Description
I have been increasingly turning towards topics that sit at the intersection between computing, particularly artificial intelligence, and (the philosophy of) science. One such intersection is the automation of scientific reasoning. Four papers deal with this topic. Votsis (2016) proposes an intuitive way to formalise the notion of ad hoc hypotheses for (among other things) the benefit of scientific automation. Votsis (2024a) proposes to augment existing models of analogical reasoning via a supplementary condition, namely the relevant uniformity criterion on concepts, with the aim of improving our understanding of good analogical reasoning and its computational implementation. Votsis (2024b) proposes a neuro-symbolic approach to automating scientific discovery, making use of both neural nets and automated theorem provers to identify interesting hypotheses. Votsis (2025) argues that there is considerable potential for methodological cross-pollination between conceptual change practices in 'classical' scientific representations and feature changes in machine learning representations. I am also working on the epistemology of machine learning. This is a topic for which I received a fully-funded PhD student (Maria Federica Norelli) in 2023. The student works with me on issues such as the transparency and explainability of machine learning models, the effect of concept/variable/feature choice on the accuracy of the produced models and the evaluation of philosophical views, e.g. Bogen and Woodard’s view on data vs. phenomena vs. theories. The latter has been jointly produced with Jon Williamson, who is Maria's secondary supervisor, and is forthcoming - see below. Beyond this work, I have conducted research on automated theorem proving and related work on logical inference representation. This was the topic I pursued while visiting the Openproof Project, Center for the Study of Language and Information (which is subsumed under the Institute for Human-Centered AI), Stanford University during my last Sabbatical. During this time, I came up with a novel way of automating the construction of propositional logic proofs, whose outputs may potentially be easier to understand by humans than standard methods. My approach builds on the well-known resolution methods but avoids converting formulae into disjunctive normal form. I already implemented this idea in code (C#) and I'm planning to release an app that allows users to employ this method to construct proofs. I foresee two papers coming out of this work: one that attempts to prove the soundness and completeness of the proposed system, and another that provides a graph-theoretic representation of its inferences.
Bibliography:
Norelli, M.F., Votsis, I. and Williamson, J. (forthcoming) ‘Data, Phenomena, Models, and Theories’, Philosophy of Science.
Votsis, I. (2016) ‘Ad hoc Hypotheses and the Monsters within’, in V. C. Müller (ed.), Fundamental Issues of Artificial
Intelligence (Synthese Library), Berlin: Springer, pp. 299-313.
Votsis, I. (2024a) ‘Modelling Analogical Reasoning: One-Size-Fits-All?’, Wittgenstein and AI (Volume I), Anthem Press.
Votsis, I. (2024b) ‘A Neuro-Symbolic Approach to the Logic of Scientific Discovery’ in E. Ippoliti, L. Magnani, and S. Arfini (eds.), Model-Based Reasoning, Abductive Cognition, Creativity, Inferences & Models in Science, Logic, Language, and Technology Series, Springer.
Votsis, I. (2025) ‘Concept and Feature Change in Scientific and Deep Neural Net Representations’, Cognitive Science Proceedings, vol. 47: 1654-1660.