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

本研究将探索实时战略（RTS）游戏的新型“示范创作”技术。为复杂的电脑游戏创建丰富的人工智能（AI）行为集需要大量的工程工作。开发者需要预测AI在游戏世界中可能遇到的所有情境。由此产生的AI通常是静态的，并导致可预测的行为，从而影响玩家体验。此外，如果没有AI和脚本方面的专业知识，普通玩家很难创造AI行为。用半自主角色来模拟多人游戏所需的类人目标和行为增加了额外的复杂性。这个潜在的变革性项目将开发新颖的学习技术，允许用户通过简单的演示来创建智能行为。研究将在RTS游戏领域进行，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。基于案例的计划者、分层任务网络计划者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来生成完整的计划，而这些计划通常是手工编写的。该项目将研究基于从用户演示中学习战略计划的新方法来自动生成此类行为库的方法。这些技术将在基于案例的RTS游戏计划系统的背景下进行评估。RTS游戏非常复杂，涉及战略决策、多代理协作、实时交互和部分可观察环境。这些特性对现有的人工智能规划和学习方法构成了重大挑战。本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科学贡献，解决目标识别、计划学习和创作支持方面的关键问题。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为RTS游戏创造行为集。这种能力有两个主要结果：首先，它允许游戏开发者以更少的努力创造游戏，其次，它将催生一种新的游戏类型，玩家将能够创造自己的ai作为游戏玩法的一部分。此外，由于RTS游戏本质上是特定领域的模拟，因此该研究将支持为诸如训练模拟环境、实时机器人控制、商业决策组织建模或经济战略或公共政策的复杂市场模拟等领域编写行为集。这个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，为需要复杂人工智能行为环境的教育应用程序轻松编写训练模拟器。这将使利用模拟器或虚拟世界开发新的教育技术成为可能。其次，该项目将涉及本科生和研究生的所有阶段的工作。

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.