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Google Tech Talks April, 3 2008 ABSTRACT Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial games. Although games are typically associated with entertainment applications, there are many “serious” applications of gaming, including military, corporate, and advertising applications. There are also what the so called “humane” gaming applications—interactive tools for medical training, educational games, and games that reflect social consciousness or advocate for a cause. Game AI is the effort of taking computer games beyond scripted interactions, however complex, into the arena of truly interactive systems that are responsive, adaptive, and intelligent. Such systems learn about the player(s) during game play, adapt their own behaviors beyond the pre-programmed set provided by the game author, and interactively develop and provide a richer experience to the player(s). In this brown bag, I will discuss a range of CBR approaches for Game AI. I will discuss differences and similarities between character-level AI (in embedded NPCs, for example) and game-level AI (in the drama manager or game director, for example). I will explain why the AI must reason at multiple levels, including reactive, tactical, strategic, rhetorical, and meta, and propose a CBR architecture that lets us design and coordinate real-time AIs operating asynchronously at all these levels. I …

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Google Tech Talks November 11, 2008 ABSTRACT Berners-Lee’s vision of the Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping method: creating enough structured data to motivate the development of applications. We believe that autonomously `Semantifying Wikipedia’ is the best way to bootstrap. We choose Wikipedia as an initial data source, because it is comprehensive, high-quality, modestly sized, and contains enough manually-derived structure to bootstrap an autonomous, self-supervised process. In this talk I will present our success to date in this endeavor: A novel approach for self-supervised learning of CRF information extractors Automatic construction of a comprehensive ontology via statistical-relational learning Vast improvements in extraction recall through shrinkage over this ontology and retraining The stimulation of a virtuous feedback cycle between communal content creation and information extraction We aim to construct a knowledge base of outstanding size to support inference, automatic question answering, faceted browsing, and potentially to bootstrap the Semantic Web. Speaker: Daniel S. Weld Daniel S. Weld is Thomas J. Cable / WRF Professor of Computer Science and Engineering at the University of Washington. After formative education at Phillips Academy, he received bachelor’s degrees in both Computer Science and Biochemistry at Yale University in 1982. He landed a Ph.D. from the MIT Artificial Intelligence Lab …

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