Human-machine scientific discovery

International audienceHumanity is facing existential, societal challenges related to food security, ecosystem conservation, antimicrobial resistance, etc, and Artificial Intelligence (AI) is already playing an important role in tackling these new challenges. Most current AI approaches are limited when it comes to ‘knowledge transfer’ with humans, i.e. it is difficult to incorporate existing human knowledge and also the output knowledge is not human comprehensible. In this chapter we demonstrate how a combination of comprehensible machine learning, text-mining and domain knowledge could enhance human-machine collaboration for the purpose of automated scientific discovery where humans and computers jointly develop and evaluate scientific theories. As a case study, we describe a combination of logic-based machine learning (which included human-encoded ecological background knowledge) and text-mining from scientific publications (to verify machine-learned hypotheses) for the purpose of automated discovery of ecological interaction networks (food-webs) to detect change in agricultural ecosystems using the Farm Scale Evaluations (FSEs) of genetically modified herbicide-tolerant (GMHT) crops dataset. The results included novel food-web hypotheses, some confirmed by subsequent experimental studies (e.g. DNA analysis) and published in scientific journals. These machine-leaned food-webs were also used as the basis of a recent study revealing resilience of agro-ecosystems to changes in farming management using GMHT crops

Machine learning and text mining of trophic links

International audienceMachine Learning has been used to automatically generate a probabilistic food-web from Farm Scale Evaluation (FSE) data. The initial food web proposed by machine learning has been examined by domain experts and comparison with the literature shows that many of the links are corroborated. The FSE data were collected using two different sampling techniques, namely Vortis and pitfall. The corroboration of the initial Vortis food web, generated by machine learning, was performed manually by the domain experts. However, manual corroboration of hypothetical trophic links is difficult and requires significant amounts of time. In this paper we review the method and the main results on machine learning of trophic links. We study common trophic links from Vortis and pitfall data. We also describe a new method and present initial results on automatic corroboration of trophic links using text mining