HCC: EAGER: Authoring Game AIs by Demonstration for Real-Time Strategy Games

本研究将探讨新颖的即时战略（RTS）游戏的“演示创作”技术。为复杂的计算机游戏创建丰富的人工智能（AI）行为集需要大量的工程工作。开发人员需要预测AI在游戏世界中可能遇到的所有可想象的情况。由此产生的AI通常是静态的，并导致可预测的行为，从而降低玩家体验。此外，如果没有在AI和脚本方面的专业知识，普通玩家很难创建AI行为。对具有半自主化身的多人游戏所需的类人目标和行为进行建模增加了额外的复杂性。这个潜在的变革性项目将开发新的学习技术，允许用户通过演示来创建智能行为。研究将在RTS游戏领域内进行，因为这些领域提出了必须解决的重大挑战，以便将学习技术扩展到现实世界的任务中。基于案例的规划器，分层任务网络规划器或行业标准的行为树执行引擎需要一个基本行为或方法库，以便生成完整的计划，传统上是手工编码。该项目将研究如何自动化的过程中产生这样的行为库的基础上，从用户演示学习战略计划的新方法。这些技术将在RTS游戏的基于案例的规划系统的背景下进行评估。RTS游戏是复杂的，涉及战略决策，多智能体协调，实时交互和部分可观察的环境。这些属性对现有的AI规划和学习方法构成了重大挑战。这项研究将为实时战略领域的学习、基于案例的推理和人工智能做出基础性的科学贡献，解决目标识别、计划学习和创作支持中的关键问题。这项研究将使游戏设计师和其他非程序员能够在不需要编程知识的情况下创建RTS游戏的行为集。这种能力有两个主要的结果：首先，它允许游戏开发人员以更少的努力创建游戏，其次，它将使一种新的游戏类型，玩家将能够创建自己的AI作为游戏的一部分。此外，由于RTS游戏本质上是特定领域的模拟，因此该研究将支持领域行为集的创作，例如用于培训的模拟环境，实时机器人控制，用于商业决策的组织建模，或用于经济战略或公共政策的复杂市场模拟。该项目的教育影响是双重的。首先，该项目将构成一个重要的进步，为需要复杂AI行为环境的教育应用程序轻松编写训练模拟器。这将使新的教育技术与模拟器或虚拟世界的发展。第二，该项目将涉及本科生和研究生在工作的所有阶段。

This research will explore novel "authoring by demonstration" techniques for real-time strategy (RTS) games. Creating rich artificial intelligence (AI) behavior sets for complex computer games requires significant engineering effort. Developers need to anticipate all imaginable circumstances that the AI may encounter within the game world. The resulting AI is often static and results in predictable behaviors, detracting from the player experience. In addition, it is difficult for average players to create AI behaviors, without significant expertise in both AI and scripting. Modeling human-like goals and behaviors required for multiplayer games with semi-autonomous avatars adds additional complexity. This potentially transformative project will develop novel learning techniques that allow users to create intelligent behaviors simply by demonstrating them. The research will be done within the domain of RTS games, as these domains pose significant challenges that must be tackled in order to scale up the learning techniques to real-world tasks.Case-based planners, hierarchical task network planners, or industry-standard behavior-tree execution engines require a library of base behaviors or methods in order to generate complete plans, which traditionally are coded by hand. The project will investigate ways to automate the process of generating such behavior libraries based on novel methods for learning strategic plans from user demonstrations. The techniques will be evaluated in the context of a case-based planning system for RTS games. RTS games are complex and involve strategic decision-making, multi-agent coordination, real-time interaction, and partially-observable environments. These properties pose significant challenges to existing AI methods for planning and learning. This research will make fundamental scientific contributions to learning, case-based reasoning, and AI for real-time strategic domains, addressing key problems in goal recognition, plan learning, and authoring support. This research will enable game designers and other non-programmers to create the behavior sets for RTS games without requiring programming knowledge. This capability has two main consequences: first, it allows game developers to create games with less effort, and second it will enable a new genre of games where players would be able to create their own AIs as part of the game play. Additionally, as RTS games are essentially domain-specific simulations, the research will support authoring of behavior sets for domains such as simulation environments for training, real-time robotic control, organizational modeling for business decision-making, or sophisticated market simulations for economics strategy or public policy. The educational impact of the project is twofold. First, the project will constitute an important advance towards easy authoring of training simulators for educational applications that require environment with complex AI behaviors. This will enable development of new educational technologies with simulators or virtual worlds. Second, the project will involve undergraduate and graduate students in all phases of the work.