Abductive Computational Systems: Creative Abduction and Future Directions

TL;DR

This paper reviews abductive reasoning across domains, analyzes computational implementations, and highlights the need for more creative hypothesis generation in future research.

Contribution

It provides a comprehensive analysis of abductive reasoning in computational systems and proposes directions for developing more creative abductive hypotheses.

Findings

Current systems mainly use syllogistic abductive reasoning

Theoretical models lack mechanisms for creative hypothesis generation

Future research should focus on enhancing creativity in abductive systems

Abstract

Abductive reasoning, reasoning for inferring explanations for observations, is often mentioned in scientific, design-related and artistic contexts, but its understanding varies across these domains. This paper reviews how abductive reasoning is discussed in epistemology, science and design, and then analyses how various computational systems use abductive reasoning. Our analysis shows that neither theoretical accounts nor computational implementations of abductive reasoning adequately address generating creative hypotheses. Theoretical frameworks do not provide a straightforward model for generating creative abductive hypotheses, computational systems largely implement syllogistic forms of abductive reasoning. We break down abductive computational systems into components and conclude by identifying specific directions for future research that could advance the state of creative abductive reasoning in computational systems.