Using Intelligent Agents to Facilitate Game Based Cultural Familiarization Training

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.

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.

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.

A key aspect in the development of computer games is the behavior of non-player characters. Each type of game poses different challenges for the development of a successful artificial intelligence. In racing games, this translates into the programming of an AI which can adapt to the driving style and to the driving capabilities of the human player so as to improve its gaming experience. In addition, in racing games, the behavior of non-player characters should be plausible, challenging throughout the game, adaptive, and it should also lead to realistic group behaviors.

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.

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.

: The After Action Review (AAR) is an interactive discussion conducted following collective training exercises to help units decide what happened, why it happened, and bow to improve future performance. AAR aids can he employed to refresh memories regarding exercise events, provide new perspectives about exercise events, convince participants of the existence of performance problems, stimulate participation in the AAR process, and document the outcomes of the AAR. The AAR process is intended to apply in live, virtual, constructive, or mixed environments. The Standard Army AAR System - (STAARS) concept includes the use of standardized AAR products/aids that can he used across training environments. This guide clarifies the concept of AAR aid standardization, describes the substantial benefits to be gained by standardization, describes general types of AAR aids, discusses the utility of each type of aid, and presents a technique for defining a standardized set of AAR aids for a specific unit type and echelon.

replicating a human-like behavior for a Non-player character (NPC) was always a challenge in sports simulations. Having a non-repetitive and intelligent behavior can dramatically increase the entertainment value of the game. To achieve this, many developers used some rule based or expert system methods which result in a controlled NPC behavior, but recently some machine learning methods are deployed in various types of games. In this paper we propose a method for an NPC to learn main skills in the tennis game. The method is based on the multi layered perceptron (MLP) network. We created a virtual 3D environment of the tennis game which can be interacted with via virtual reality devices. Then, after collecting required data from this environment, implemented a virtual opponent that learns the fundamental skills required to play a tennis game. Finally we compared results of different learning methods.

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 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.

Developing a virtual 3D environment by using game engine is a strategy to incorporate various multimedia data into one platform. The characteristic of game engine that is preinstalled with interactive and navigation tools allows users to explore and engage with the game objects. However, most CAD and GIS applications are not equipped with 3D tools and navigation systems intended to the user experience. In particular, 3D game engines provide standard 3D navigation tools as well as any programmable view to create engaging navigation thorough the virtual environment. By using a game engine, it is possible to create other interaction such as object manipulation, non playing character (NPC) interaction with player and/or environment. We conducted analysis on previous game engines and experiment on urban design project with Unity3D game engine for visualization and interactivity. At the end, we present the advantages and limitations using game technology as visual representation tool for architecture and urban design studies.

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.

Human papillomavirus (HPV) is a common sexually transmitted virus that infects over 13 million people every year. Over 80% of sexually active adults will acquire HPV at some point in their lives, which is concerning since certain high-risk strains of HPV can cause six types of cancer. Vaccination against HPV is safe and effective, but despite high vaccine efficacy, vaccination rates are low among both service members and civilians. We conducted a retrospective, cross-sectional study to identify the percentage of United States Military Academy (USMA) Cadets who had received at least one HPV vaccine and those who completed the vaccine series. Deidentified vaccination and demographic data were retrieved from the Defense Health Agency Military Health System Data Repository (MDR) for all Cadets who were enrolled at USMA between January 2018 and May 2022 (graduating classes of 2018-2025). To identify the population of Cadets for our study and confirm presence of "any" vaccine in the MDR, Tdap vaccination information was also retrieved. The study population was defined as any Cadet who was enrolled at USMA between January 2018 and May 2022 (graduating classes of 2018-2025) and had record of an HPV and/or Tdap vaccine in the MDR. We compared demographic information between the identified population and known demographics of the USMA population to confirm that the retrieved data were consistent with population demographics. Descriptive statistics were performed to identify demographic differences based on vaccinated and unvaccinated Cadets and to determine the percentage of Cadets who received at least one HPV vaccine. Vaccine series completion was defined as the receipt of three HPV vaccine doses among those who reported receiving at least one dose of HPV vaccine or two doses if vaccinated before the age 15. A total of 9,567 Cadets were assessed for eligibility with 9,433 having Tdap and/or HPV vaccine on record. Of these Cadets, 5,738/9,433 (60.8%) had received at least one HPV vaccine. Of those starting the series, 4,492 completed the two- or three-dose series for a completion rate of 47.6%. We found HPV vaccine completion rate among female Cadets (55.6%) is higher than that of male Cadets (45.2%). More Cadets from minority groups received one vaccine; however, full completion rates were similar for both the groups (48.1%). In contrast, the percentage of Cadets vaccinated against Tdap was 97.2%. Most of those vaccinated received their first vaccine between the ages of 11 and 15 (53.0%) and their final vaccine after they turned 16 (68.9%). Though the vaccination rate among the Corps of Cadets is comparable to the public, it is still less than the 80.0% goal set by the Healthy People 2030 objective. Based on these results, we recommend that the USMA modify its policy on HPV vaccine administration to encourage more vaccine uptake. We also conclude that further studies on the rationale for avoiding the vaccine are necessary to better inform educational campaigns and mitigate stigma.

Although barely explored so far, swarm intelligence can arguably have a profound impact on video games; for instance, as a simple yet effective approach for the realistic intelligent behavior of Non-Player Characters (NPCs). In this context, we describe the application of particle swarm optimization to the behavioral design of NPCs in a first-person shooter video game. The feasibility and performance of our method is analyzed through some computer experiments. They show that the proposed approach performs very well and can be successfully used in a fully automatic (i.e., without any human player) and efficient way.

Creating the behavior for non-player characters (NPCs) in video games is a complex task that requires the collaboration among programmers and game designers. Usually these game designers are responsible of configuring and fine tuning certain parameters of the behavior, while programmers write the actual code of those behaviors. That requires several iterations between them. In this paper, we present a new approach for creating the behavior of NPCs that gives more power to the game designer to create behavior without technical knowledge using program by demonstration but preserving the designer confident of the final behavior.