Behavior NPC Prediction Using Deep Learning

Non-Playable Characters (NPCs) are a subtype of virtual agents that populate video games by endorsing social roles in the narrative. To infer NPCs’ roles, players evaluate NPCs’ appearance and behaviors, usually by ascribing human traits to NPCs, such as intelligence, likability and morality. In particular, hostile NPCs in video games are essential to build the games’ inherent challenges. The three experiments reported here investigated the extent to which the perception of hostility in a military shooter game (including both threat of appearance and aggressiveness in behaviors) is influenced by the appearance and the behaviors of NPCs thanks to perceived intelligence, likability and morality-related questionnaires. Our results first show that hostility is efficiently conveyed through NPCs’ behaviors, but not significantly by their appearance. Second, our study allows identifying the main predictors of hostility perception, namely unfriendliness, knowledge and harmfulness.

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 purpose of this thesis is to provide an understanding of players' moral responses toward Non-Player Characters (NPCs) in video gameplay. The main research question for this thesis is what is the difference of moral response toward different types of NPCs. To answer this, there is a need to find how players feel towards animal, human, and robot NPCs in gameplay. This study proposes that interrogating animal or robot characters makes players feel less morally justified versus human NPCs.--Author's abstract

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.

This perspective article explores the potential of non-player characters (NPCs) in video games as a reflective tool for coaches to enhance participant engagement in sports. While coaches traditionally focus on movement skill instruction, their role extends to fostering young people's immersion in sports contexts and potentially contribute to the possibility of lifelong participation. However, challenges persist in translating coaching theory in coach education programs into practice, including the awareness of roles and how to make young people immersion in sports. Integrating elements from video games, where NPCs play pivotal roles in shaping player experiences, presents a possible avenue for re-thinking the role of coach, especially in participation. By drawing parallels between NPCs and coaches, this article advocates for a new reflection tool for coaching roles. Future research should investigate the effectiveness of leveraging NPCs to enhance athlete engagement and motivation, ultimately creating dynamic and inclusive coaching environments that cater to the evolving needs of participants.

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.

Current computer games are being set in increasingly more complex and dynamic virtual environments. Massively multiplayer online games, for example, are played in persistent virtual worlds, which evolve and change as players create and personalize their own virtual property. In contrast, technologies for controlling the behavior of nonplayer characters that populate virtual game worlds are frequently limited to preprogrammed rules. Characters using fixed rule-sets lack the ability to adapt in time with their environment. Motivated reinforcement learning offers an alternative to character design that can achieve nonplayer characters that both evolve and adapt in dynamic environments. This article presents and evaluates two computational models of motivation for use in nonplayer characters in persistent computer game worlds. These models represent motivation as an ongoing search for novelty, interest, and competence. Two metrics are introduced to evaluate the adaptability of characters controlled by motivated reinforcement learning agents using different models of motivation. These metrics characterize the behavior of nonplayer characters in terms of the variety and complexity of learned behaviors. An empirical evaluation of characters in simulated game scenarios shows that characters motivated by the search for competence are more adaptable in dynamic environments than those motivated by interest and novelty alone.

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.

Many computer games use custom scripts to control the ambient behaviors of non-player characters (NPCs). Therefore, a story writer must write fragments of computer code for the hundreds or thousands of NPCs in the game world. The challenge is to create entertaining and non-repetitive behaviors for the NPCs without investing substantial programming effort to write custom non-trivial scripts for each NPC. Current computer games have simplistic ambient behaviors for NPCs; it is rare for NPCs to interact with each other. In this paper, we describe how generative behavior patterns can be used to quickly and reliably generate ambient behavior scripts that are believable, entertaining and non-repetitive, even for the more difficult case of interacting NPCs. We demonstrate this approach using BioWare's Neverwinter Nights game.

