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

这项研究将探索用于实时策略(RTS)游戏的新颖的“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。开发人员需要预见到人工智能在游戏世界中可能遇到的所有可以想象的情况。由此产生的人工智能通常是静态的，导致可预测的行为，降低了玩家的体验。此外，对于普通玩家来说，如果没有在人工智能和脚本方面的显著专业知识，很难创造出人工智能行为。使用半自主化身对多人游戏所需的类似人类的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，允许用户只需演示它们就可以创造智能行为。这项研究将在RTS游戏领域内进行，因为这些领域构成了必须解决的重大挑战，以便将学习技术扩展到真实世界的任务。基于案例的规划者、分层任务网络规划者或行业标准行为树执行引擎需要基本行为或方法库来生成完整的计划，这些计划传统上是手动编码的。该项目将研究如何基于从用户演示中学习战略计划的新方法来自动化生成此类行为库的过程。这些技术将在基于案例的RTS游戏规划系统的背景下进行评估。RTS博弈是复杂的，涉及战略决策、多智能体协调、实时交互和部分可观测的环境。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。这项研究将为学习、基于案例的推理和实时战略领域的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为RTS游戏创建行为集。这一功能有两个主要后果：第一，它允许游戏开发商以更少的努力创建游戏，第二，它将支持一种新的游戏类型，玩家将能够创建自己的AI作为游戏的一部分。此外，由于RTS游戏本质上是特定于领域的模拟，研究将支持为诸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等领域的行为集的创作。该项目的教育影响是双重的。首先，该项目将是朝着轻松编写培训模拟器的方向迈出的重要一步，这些培训模拟器适用于需要具有复杂人工智能行为的环境的教育应用程序。这将使开发具有模拟器或虚拟世界的新教育技术成为可能。其次，该项目将使本科生和研究生参与到工作的各个阶段。

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.