Project Halo: Towards a Digital Aristotle

By Friedland, Noah S.; Allen, Paul G. et al. | AI Magazine, Winter 2004 | Go to article overview

Project Halo: Towards a Digital Aristotle


Friedland, Noah S., Allen, Paul G., Matthews, Gavin, Witbrock, Michael, et al., AI Magazine


Project Halo is a multistaged effort, sponsored by Vulcan Inc, aimed at creating Digital Aristotle, an application that will encompass much of the world's scientific knowledge and be capable of applying sophisticated problem solving to answer novel questions. Vulcan envisions two primary roles for Digital Aristotle: as a tutor to instruct students in the sciences and as an interdisciplinary research assistant to help scientists in their work.

As a first step towards this goal, we have just completed a six-month pilot phase designed to assess the state of the art in applied knowledge representation and reasoning (KR&R). Vulcan selected three teams, each of which was to formally represent 70 pages from the advanced placement (AP) chemistry syllabus and deliver knowledge-based systems capable of answering questions on that syllabus. The evaluation quantified each system's coverage of the syllabus in terms of its ability to answer novel, previously unseen questions and to provide human-readable answer justifications. These justifications will play a critical role in building user trust in the question-answering capabilities of Digital Aristotle.

Prior to the filial evaluation, a "failure taxonomy" was collaboratively developed in an attempt to standardize failure analysis and to facilitate cross-platform comparisons. Despite differences in approach, all three systems did very well on the challenge, achieving performance comparable to the human median. The analysis also provided key insights into how the approaches might be scaled, while at the same time suggesting how the cost of producing such systems might be reduced. This outcome leaves us highly optimistic that the technical challenges facing this effort in the years to come can be identified and overcome.

This article presents the motivation and longterm goals of Project Halo, describes in detail the six-month first phase of the project-the Halo Pilot-its KR&R challenge, empirical evaluation, results, and failure analysis. The pilot's outcome is used to define challenges for the next phase of the project and beyond.

Aristotle (384-322 BCE) was remarkable for the depth and scope of his knowledge, which included mastery of a wide range of topics from medicine and philosophy to physics and biology. Aristotle not only had command over a significant portion of the world's knowledge, but he was also able to explain this knowledge to others, most famously, though briefly, to Alexander the Great.

Today, the knowledge available to humankind is so extensive that it is not possible for a single person to assimilate it all. This is forcing us to become much more specialized, further narrowing our worldview and making interdisciplinary collaboration increasingly difficult. Thus, researchers in one narrow field may be completely unaware of relevant progress being made in other neighboring disciplines. Even within a single discipline, researchers often find themselves drowning in new results. MEDLINE,1 for example, is an archive of 4,600 medical publications in 30 languages, containing over 12 million publications, with 2,000 added daily.

Making the full range of scientific knowledge accessible and intelligible might involve anything from simply retrieving facts to answering a complex set of interdependent questions and providing appropriate justifications for those answers. Retrieval of simple facts might be achieved by information-extraction systems searching and extracting information from a large corpus of text, such as Voorheese (2003). But aside from the simplicity of the types of questions such advanced retrieval systems are designed to answer, they are only capable of retrieving "answers"-and justifications for those answers-that already exist in the corpus. Knowledge-based question-answering systems, by contrast, though generally more computationally intense, are capable of generating answers and appropriate justifications and explanations that are not found in texts. …

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