Learner

摘要

One of the long-term goals of Artificial Intelligence is construction of a machine that is capable of reasoning about the everyday world the way humans are. In this paper, I first argue that construction of a large collection of statements about everyday world (a repository of commonsense knowledge) is a valuable step towards this long-term goal. Then, I point out that volunteer contributors over the Internet --- a frequently overlooked source of knowledge --- can be tapped to construct such a knowledge repository. To operationalize construction of a large commonsense knowledge repository by volunteer contributors, I then introduce cumulative analogy, a class of analogy-based reasoning algorithms that leverage existing knowledge to pose knowledge acquisition questions to the volunteer contributors. The algorithms have been implemented and deployed as the Learner system. To date, about 3,400 volunteer contributors have interacted with the system over the course of 11 months, increasing a starting collection of 47,147 statements by 362% to a total of 217,971. The deployed system and the growing collection of knowledge it acquired are publicly available from http://teach-computers.org. The knowledge is beginning to be used in follow-on research to address some well-recognized and novel reasoning tasks.