Abductive Computational Systems: Creative Abduction and Future Directions
🤖 AI Summary
Existing theoretical frameworks and computational systems inadequately support creative abductive hypothesis generation: formal theories lack generative models, while computational implementations remain largely confined to syllogistic abduction. Method: Through philosophical epistemological analysis and cross-domain comparative evaluation of computational systems, we systematically deconstruct the application of abduction in scientific discovery and design, and identify structural limitations—particularly regarding hypothesis novelty, openness, and semantic richness—in dominant models (e.g., Peircean, logic-programming, and Bayesian approaches). Contribution/Results: We introduce the novel concept of the “generative mechanism gap,” transcending traditional formal abductive paradigms; construct a theory-gap map specifically oriented toward creative hypothesis generation; and delineate a concrete technical pathway integrating cognitive modeling, semantic expansion, and interactive exploration. This work provides foundational theoretical guidance and actionable research directions for developing AI systems capable of genuine creative abduction.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Understanding abductive reasoning across diverse domains
Evaluating computational systems' use of abductive reasoning
Addressing gaps in creative hypothesis generation computationally
Innovation
Methods, ideas, or system contributions that make the work stand out.
Reviews abductive reasoning in multiple domains
Analyzes computational abductive reasoning systems
Proposes future directions for creative abduction
🔎 Similar Papers
No similar papers found.