CHI Systems, under contract to the U. S. Army Research Institute, developed an immersive training system, called Virtual Environment Composable Training for Operational Readiness Training Delivery (VECTOR-TD), which provides scenario-based virtual environments for cultural familiarization. VECTOR-TD was designed to provide a new technology for game-based training in cultural familiarization through the application of scenario-based training. VECTOR-TD integrates the Lithtech Jupiter game engine for the virtual environment and CHI System’s iGEN® cognitive agent development toolkit to implement intelligent game characters and integrated performance monitoring. In order for VECTOR-TD to have true long term value to the Army, it was determined that it would be necessary to develop content management tools which would allow Army training personnel to edit and author VECTOR-TD scenarios. To address this, VECTOR-Scenario Editor (VECTOR-SE) was developed based on VECTOR-TD XML-based scripting language to externalize the representation of the training scenario, the behavior of non-player characters (NPCs) within the scenario, and the behavior of the instructor NPC responsible for performance evaluation and After Action Review (AAR). The XML-based language provides direct control of the behavior, dialog, emotional state, and predispositions of NPCs within the scenario. VECTOR-SE was successfully developed to provide content development and management tools that allow non-programmers to author scenarios and manipulate training parameters. VECTOR-SE is based on an instructional design process model and involves the specification of learning objectives, the creation of scenario segments (i.e., vignettes), the authoring of character/trainee dialog, the designation of dialog branching, the placement of characters at game locations, and the designation of when training assistance/remediation will be delivered. The significance of VECTOR-SE is twofold. First, it drastically reduces the time and skill required to develop VECTOR scenarios. Within the original VECTOR project, a 15-minute cultural training scenario was developed which required 2 months of programming and cognitive modeling effort. The same scenario was later implemented in VECTOR-SE in under 3 days. Additional scenarios with comparable complexity have also been implemented using VECTOR-SE and similar significant reductions in development time have been observed. Second, VECTOR-SE makes scenario development or modification accessible to a wider audience of professionals. VECTOR-TD and SE are currently being evaluated at the U.S. Military Academy at West Point.

Abstract: Applications of fuzzy logic in game creation have proven useful, especially when it comes to modelling the behavior of non-player characters (NPCs) in video games. NPC behavior in video games has historically been governed by strict rule-based systems or scripted sequences, which frequently led to restricted and predictable interactions. On the other hand, fuzzy logic adds a degree of flexibility and ambiguity to the decision-making processes of NPCs. By improving the naturalness, adaptability, and emotional resonance of NPC behavior, fuzzy logic may greatly enhance player experiences in games. It provides players with progressively more engaging and dynamic gaming experiences as it continues to be incorporated into game production, opening up new possibilities for interactive narrative, gaming, and immersive virtual worlds. Based on Utku Kose's article, "Developing a Fuzzy Logic Based Game System," I have investigated the use of fuzzy logic in gaming contexts for NPC decision-making in this study.

Non-Player Characters (NPCs) are critical to the interactive and dynamic character of modern video games. They inhabit game worlds, engage with players, and play a large role in storytelling and gameplay mechanics. Yet, conventional rule-based NPCs tend to fall short of realism, flexibility, and scalability needed to keep pace with changing player expectations and sophisticated game worlds. This paper discusses the intersection of Artificial Intelligence (AI) engineering techniques with game development to create intelligent, adaptive NPCs. Through modular AI architectures, reinforcement learning, behaviour cloning, and effective deployment strategies, this research presents a complete framework for developing intelligent NPCs that provide increased interactivity and engagement. It also discusses the application of memory repositories to preserve NPC contextual awareness across extended interactions. Comparative analysis and case studies are employed to illustrate enhancements in gameplay dynamics and player experience.

Creating more believable Non-Player Characters (NPCs) is a significant challenge for video game researchers and industry designers alike. While researchers explore a myriad of solutions, one somewhat forgotten solution area is NPC reputation systems. In this paper, we describe a redefined reputation system for NPC characters that allows for more realistic and dynamic social relationships. Our reputation system focuses on an agent's ability to remember and share observed behavior of other actors in the world. With this knowledge, NPCs can predict behavior of other actors, react according to their own subjective opinion, and exhibit more believable behavior to further immerse the player in the game world.

An open world turn based monster battle game was developed in Java using the popular LibGDX game framework applying multiple machine learning algorithms for its mechanics consisting of an ID3 decision tree, perceptron, naive Bayes classifier and A∗ pathfinding in an attempt to imitate ‘machine intelligence’. A tiled map was used as the game area containing multiple AI agents with different personalities that change depending on the difficulty level chosen. The aim of the game focuses on the player defeating each ‘intelligent machine’ non-player character's (NPC) upon interaction with each other, when player and enemy NPC sprites meet a battle screen appears to allow the player and enemy to engage in a turn-based battle with their monsters. When a battle is lost the player loses a life, otherwise they can approach and engage other enemy agents to battle on the map, and thus the game is called ‘Battle Monsters’.

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.