Modeling motivation for adaptive nonplayer characters in dynamic computer game worlds

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.

In modern computer games, a NPC (non-player character) can make choices. In the previous researches, a NPC can have the certain knowledge in a game world; this will reduce the believability of perception for a NPC. A decision model based on Bayesian network and fuzzy logic for a NPC is set up in this paper; a NPC can partially know domain knowledge. A behavior animation of a virtual dog by the model is realized on PC, the results show that the virtual dog can behave believable in watching a house.

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.

For the last few decades, there was tremendous interest in harnessing 3D virtual environments for education and training. Multi-user game-like environments make use of non-player characters and quest activities in tutoring and training. This approach incorporates successful active learning and incremental progress. This article presents issues encountered while adapting the multi-user online game genre for educational virtual environments. In this endeavour, non-player characters play a central role in organising and delivering educational content. Educational virtual environments call for additional kinds of extension beyond generic user content creation as delivered in some virtual worlds like Second Life. Support for these environments will emphasise the creation of new activities. A set of libraries and techniques named portable non-player character tutors and quests reduces the effort needed to develop and integrate educational non-player characters. These non-player characters are introduced in CVE, a platform for rapidly developing educational virtual environments. The portable non-player character tutors and quests framework enables formatted web-based exercises, quizzes, and educational content to be incorporated into virtual worlds, reducing the effort needed to create new content. The framework presented in this article introduces initial non-player characters that support educational quest activities to the 3D environments users.

Rapid and accurate evaluation of gamma radiation dose in dynamic virtual environments is crucial for radiation protection and safety assessment. The point kernel method has been widely used for rapid dose calculation due to its high efficiency. However, the 'point kernel calculations (PKCs) count' is defined as the product of the point kernels number and the counting grids number. Consequently, when either the number of point kernels generated from the discretised radiation source or the number of counting grids in the space becomes large, the time consumption increases significantly. This issue is further exacerbated when the geometric model is no longer a regular geometry describable by mathematical formulas but rather an arbitrary 3D model in a virtual environment, where the cost of ray tracing grows substantially. To address this challenge, an efficient computational approach is proposed and a high-performance point kernel code, PLSPK, is developed in this work. The main features of PLSPK include using triangular mesh models as geometric representations to handle arbitrary shapes, and optimising the ray-tracing process through the application of the maximised-parallelism bounding volume hierarchy technology, enabling rapid selection of target triangles for ray tracing in complex scenes. Furthermore, by leveraging graphics processing unit parallel computation, the code significantly accelerates large-scale PKC in gamma radiation fields, allowing PLSPK to meet the demand for continuous rapid updates of radiation field in dynamic environments. Compared with serial implementations, the acceleration achieved by PLSPK becomes more pronounced as the PKC count (PKC count) increases, when the PKC count reaches 1E + 06, the speedup is approximately 2000×, with small impact from scene complexity. Accuracy validation against existing point kernel codes and the Monte Carlo particle transport program MCNP demonstrates that PLSPK provides reliable dose estimations. These features make PLSPK a reliable tool for high-efficiency gamma radiation field calculation in complex dynamic virtual environments.

Non-Player Characters (NPCs) are a vital part of computer games. Their behaviour is usually pre determined by the developers before a computer game is released. While these NPCs exist in a virtual environment in which they aim to achieve their objectives, these objectives might involve certain actions which should be both believable and add realism to games. In internet gameplay, these may range from navigation of the environment, attack and defense of an objective or assisting other NPCs or human players to achieve their goals. Various forms of decision making process are now implemented to provide believability and realism to games. These techniques range from reinforcement learning, imitation learning, supervised and unsupervised learning for online and offline games. While these are widely implemented, a new genre of realism is being added to games especially first person shooter games, namely Smart Terrains with virtual destructive environments. Smart Terrain has the potential to improve the realism of games and the behavior of the NPC in games such as first person shooter games where gameplay is fast paced. The potential issue would be if NPCs are dynamic enough to recognize changes in the virtual environment or if the virtual environment provides the required information to assist the NPCs with their objectives or tasks. This paper presents an overview of Smart Terrain in enhancing Online Tactic Agent decision making.

