Overview
There is a long and fascinating history of humankind's endeavor to explain and, with the advent of AI, ultimately mechanize the overarching processes that lead to scientific discoveries. This quest dates back to Aristotle's account of human de ductive reasoning (the theory of the syllogism, developed to model the discoveries of Euclid), and continues through modern AI, which, through impressive systems like LT, Bacon, GT, Eurisko, and Graffiti (and many theorem provers, model finders, and co mputational frameworks for machine-assisted reasoning), has placed some degree of such automation within reach. Over the past 60 years, starting with AI's inaugural conference, systems such as these have automated aspects of scientific discovery. Machines have generated novel and interesting conjectures (some which have spawned new scientific research areas), and increasingly efficient techniques have been invented to prove or refute them.
Nevertheless, the sobering fact remains that such advances fall far short of approaching the creativity and innovation of even amateur scientists. We believe that AI is ripe for revolutionary progress in automated and semi-automated scientific discovery, in no small part because the field now has on hand systems that mark advances in various parts of discovery-parts that, when interconnected, may make for some very exciting new systems. We also believe that dialogue between researche rs behind these systems could lead to a new generation of powerful AI discovery systems.
This symposium will survey the state of the art in systems that cover some aspects of the entire process of scientific discovery (including, e.g, representation, exploration, conjecture generation, validation, and publishing/reporting). Of pa rticular interest is how the current technologies can fit together to form an environment by which the human reasoner's vision and reach can be augmented, and what goals should be set in order to move closer to the complete mechanization of general sci entific discovery--or at least closer to a time when machines can truly operate as intelligent assistants in the search for new discoveries.