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(307) Beyond Behavior: An Introduction to Knowledge Representation
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Description

As a field, we have spent a lot of time worrying about how our characters behave, and have come up with various techniques, including HFSMS, behavior trees, and GOAP planners, for dealing with the scale challenges that complex behavior presents. However, we have spent considerably less time worrying about what our characters KNOW. After all, if our AI is to make decisions about what to do, we must figure out what information the AI bases its decisions ON. The format, organization, and updating of that information is the problem of Knowledge Representation. Moreover, if architected appropriately, it can result in a great reduction in behavioral complexity, thus making behavior authoring easier and your game more fun. Knowledge representation is one of the great unrecognized problems of game AI. In this lecture we will present a number of systems for KR drawn from both academic and industrial sources. We will provide an in-depth look at the architecture of the AI for Bungie's Halo, and the role that KR plays in that architecture. We will look at several academic techniques for KR, including Occupancy Maps and Semantic Nets. Finally, we will look at a tantalizing topic for the future of AI and games: the interaction of KR and natural language. Attendees will come away with an appreciation for the role that KR can play in creating compelling AI and gameplay, and with some concrete places to start their own exploration of the topic.

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