Automatizando a descoberta: o que podemos aprender com o estudo do raciocínio abdutivo?

Mariana Vitti  Rodrigues; Maria Eunice Quilici Gonzalez

doi:10.23925/2316-5278.2024v25i1:e68263

Authors

Mariana Vitti Rodrigues Universidade Estadual Paulista "Júlio de Mesquita Filho"
Maria Eunice Quilici Gonzalez Universidade Estadual Paulista "Júlio de Mesquita Filho"

DOI:

https://doi.org/10.23925/2316-5278.2024v25i1:e68263

Keywords:

Abduction, AI-Descartes, Automation, Inference to the best explanation

Abstract

The aim of this paper is to investigate the extent to which abductive inference can be automated. In order to do so, we present the Peircean account of abduction, according to which abduction is the process of generating and selecting an explanatory hypothesis that guides future inquiry (CP 5.171; 1903). Then, we introduce the contemporary concept of abduction characterized as Inference to the Best Explanation (IBE), which aim is to select a hypothesis, among a set of available hypotheses, considering their explanatory potential in terms of likelihood and loveliness (Lipton, 2004). Subsequently, we discuss IBE in relation to Bayesianism, according to which rational agents update their degrees of beliefs in a proposition based on new evidence and explanatory considerations. To illustrate our analysis, we present the software called AI-Descartes, an open-source AI system that combines logical reasoning with symbolic regression, aiming to derive scientific discovery from axiomatic knowledge and experimental data (Cornelio et al., 2023). Finally, we provide considerations about the relevance of studying abduction in the context of Artificial Intelligence.

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