Improving the Spatial Awareness of Virtual Agents in Games

许多现代游戏融合了人工智能代理，作为玩家的对手、同伴/追随者，甚至只是旁观者。这些非玩家角色(NPC)代理有不同的角色，但所有这些非玩家角色(NPC)代理都被要求以一种对玩家有意义的方式与游戏环境交互。然而，在空间上有意识的NPC行为很少见，大多数NPC执行简单、刻板和大量照本宣科的行为，表明他们对与其预期活动相关的空间属性几乎不了解。游戏中的高级对手可能会“隐蔽”或模仿其他战略行为，但他们这样做是基于嵌入的，手动空间名称和非常简单(且高度本地化)的空间属性。一般来说，角色不能真实地隐藏、偷偷、探索、伏击或以其他方式利用任意空间，或者具有动态变化的属性的空间。NPC往往相互干扰和玩家，并在游戏开发者没有预见到的情况下做出过于简单、明显非理性的选择，通过与玩家对特工应该如何行为的期望相矛盾而引发挫败感。这项研究旨在提高人工智能体在现代计算机游戏中的技术能力和交互质量。它的目标是机械效益，即存在算法潜力，以更好地分析和抽象虚拟空间，以提高环境意识。它以不同的方式总结世界状态的各个方面，以更好、更紧凑地表示与常见运动和战略规划问题相关的空间性质，对于提高NPC决策的质量具有重大潜力，无论是应用于脚本较少的游戏战斗模拟中，这项工作解决了开发适用于不同空间任务的抽象概念方面的挑战，重点关注与可见性和感知的使用相关的核心问题。此外，由于交互环境中典型的实时计算问题，以及快速开发环境(如游戏)所需的实际实现设计和工具的需求，这一工作还伴随着效率要求。游戏在突破人工智能的边界方面有着悠久的历史。现代游戏不断提高这一门槛，以越来越详细的方式呈现代表主体和环境的虚拟环境，因此越来越需要证明理性和有效的行为。这项研究工作有利于加拿大在游戏开发行业的强大存在，并为人工智能在日益增长的、真实世界的虚拟环境集成中的实际使用提供有用的抽象和技术。

Many modern games incorporate artificially intelligent agents, as opponents, companions/followers for the player, or even just as bystanders.  These Non-Player Character (NPC) agents have different roles, but all of them are required to interact with the game environment in a way that makes sense to the player.  Spatially aware NPC behaviours, however, are rare, with most NPCs enacting simple, stereotypical and heavily scripted behaviours that indicate little understanding of spatial properties relevant to their intended activities.  Advanced opponents in games may "take cover" or mimic other strategic behaviours, but they do so based on embedded, manual space designations and very simple (and highly localized) properties of the space. Characters in general are not able to realistically hide, sneak, explore, ambush, or otherwise exploit an arbitrary space, or one with dynamic, changing properties.  NPCs tend to interfere with each other and the player, and make overly simple, apparently irrational choices in contexts not foreseen by the game developer, inducing frustration by contradicting a player's expectation of how an agent should behave. This research aims to improve the technical capability and interactive qualities of artificial agents in modern computer games.  It is aimed at the mechanical benefits, where algorithmic potential exists to better analyze and abstract a virtual space to improve environmental awareness.  "Summarizing" aspects of the world state in different ways to better, and more compactly represent spatial qualities relevant to common movement and strategic planning problems has significant potential for improving the quality of decisions made by an NPC, whether applied to opponents in less-scripted game combat simulation, or to non-combat NPCs that need to align their behaviour with high-level player expectations.  This work addresses challenges in developing abstractions appropriate to different spatial tasks, focussing on core issues related to use of visibility and perception.  This is coupled with efficiency requirements due to real-time computation concerns, typical of interactive environments, as well as the need for practical implementation designs and tooling necessary in rapid development contexts like games. Games have a long history of pushing the boundaries of artificial intelligence.  Modern games continue to raise that bar in presenting virtual contexts that represent agents and environments in increasing detail, and thus with increasing need to demonstrate rational and effective behaviour.  This research work benefits Canada's strong presence in the game development industry, and offers abstractions and techniques useful for the practical use of artificial intelligence in the growing, real-world integration of virtual environments.