Date Published: June 26, 2019
Publisher: Public Library of Science
Author(s): Toby Howison, Josie Hughes, Fabio Giardina, Fumiya Iida, Josh Bongard.
Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour.
Complex physical phenomena are often governed by highly non-linear, multidimensional dynamics. Hence, it can be challenging to understand these systems using traditional modelling tools, as we lack knowledge of the underlying physical phenomena required to implement these. The obvious course of action, then, is to infer these phenomena via physical experimentation. Automating this inference process, in other words automating the discovery of system physics from experimental data, has been the focus of intensive study.
In this paper we presented the PDBC method as a framework for clustering and aiding understanding of systems with discrete behavioural modes. Furthermore, we presented the VSFP problem, a new category of falling paper systems, and applied the PDBC method to it.
For systems which do exhibit discrete behavioural modes, this approach opens up new avenues of analysis and understanding. However, further work is required to apply the method to systems with ambiguous or continuous behavioural phases. Additionally, further work is required in the choice of system parametrisation, output selection and behavioural interpretation. One of the main issues here is the human interpretation of system behaviours. Although relatively clear in the VSFP system, more complex system may exhibit a range of similar behaviours which are hard to distinguish between. Hence, there is scope to automate the process deciding what constitutes a discrete behavioural mode.