Symposium on Computational Discovery of Communicable Knowledge

Stanford University, March 24-25, 2001


Research on computational approaches to scientific discovery has a long history in artificial intelligence and cognitive science. Early efforts focused on reconstructing episodes from the history of science, but the past decade has seen similar techniques produce a variety of new scientific discoveries, many of them leading to publications in the relevant scientific literatures. Work in this paradigm has emphasized formalisms used to communicate among scientists, including numeric equations, structural models, and reaction pathways.

However, in recent years, research on data mining and knowledge discovery has produced another paradigm. Even when applied to scientific domains, this framework employs formalisms developed by AI researchers themselves, such as decision trees, rule sets, and Bayesian networks. Although such methods can produce predictive models that are highly accurate, their outputs are not cast in terms familiar to scientists, and thus typically are not very communicable.

This symposium aims to bring together researchers who are pursuing computational methods for discovery of communicable knowledge and to review recent advances in this area. The primary focus will be on discovery in scientific disciplines, since communication is often central there. The schedule will include time for both formal talks and discussions, including extended breaks for less formal interactions.

Presentations will focus on the discovery task (the problem statement and system input, including data, and any background knowledge) and on the generated knowledge (the system output). Although AI and machine learning have traditions of emphasizing particular algorithms, this meeting will address results at a more abstract level. The aim will be to explore what methods for the computational discovery of communicable knowledge have in common rather than the great diversity of methods used to achieve that end.

In memorium

As an important note, Jan Zytkow passed away on Tuesday, January 16, and Herbert Simon died on Friday, February 9, only a few weeks later. Both were instrumental in launching and nurturing the field of computational scientific discovery, and we will also reserve some time to remember them and their many contributions. Both will be sorely missed, but the best way to honor them is to continue building on the tradition they both helped establish.

Time and location

The symposium will take place on Saturday, March 24, and Sunday, March 25, just before the AAAI Spring Symposia, at Stanford University's Center for the Study and Language and Information (CSLI). Talks will be held in the main conference room of Cordura Hall on the Stanford campus.

Other information

Attendance will be by invitation only, but there is no registration fee. There will be 15 invited speakers presenting at the symposium over two days. We will have space for a similar number of non-presenting attendees at the meeting. If you are interested in participating, please send email to with a brief account of your previous and current work on the symposium topic.



This symposium has received financial support from NASA Ames Research Center, along with administrative support from the Center for the Study of Language and Information and from the Institute for the Study of Learning and Expertise.

Related sites

  • Institute for the Study of Learning and Expertise

  • Computational Learning Laboratory at Stanford University