The situation changed in the 1970s when a number of teams developed AI systems that tackled this problem. Some demonstrated the ability to rediscover insights from the history of mathematics (Lenat, 1977) and physics (Langley, 1981), whereas others produced novel results in specific disciplines like organic chemistry (Lindsay et al., 1980). The innovators came from different backgrounds – psychology, computer science, and philosophy – but shared a commitment to understanding discovery in computational terms. Research in this tradition has continued for over forty years, leading to publications in both AI (Dzeroski & Todorovski, 1995; Bradley et al., 2001; Bridewell et al., 2008) and scientific literatures (Valdes-Perez, 1994; Langley, 2000). Multiple books have addressed the topic (Langley et al., 1987; Glymour et al., 1987; Shrager & Langley, 1990; Dzeroski & Todorovski, 2007; Addis et al., 2019) and four symposia (1989, 1995, 2001, and 2013) have convened experts in the area, but the problem did not receive widespread attention.
At least, that was the case until recently, when researchers from different fields, especially physics and applied mathematics, began devoting energy to the topic. Work from this perspective emphasizes continuous mathematics rather than symbolic structures and often relies on recent developments in statistical learning and high-powered computers. Recent examples include Brunton, Proctor, and Kutz (2016), Chen et al. (2019), Cranmer et al. (2020), Fries, He, and Choi (2022), Iten et al. (2020), Raissi and Karniadakis (2018), Wu and Tegmark (2019), and Zhang and Lin (2018), many of them inspired by Schmidt and Lipson's (2009) visible work in the area. These researchers have made parallel progress on problems related to those addressed by the older community, but there has been little contact between the two groups to date.
Despite the differences between these two paradigms, they also share some important assumptions about the scientific enterprise and the nature of discovery that suggest the potential for bridging the current gap between them. These include beliefs that:
We hope that the symposium will help overcome this conceptual divide, increase interaction among the groups, and encourage a more unified research community with a common view of scientific discovery. To achieve these aims, it will include introductory review talks about the problems, methods, and results in each paradigm to familiarize participants with them. We will also organize sessions around types of scientific models (e.g., qualitative structures, causal relations, numeric equations, processes) rather than methodological paradigms. In addition, we will ask speakers to abstract away from their algorithmic and mathematical details and instead to focus on other facets of their work, in particular:
We believe that strategies of this sort will lead to a community of researchers who appreciate different approaches to a shared set of problems. We also hope they will offer novel insights to participants about some key questions, such as:
Authors should submit abstracts of proposed talks through the AAAI
January 15 January 30, 2023, AoE (Anywhere on
Earth). These should be no longer than one page in 11-point,
single-column style using pdf format, in effect providing an
outline of the proposed content. Submissions should include one or two
references that are representative of the authors' work on scientific
discovery, along with links to these papers. The organizing committee
will select abstracts that cover a broad range of problems and approaches.
Please indicate whether you will be able to give your talk in person
at the meeting. A few virtual presentations may be possible, but the
committee will favor in-person talks.
When making a submission, make sure to select the track Computational
Approaches to Scientific Discovery rather than the track SSS-23,
which is a holdover from an earlier version of the EasyChair site.
For submissions that report implemented systems, the committee will give preference to ones that address the first seven questions listed above rather than ones that emphasize algorithmic and mathematical details. We also welcome abstracts on other topics, such as proposals for how to evaluate discovery systems or precise specifications of new discovery problems. Authors of accepted abstracts will be asked to write a full paper for distribution to symposium participants on this Web site. They may also be encouraged to submit an expanded version for possible inclusion in a special issue of Machine Learning or another refereed journal that reports results presented at the event.
Bradley, E., Easley, M., & Stolle, R. (2001). Reasoning about nonlinear system identification. Artificial Intelligence, 133, 139–188.
Bridewell, W., Langley, P., Todorovski, L., & Džeroski, S. (2008). Inductive process modeling. Machine Learning, 71, 1–32.
Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016). Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 113, 3932–3937.
Chen, R. T. Q., Rubanova, Y., Bettencourt, J., & Duvenaud, D. K. (2018). Neural ordinary differential equations. Advances in Neural Information Processing Systems 31. Curran Associates.
Cranmer, M., Sanchez-Gonzalez, A., Battaglia, P., et al. (2020). Discovering symbolic models from deep learning with inductive biases. Neural Information Processing Systems 33. Vancouver, Canada.
Džeroski, S., & Todorovski, L. (1995). Discovering dynamics: From inductive logic programming to machine discovery. Journal of Intelligent Information Systems, 4, 89–108.
Džeroski, S., & Todorovski, L. (Eds.). (2007). Computational discovery of communicable scientific knowledge. Berlin: Springer.
Fries, W. D., He, X., & Choi, Y. (2022). LaSDI: Parametric latent space dynamics identification. Computer Methods in Applied Mechanics and Engineering, 399, 115436.
Glymour, C., Scheines, R., Spirtes, P., & Kelly, K. (1987). Discovering causal structure: Artificial intelligence, philosophy of science, and statistical modeling. San Diego: Academic Press.
Iten, R., Metger, T., Wilming, H., et al. (2020). Discovering physical concepts with neural networks. Physical Review Letters, 124, 010508.
King, R. D., Rowland, J., Oliver, S. G., et al. (2009). The automation of science. Science, 324, 85–89.
King, R. D., Whelan, K. E., Jones, F. M., et al. (2004). Functional genomic hypothesis generation and experimentation by a robot scientist, Nature, 427, 247–252.
Langley, P. (1981). Data-driven discovery of physical laws. Cognitive Science, 5, 31–54.
Langley, P. (2000). The computational support of scientific discovery. International Journal of Human-Computer Studies, 53, 393–410.
Langley, P., Simon, H. A., Bradshaw, G. L., & Zytkow, J. M. (1987). Scientific discovery: Computational explorations of the creative processes. Cambridge, MA: MIT Press.
Lenat, D. B. (1977). The ubiquity of discovery. Artificial Intelligence, 9, 257–285.
Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A., & Lederberg, J. (1980). Applications of artificial intelligence for organic chemistry: The DENDRAL project. New York, NY: McGraw-Hill.
Raissi, M., & Karniadakis, G. E. (2018). Hidden physics models: Machine learning of nonlinear partial differential equations. Journal of Computational Physics, 357, 125–141.
Popper, K. R. (1961). The logic of scientific discovery. New York: Science Editions.
Schmidt, M., & Lipson, H. (2009). Distilling free-form natural laws from experimental data. Science, 324, 81–85.
Shrager, J., & Langley, P. (Eds.) (1990). Computational models of scientific discovery and theory formation. San Francisco: Morgan Kaufmann.
Todorovski L. (2011). Equation discovery. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning. Boston, MA: Springer.
Valdes-Perez, R. E. (1994). Human/computer interactive elucidation of reaction mechanisms: Application to catalyzed hydrogenolysis of ethane. Catalysis Letters, 28, 79–87.
Wu, T., & Tegmark, M. (2019). Toward an artificial intelligence physicist for unsupervised learning. Physical Review E, 100, 033311.
Zhang, S., & Lin, G. (2018). Robust data-driven discovery of governing physical laws with error bars. Proceedings of the Royal Society A, 474, 20180305.