Abstract

Intelligent behavior requires, in one form or another, access to an enormous reservoir of knowledge and information. Unfortunately, intelligent systems are currently limited by a severe knowledge acquisition bottleneck. In most cases, the cost of getting knowledge is simply too high. Hand-coding is often too arduous or too time-consuming, while learning algorithms are often too slow or handcuffed by insufficient training data.

This paper explores the idea of harnessing computer networks to overcome the knowledge acquisition bottleneck. We introduce the idea of a CYLINA (CYberspace Leveraged INtelligent Agent)—an intelligent system that gains knowledge/information through interactions with a large population of network users. Instead of depending on the big efforts of a few knowledge engineers, CYLINAs rely on small, incremental contributions from a large population of experts. Our thesis is that the shear volume of interaction will allow significant knowledge to be acquired in a short amount of time.

We consider potential applications for CYLINAs, then focus on Auto-FAQ, an experimental system currently under development at GTE Laboratories. Auto-FAQ is a question-answering system. Its intent is to make information typically found in USENET News FAQs much more accessible (It has many other uses as well.) Users ask questions in natural language forms. These questions index directly into the systems infobase. Infobase entries are question-answer pairs. Answers can be raw text, URLs, or Links into existing entries in the system's infobase. By using the system recursively, users can explore entire subjects with a series of questions. Facilities exist to tag gaps in the systems knowledge base. When a gap is found, it is posted to a public list. Individuals in the cyberspace community can search the list, volunteer expertise, and fill in gaps as appropriate.

A version of Auto-FAQ is currently operating on a private network at GTE Laboratories. The system is currently able to answer basic questions about itself, WWW, and Mosaic. Future plans are to make Auto-FAQ and its associated software available on the global Internet.

Author Keywords: Author Keywords: Question answering; Intelligent agents; Cyberspace leveraging; Machine learning; Information retrieval; World-Wide Web; Frequently asked questions; FAQ