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The Robot Scientist Project

  • Ross D. King
  • Michael Young
  • Amanda J. Clare
  • Kenneth E. Whelan
  • Jem Rowland
Conference paper
  • 575 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3735)

Abstract

We are interested in the automation of science for both philosophical and technological reasons. To this end we have built the first automated system that is capable of automatically: originating hypotheses to explain data, devising experiments to test these hypotheses, physically running these experiments using a laboratory robot, interpreting the results, and then repeat the cycle. We call such automated systems “Robot Scientists”. We applied our first Robot Scientist to predicting the function of genes in a well-understood part of the metabolism of the yeast S. cerevisiae. For background knowledge, we built a logical model of metabolism in Prolog. The experiments consisted of growing mutant yeast strains with known genes knocked out on specified growth media. The results of these experiments allowed the Robot Scientist to test hypotheses it had abductively inferred from the logical model. In empirical tests, the Robot Scientist experiment selection methodology outperformed both randomly selecting experiments, and a greedy strategy of always choosing the experiment of lowest cost; it was also as good as the best humans tested at the task. To extend this proof of principle result to the discovery of novel knowledge we require new hardware that is fully automated, a model of all of the known metabolism of yeast, and an efficient way of inferring probable hypotheses. We have made progress in all of these areas, and we are currently 6building a new Robot Scientist that we hope will be able to automatically discover new biological knowledge.

Keywords

Quantitative Structure Activity Relationship Biological Knowledge Inductive Logic Programming Laboratory Information Management System Average Classification Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Buchanan, B.G., Sutherland, G.L., Feigenbaum, E.A.: Toward automated discovery in the biological sciences. In: Machine Intelligence 4, pp. 209–254. Edinburgh University Press (1969)Google Scholar
  2. 2.
    Langley, P., Simon, H.A., Bradshaw, G.L., Zytkow, J.M.: Scientific Discovery: Computational Explorations of the Creative Process. MIT Press, Cambridge (1987)Google Scholar
  3. 3.
    King, R.D., Muggleton, S.H., Srinivasan, A., Sternberg, M.J.E.: Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proc. Nat. Acad. Sci. USA 93, 438–442 (1996)CrossRefGoogle Scholar
  4. 4.
    King, R.D., Whelan, K.E., Jones, F.M., Reiser, P.J.K., Bryant, C.H., Muggleton, S., Kell, D.B., Oliver, S.: Functional Genomic Hypothesis Generation and Experimentation by a Robot Scientist. Nature 427, 247–252 (1994)CrossRefGoogle Scholar
  5. 5.
    Popper, K.: The Logic of Scientific Discovery. Hutchinson, London (1972)Google Scholar
  6. 6.
    Reiser, P.G.K., King, R.D., Kell, D.B., Muggleton, S.H., Bryant, C.H., Oliver, S.G.: Developing a logical model of yeast metabolism. Electronic Transactions in Artificial Intelligence 5, 223–244 (2001)Google Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Chichester (2001)zbMATHGoogle Scholar
  8. 8.
    Bryant, C.H., Muggleton, S.H., Oliver, S.G., Kell, D.B., Reiser, P., King, R.D.: Combining inductive logic programming, active learning, and robotics to discover the function of genes. Electronic Transactions in Artificial Intelligence 5, 1–36 (2001)Google Scholar
  9. 9.
    Muggleton, S., Page, D.: A learnability model of universal representations and its application to top-down induction of decision trees. In: Furukawa, K., Michie, D., Muggleton, S. (eds.) Machine Intelligence 15, pp. 248–267. Oxford University Press, Oxford (1999)Google Scholar
  10. 10.
    King, R.D., Muggleton, S., Lewis, R.A., Sternberg, M.J.E.: Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proc. Nat. Acad. Sci. U.S.A. 89, 11322–11326 (1992)CrossRefGoogle Scholar
  11. 11.
    Levis, R.J., Menkir, G., Rabitz, H.: Selective Covalent Bond Dissociation and Rearrangement by Closed-Loop, Optimal Control of Tailored, Strong Field Laser Pulses. Science 292, 709–713 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ross D. King
    • 1
  • Michael Young
    • 1
  • Amanda J. Clare
    • 1
  • Kenneth E. Whelan
    • 1
  • Jem Rowland
    • 1
  1. 1.The University of WalesAberystwyth

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