov/ct2/show/NCT05445414. DERR1-10.2196/45833.

The relationship between parasocial friendship quality with non-playable video game characters, gaming motivations, and obsessive vs harmonious passion

The study was designed to explore the connection between parasocial friendship with non-playable video game characters to motivation for gaming and the type of passion a player has for gaming (harmonious or obsessive). A structural equation modeling approach showed that friendship quality with a non-playable character is positively related to gaming motives for social, fantasy, escape, and coping. The direct effect of friendship quality was not significant to harmonious passion or obsessive passion suggesting that the motives fully mediated the effects of friendship quality onto that passion type. Socialization motives mediated the association between friendship quality and both harmonious and obsessive passion. Fantasy, and coping motives mediated the association between friendship quality and only harmonious passion, while escape motives mediated between friendship quality and only obsessive passion. An alternative model revealed that friendship quality with a non-playable character does not significantly mediate between fantasy motives and passion outcomes. The present findings are discussed within the context of the broader literature.

A player reputation system based on belief formation among Non-Player character societies in Open-World Role-Playing games

The present work is an attempt to design and implement an artificial game environment that provides information dissemination among Non-Player Characters (NPCs) of Open-World Role-Playing Game communities. Based on the perspective that an NPC is a mobile, communicating, cooperative agent that has autonomy and learning ability, and shows socialness in its virtual environment, it was possible to experiment and observe the impacts of human player-generated events on NPCs’ social learning. By means of introducing different tuneable NPC autonomy and socialness parameter values, it is ensured that information and NPC opinions about the player not only spread over the society, but they also help develop player reputation among NPCs.The defined primitives such as NPCs’ self and group-model views; event valuation and reputation models; established learning, memorization, forgetting mechanisms; the introduced information exchange and update protocol; and reputation metric provided for the construction of a tuneable, scalable virtual environment that can be used to investigate the individual and social behavioural aspects of artificial NPC societies. In this virtual environment, it is shown that player’s reputation converges to fixed values. This is noteworthy since it is an indicator of societal learning and belief formation through NPC communications in the game environment.

Cheer for me: effect of non-player character audience feedback on older adult users of virtual reality exergames.

The presence of an audience and its feedback could affect people's performance and experience during an event, especially related to sports such as tennis or boxing. Similarly, in videogames, players' gameplay could be affected if there is an audience and its feedback in response to players' performance in the environment. The inclusion of an audience with non-player characters (NPC) is common in videogames in general. However, there is a limited exploration of the use of an NPC audience in virtual reality (VR) exergames, especially focusing on elderly players. To fill this gap, this work examines the effect of an NPC audience and its associated feedback (with/without) on elderly users of VR exergames. In a user study, we used 120 NPC in a virtual audience. Results showed that the presence of the NPC audience with responsive feedback led to higher performance (with a higher success rate of performing gesture actions, more successful combinations of actions (or combos for short) performed, and more opponent's combos prevented) and better gameplay experience (with higher levels of competence, autonomy, relatedness, immersion, and intuitive controls) of elderly players. Our results can help frame the design and engineering of VR exergames that are targeted at elderly users to help them have an enhanced gameplay experience and improve their health.

A Case for Personalized Non-Player Character Companion Design

Personalized video games have the potential to provide unique and meaningful experiences for the player. User data taken from biosensors, questionnaires, or in-game performance data can infer a player’s psychological state, to which relevant game features can be adapted to enhance the player experience. This survey discusses the data types, game elements, and methods that have been used thus far to create adaptive experiences in games. The survey specifically focuses on personalized nonplayer character (NPC) companions through adaptation. Studies using performance data, affect and cognition, and self-reports to adapt companions and other NPC types are reviewed for their success in providing an enhanced experience. We then provide a motivation for a personalized companion before detailing a framework for an adaptive system that modifies companion characteristics based on the player’s state. This framework takes a human-centered approach to personalized companion design; it proposes the game elements appropriate for adaptation, the data types that suit the adaptation of the companion type, the techniques that would enable successful adaptation, and methods for companion evaluation. The framework firstly quantifies the companion’s behaviour according to recent companion design architecture. Following this, the companion’s characteristics are updated according to the player state, previous player states, evaluations of changes, and the player model. The last phase highlights how to evaluate the companion during the design stage to ensure that it is reliable in assisting the player. We suggest this as a starting point for game designers when considering how to approach a companion that aims to enhance and sustain player experience.