Summary form only given. Extending the frontier of visual computing, an interactive, multimodal VR environment utilizes audio and touch-enabled interfaces to communicate information to a user and augment the graphical rendering. By harnessing other sensory channels, an immersive multimodal display can further enhance a user's experience in a virtual world. In addition to immersive environments, multimodal display can provide a natural and intuitive human-computer interface for many desktop applications such as computer games, online virtual worlds, visualization, simulation, and training. Compared to visual and haptic rendering, sound rendering has extremely demanding computing requirements, making the problem of auditory display highly challenging. In this talk, I will give an overview of our recent work on interactive auditory display consisting of sound synthesis and sound propagation. These include generating realistic physically-based sounds from perceptually-guided principles and dynamic simulation. I will also describe novel algorithms for immersive sound effects based on improved numerical techniques and fast geometric sound propagation. Finally, I present new techniques on cross-modal interaction for VR. These systems improve the state of the art in sound rendering by at least one to two orders of magnitude and will be demonstrated in complex, dynamic virtual environments and VR applications. I conclude by discussing possible future research directions on multimodal interaction with VR systems.

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.

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.

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.

Most of the existing Virtual World (VW)-based curriculum-related educational systems use conventional non-player characters (NPCs) to interact with users, represented as avatars, to guide and help them to accomplish learning activities. Also, a few of them use some kind of gamification and keep data for user interactions and activities, and even fewer allow for real-time tutor intervention. In this paper, we present the design, implementation, and evaluation of an educational system based on VW technology, which employs gamification features; two types of NPCs, one conventional and another LLM-based; and a database that stores, apart from educational information, information about the interactions users have with NPCs. Furthermore, we designed and implemented a learning management unit for online-tutor tracing and for supporting the learning progress of users. The evaluation of the system, via experimental use and questionnaires, shows that both types of NPCs were useful for different reasons, although there was a preference for the LLM-based NPC. LLM-based NPCs made dialogues more interesting and were perceived as more friendly and helpful, but conventional ones provided more targeted help. However, both were less interesting than the two gamification features: a scoring system and quizzes. Additionally, the effectiveness of the tutoring system was confirmed in terms of learning outcomes and overall experience, although in a subjective manner. Finally, online-tutor support was recognized as a very positive capability.

Playing video games is one way to break up the monotony of an otherwise boring day. However, a significant number of people quickly tire of playing some of the games that are available to them. This study will result in the development of a strategy that will give a rapid response during combat and may boost NPC abilities. This will be done so that this problem can be circumvented. There is a non-player character that is considered to be one of the most important in the game (NPC). NPCs that are autonomous and adaptive may change their behavior in reaction to the decisions made by the player as well as the conditions in their environment. Previous research has made use of the neural network methodology to forecast the behavior of NPCs; however, this method has a drawback in that the predicted behavior is not necessarily as desired, which leads to a poor level of accuracy. This study attempts to answer the problem of inadequate accuracy by using as its three input parameters the power of the non-player character (NPC), the distance between the player and the NPC, and the power of the opponent. The outcomes of the test indicate that the machine learning technique may be used to identify the results of the NPC behavior analysis as well as the level of accuracy reached.

This paper presents an approach of event-based control for the modeling of autonomous agents in a dynamic virtual environment. It constructs a biologically inspired neural network for real-time planning of collision-free paths. In addition, it uses a state-to-reference transformation to convert the stimuli of autonomous agents from the time domain to an event domain that corresponds to the perceptual inputs of agents. This event-based approach of control allows autonomous agents to move smoothly towards their goals and to make prompt reaction when environment changes take place.