This master thesis explores the challenge of algorithmic hypothesis generation and its connection to potential inductive biases. In the scientific method, hypotheses are formulated and tested against data for validity. The process of hypothesis generation is, however, not explicitly formulated. A structured approach to hypothesis generation would allow for a near algorithmic process that could scale far beyond the current capabilities of science. The thesis explored the concepts of entropy, symmetry and minimum description length for use as inductive biases. Two algorithms were implemented and evaluated: one for symmetry finding and one for symbolic regression. The theoretical results show a strong connection between entropy and minimum description length with a weaker connection to symmetry. Both implementations indicate potential paths exist to partial or full automation of the hypothesis generation process. In general, there do not seem to exist fundamental issues in automating the process besides the challenge of its implementation. Thus, the thesis demonstrates a clear path forward towards partial or complete automation of the scientific method for physical research.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:su-224501 |
| Date | January 2023 |
| Creators | Möller, Hampus |
| Publisher | Stockholms universitet, Fysikum |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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