AbHbased
AbHbased (short for abductive-hypothesis-based) is a computational framework and methodology for hybrid reasoning that integrates abductive inference with hypothesis-driven evaluation. It is designed to support systems that must generate, rank and explain candidate explanations from incomplete or uncertain information, combining symbolic rules with probabilistic scoring to produce actionable hypotheses.
The approach emerged in the late 2010s in academic research addressing the gap between pure symbolic abduction
Architecturally, AbHbased systems typically include a knowledge representation layer (ontologies or rule sets), an abductive engine
Applications for AbHbased prototypes have spanned diagnostics, fault localization, legal and compliance reasoning, robotics planning, and