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

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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.