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Our proposal integrates the scientific method with 21st century automation technology, with the goal of making scientific discovery more efficient (cheaper, faster, better). A Robot Scientist is a physically implemented laboratory automation system that exploits techniques from the field of artificial intelligence to execute cycles of scientific experimentation. Our vision is that within 10 years many scientific discoveries will be made by teams of human and robot scientists, and that such collaborations between human and robot scientists will produce scientific knowledge more efficiently than either could alone. In this way the productivity of science will be increased, leading to societal benefits: better food security, better medicines, etc. The Physics Nobel Laureate Frank Wilczek has predicted that the best scientist in one hundred years time will be a machine. The proposed project aims to take that prediction several steps closer. We will develop the AdaLab (an Adaptive Automated Scientific Laboratory) framework for semi-automated and automated knowledge discovery by teams of human and robot scientists. This framework will integrate and advance a number of ICT methodologies: knowledge representation, ontology engineering, semantic technologies, machine learning, bioinformatics, and automated experimentation (robot scientists). We will evaluate the AdaLab framework on an important real-world application in cell biology with biomedical relevance to cancer and ageing. The core of AdaLab will be generic. The expected project outputs include:

  • An AdaLab demonstrated to be greater than 20% more efficient at discovering scientific knowledge (within a limited scientific domain) than human scientists alone.
  • A novel ontology for modelling uncertain knowledge that supports all aspects of the proposed AdaLab framework.
  • The first ever communication mechanism between human and robot scientists that standardises modes of communication, information exchange protocols, and the content of typical messages.
  • New machine learning methods for the generation and efficient testing of complex scientific hypotheses that are twice as efficient at selecting experiments as the best current methods.
  • A significant advance in the state-of-the-art in automating scientific discovery that demonstrates its scalability to problems an order of magnitude more complex than currently possible.
  • Novel biomedical knowledge about cell biology relevant to cancer and ageing.
  • A strengthened interdisciplinary research community that crosses the boundaries between multiple ICT disciplines, laboratory automation, and biology.
Call Topic: Adaptive Machines in Complex Environments (AMCE), Call 2013
Start date: (36 months)
Funding support: 1 400 000 €

Project partners

  • Brunel University - United Kingdom
  • University of Manchester - United Kingdom
  • University Paris-Nord - France
  • University of Evry-Val-d-Essonne - France
  • KU Leuven - Belgium