Commonsense Knowledge: Papers from the AAAI Fall Symposium
Catherine Havasi, Doug Lenat, and Benjamin Van Durme, Cochairs
When we are confronted with unexpected situations, we fall back on increasingly general knowledge, or analogize to (superficially) far-flung knowledge — but, lacking both, when software applications fail, they often do so in brittle ways akin to human idiots savant. The sheer amount of commonsense knowledge one would need to represent makes it challenging to acquire, to represent, to reason efficiently with, and to harness in applications. But ultimately this is the bottleneck to strong AI, and so it has remained one of the central topics of research interest for 50 years, from McCarthy, Hayes, and colleagues grappling with representation and reasoning, to Lenat, Singh, and Schubert conducting large scale engineering projects to construct collections of background knowledge and special-purpose reasoners to support general inference. Recent advances in text mining, crowdsourcing, and professional knowledge engineering efforts have finally led to commonsense knowledge bases (for example, ResearchCyc) of sufficient breadth and depth for practical applications. A growing number of research projects are now seeking to utilize these knowledge collections in a wide variety of applications — including computer vision, speech processing, robotics, dialogue and text understanding — in real-world tasks such as healthcare, finance, and traffic control, where brittleness is unacceptable. At the same time, new application domains are giving fresh insights into desiderata for common sense reasoners and guidance for knowledge collection efforts.
The Commonsense Knowledge Symposium will bring together the diverse elements of this community whose work benefits from or contributes to general inference about the world. The aim is to bring together (1) researchers who focus directly on building systems for acquiring or reasoning with commonsense knowledge, with (2) those who wish to use these resources to help tackle tasks within their industry or within AI itself.