“Rise of the Robo Scientists”:where is data coming from?

Pieter Pauwels

Datasalon #621th January 2011

BOZAR, Brussel

Picture from: http://www.aber.ac.uk/en/cs/research/cb/projects/robotscientist/pictures/

The (boring) details

• What: The Robot Scientist Project• When: 1999 – ongoing• Where: Aberystwyth University Wales &

Cambridge University England• Who: Adam (5m x 3m x 3m) & Eve• Why: instead of merely creating a “deluge of

data” for the scientist, Adam aims at activelyhelping in the experimental research of microbiologists through hypothesis generationand testing

Science 3 April 2009: Vol. 324 no. 5923 pp. 85-89 DOI: 10.1126/science.1165620

Scientific American 17 January 2011: Vol. 304 pp. 72-77

DOI: 10.1038/scientificamerican0111-72

1. The Robot Scientists Project

2. Logic

3. Relation with “where is data coming from??”

Movie from: http://www.aber.ac.uk/en/cs/research/cb/projects/robotscientist/

The Robot Scientist makes use of an iterative approach to experimentation, where knowledge acquired from a previous iteration is used to guide the next experimentation step. This is a process known as Active Learning, where the learner can plan its own agenda, i.e. decide how best to improve its knowledge base and how to go about acquiring this information. The Robot Scientist uses the laboratory robot to execute the experiment(s) selected as most informative; has a plate reader to analyse the experiments, generating data corresponding to the scientific observations; uses abductive logic programming to generate valid hypotheses that explain the observations; and uses these hypotheses to determine the next most informative experiment. At the beginning of any investigation, the Robot Scientist has not discovered any information, therefore all possible hypotheses are equally valid. As the directed discovery process continues, each new observation (or experiment/interpretation cycle) will invalidate some of the hypotheses, thereby excluding incorrect discoveries. The experiment selection process aims to choose the experiment most likely to refute the most hypotheses. This iterative process allows irrelevant experiments to be avoided, potentially saving both laboratory time and the cost of using unnecessary reagents and biological materials.

Quote from: http://www.aber.ac.uk/en/cs/research/cb/projects/robotscientist/

Scientific active learning system

1. The Robot Scientists Project

2. Logic

3. Relation with “where is data coming from??”

Charles Sanders Peirce (1839 – 1914)

“the process of scientific enquiry”(cfr. C.S. Peirce)

Image from: Flach and Kakas. Abductive and Inductive Reasoning: Background and Issues. In: Abduction and Induction: Essays on their Relation and Integration.

Kluwer Academic Press, pp. 1-27, 2000.

“the process of scientific enquiry”

Close the loop!

hypothesis

prediction observation

Example 1

• Abduction:Result: grass is wetRule: it has rained -> grass is wetCase: it has rained

• Deduction:Rule: it has rained -> grass is wetCase: it has rainedResult: grass is wet

• Induction:Case: it has rainedResult: grass is wetRule: it has rained -> grass is wet

Example 2

• Abduction:Result: grass is wetRule: it has rained -> grass is wet

sprinklers are on -> grass is wet=> Hypothesis: it has rained

• Deduction:Rule: it has rained -> pluviometer is fullCase: it has rained=> Prediction: pluviometer is full

• Experiment

• Induction:Case: it has rained it has rainedResult: grass is wet pluviometer is full=> Rule: it has rained -> grass is wet it has rained -> pluviometer is full

1. The Robot Scientists Project

2. Logic

3. Relation with “where is data coming from??”