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

Autores

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

Palavras-chave:

Abdução, AI-Descartes, Automação, Inferência à melhor explicação

Resumo

O objetivo deste artigo é investigar em que medida a inferência abdutiva pode ser automatizada. Para isso, apresentamos a noção peirceana de abdução, segundo a qual a abdução é o processo de geração e seleção de hipóteses explicativas que orientam a investigação científica (CP 5.171; 1903). Em seguida, apresentamos o conceito contemporâneo de abdução, caracterizado como Inferência à Melhor Explicação (IME), cujo objetivo é selecionar uma hipótese, entre um conjunto de hipóteses disponíveis, considerando seu potencial explicativo em termos de probabilidade e uberdade (Lipton, 2004). Subsequentemente, discutimos IME em relação ao Bayesianismo, segundo o qual agentes racionais atualizam seus graus de crença em uma proposição com base em novas evidências e considerações explicativas. Para ilustrar nossa análise, apresentamos o software denominado AI-Descartes, um sistema de Inteligência Artificial de código aberto que combina raciocínio lógico com regressão simbólica, projetado para derivar descobertas científicas a partir de conhecimento axiomático e dados experimentais (Cornelio et al., 2023). Por fim, apresentamos considerações sobre a relevância do estudo da abdução no contexto da Inteligência Artificial.

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