Incorporating background knowledge and structured explananda in abductive reasoning: a framework

Abductive reasoning plays a dominant role in a wide variety of cognitive tasks, including diagnosis, language understanding, and learning. The author´s work is aimed at developing a computational model of abductive reasoning keeping in view some of the important aspects that cut across specific instances of abductive reasoning. A significant portion of the paper is devoted to the discussion of two of those aspects, namely, those of background knowledge and structured explananda. The former aspect concerns the effect the agent´s memory of specific facts has on abductive reasoning. The latter aspect corresponds to dealing with explananda having conceptual structure and contrasts with the approach of taking explananda to be atomic. The paper discusses various issues related to these two aspects and develops an algorithm for abductive reasoning incorporating those aspects