--In the realm of competitive gaming, 3D first-person shooter (FPS) games have gained immense popularity, prompting the development of game AI systems to enhance gameplay. However, deploying game AI in practical scenarios still poses challenges, particularly in large-scale and complex FPS games. In this paper, we focus on the practical deployment of game AI in the online multiplayer competitive 3D FPS game called Arena Breakout, developed by T encent Games. We propose a novel gaming AI system named Private Military Company Agent (PMCA), which is interactable within a large game map and engages in combat with players while utilizing tactical advantages provided by the surrounding terrain. T o address the challenges of navigation and combat in modern 3D FPS games, we introduce a method that combines navigation mesh (Navmesh) and shooting-rule with deep reinforcement learning (NSRL). The integration of Navmesh enhances the agent's global navigation capabilities while shooting behavior is controlled using rule-based methods to ensure controllability. NSRL employs a DRL model to predict when to enable the navigation mesh, resulting in a diverse range of behaviors for the game AI. Customized rewards for human-like behaviors are also employed to align PMCA's behavior with that of human players. I NTRODUCTION First-person shooter (FPS) games in 3D have gained immense popularity in the competitive gaming realm. As these games have evolved from early titles like Maze War and Half-Life to more recent ones such as Apex Legends, CS: GO, and V alorant, there has been a growing interest in developing intelligent AI systems for FPS games.

This short paper presents an efficient path following solution for ground vehicles tailored to game AI. Our focus is on adapting established techniques to design simple solutions with parameters that are easily tunable for an efficient benchmark path follower. Our solution pays particular attention to computing a target speed which uses quadratic Bezier curves to estimate the path curvature. The performance of the proposed path follower is evaluated through a variety of test scenarios in a first-person shooter game, demonstrating its effectiveness and robustness in handling different types of paths and vehicles. We achieved a 70% decrease in the total number of stuck events compared to an existing path following solution.

We approximated the evaluation function for the game Tic-Tac-Toe by singular value decomposition (SVD) and investigated the effect of approximation accuracy on winning rate. We first prepared the perfect evaluation function of Tic-Tac-Toe and performed low-rank approximation by considering the evaluation function as a ninth-order tensor. We found that we can reduce the amount of information of the evaluation function by 70% without significantly degrading the performance. Approximation accuracy and winning rate were strongly correlated but not perfectly proportional. We also investigated how the decomposition method of the evaluation function affects the performance. We considered two decomposition methods: simple SVD regarding the evaluation function as a matrix and the Tucker decomposition by higher-order SVD (HOSVD). At the same compression ratio, the strategy with the approximated evaluation function obtained by HOSVD exhibited a significantly higher winning rate than that obtained by SVD. These results suggest that SVD can effectively compress board game strategies and an optimal compression method that depends on the game exists.

Machine learning systems have been mopping the floor with their human opponents for well over a decade now (seriously, that first Watson Jeopardy win was all the way back in 2011), though the types of games they excel at are rather limited. Typically competitive board or video games using a limited play field, sequential moves and at least one clearly-defined opponent, any game that requires the crunching of numbers is to their advantage. Diplomacy, however, requires very little computation, instead demanding players negotiate directly with their opponents and make respective plays simultaneously -- things modern ML systems are generally not built to do. But that hasn't stopped Meta researchers from designing an AI agent that can negotiate global policy positions as well as any UN ambassador. Diplomacy was first released in 1959 and works like a more refined version of RISK where between two and seven players assume the roles of a European power and attempt to win the game by conquering their opponents' territories.

The last of us is a third-person shooter (TPS) action-adventure made by Naughty Dog and distributed by Sony Computer Entertainment developed majorly for PlayStation 3 and later on remastered for PlayStation 4 in 2014 [1]. Since it's release the game has received amazing reviews by game developer critics [2] [3] [4] as well as by the gaming community and is considered as the best game of the decade as per Metacritic [5]. The game is set in post-apocalyptic America after the parasitic Cordyceps fungus [6] has wiped out majority of the humanity as we know it and divided the entire world into the infected and the survivors. In the nature this species of fungus [7] can be seen attacking on the ants and taking control of their brains [8] forcing the ants to lose control and become a useless creature with only one job left - become host for the fungus, generating a massive sprout which eventually shoots out of their head and infect others eventually. Inspired by this natural phenomena, the creators of The last of Us thought of a scenario where a similar fungus affected the human body.

Artificial intelligence is evolving the landscape of every industry, and the gaming industry is no exception. Through innovation and growth, technology is exceeding our expectations every day. In gaming, artificial intelligence (AI) refers to responsive and flexible video game experiences. While artificial intelligence has long been present in video games, it is today seen as a burgeoning new frontier in how games are both created and played. AI games are progressively handing over control of the game experience to the player, whose actions influence the game experience.

Do your non-player characters lack drive and ambition? Are they slow, stupid and constantly banging their heads against the wall? Then this course is for you. Join Penny as she explains, demonstrates and assists you in creating your very own NPCs in Unity with C#. All you need is a sound knowledge of Unity, C# and the ability to add two numbers together. In this course, Penny reveals the most popular AI techniques used for creating believable character behaviour in games using her internationally acclaimed teaching style and knowledge from over 25 years working with games, graphics and having written two award winning books on games AI.

Video game worlds are getting increasingly large and complex. This poses challenges to the game AI for both pathfinding and strategic decisions, not least in real-time strategy games. One way to alleviate the problem is to manually pre-label the game maps with information about regions and critical choke points, which the game AI can then take advantage of. We present a method for automatically decomposing game maps into non-uniform sized regions. The method uses a flooding algorithm at its core and has the benefit, in addition to its effectiveness, to be relatively intuitive both conceptually and in implementing. Empirical evaluation on game maps shows that the automatic decomposition results in intuitive regions of a comparable standard to human-made labeling. Furthermore, we show that our automatic decomposition, when used by a pathfinding algorithm capable of taking advantage of pre-labeled regions, significantly improves search effectiveness.

Do your non-player characters lack drive and ambition? Are they slow, stupid and constantly banging their heads against the wall? Then this course is for you. Join Penny as she explains, demonstrates and assists you in creating your very own NPCs in Unity with C#. All you need is a sound knowledge of Unity, C# and the ability to add two numbers together. In this course, Penny reveals the most popular AI techniques used for creating believable character behaviour in games using her internationally acclaimed teaching style and knowledge from over 25 years working with games, graphics and having written two award winning books on games AI.

Do your non-player characters lack drive and ambition? Are they slow, stupid and constantly banging their heads against the wall? Then this course is for you. Join Penny as she explains, demonstrates and assists you in creating your very own NPCs in Unity with C#. All you need is a sound knowledge of Unity, C# and the ability to add two numbers together. In this course, Penny reveals the most popular AI techniques used for creating believable character behaviour in games using her internationally acclaimed teaching style and knowledge from over 25 years working with games, graphics and having written two award winning books on games AI.