Joined-Up Reasoning for Automated Scientific Discovery: A Position Statement and Research Agenda

We use the phrase “joined-up” here with a double meaning: to convey two aspects of scientific discovery which we believe are essential, yet under-researched with respect to automating scientific discovery processes. Firstly, from an Artificial Intelligence perspective, the majority of approaches to using AI techniques involve a disjointed sequential application of different problem solving methods, with the user providing the glue in various ways. These include routine logistical aspects such as the pre-processing of data and knowledge, translating outputs into input, choosing parameter settings for running AI methods, etc. More importantly, however, the user performs various aspects of meta-level reasoning, including asking the most pertinent questions, determining what it means if a process terminates with success and identifying – and investigating – anomalies. This approach tends to lead to auto-assisted discoveries where the user knows what they are looking for, but not what it looks like, rather than the deeper discoveries of examples/concepts/hypotheses/explanations that the user didn’t even know he or she was looking for. While AI methods promise the discovery of such surprising and novel scientific artefacts, they rarely deliver on this promise, as their application is too regimented within the problem solving paradigm of AI.