Abductive reasoning

About: Abductive reasoning is a research topic. Over the lifetime, 1917 publications have been published within this topic receiving 44645 citations. The topic is also known as: abduction & abductive inference.

An expert system approach to diagnosis of product quality deviations

Abstract: The diagnostic task of relating observed product quality data to operating parameters involves mapping from magnitude and directional changes in product quality attributes to explanatory changes in operating parameters. Working knowledge is typically in the form of individual parameter-product quality relationships. Thus, the predominant diagnostic task is one of assembling an overall hypothesis about changes in operating parameters from relationships which offer pieces of explanatory information. This kind of inferencing is referred to generically as Abductive Inference, where, given observed data, an explanation is found which best accounts for the data. A computational structure which captures general aspects of this type of reasoning is hypothesis assembly in which interacting hypothesis parts are combined to form an overall explanatory hypothesis.

Cognitive Robotics: On the Semantic Knife-edge

Abstract: This paper describes some aspects of recent and ongoing work in the area of Cognitive Robotics in the Department of Electrical and Electronic Engineering at Imperial College. Our approach to Cognitive Robotics has been to apply abductive reasoning procedures using the Event Calculus, an extension to First Order Predicate Calculus (FOPC), to provide a unified view of several related mobile robotics tasks: sensor data assimilation, map-building and planning. Cognitive robotics depends on an explicit declarative representation. While this greatly facilitates reasoning about domain knowledge, it comes with an extra c omputational overhead. This is the basis of the semantic knife-edge, maintaining a delicate balance between expressivity and efficient implementation.

GEAR: A General Inference Engine for Automated MultiStrategy Reasoning

Abstract: The pervasive use of AI today caused an urgent need for human-compliant AI approaches and solutions that can explain their behavior and decisions in human-understandable terms, especially in critical domains, so as to enforce trustworthiness and support accountability. The symbolic/logic approach to AI supports this need because it aims at reproducing human reasoning mechanisms. While much research has been carried out on single inference strategies, an overall approach to combine them is still missing. This paper claims the need for a new overall approach that merges all the single strategies, named MultiStrategy Reasoning. Based on an analysis of research on automated inference in AI, it selects a suitable setting for this approach, reviews the most promising approaches proposed for single inference strategies, and proposes a possible combination of deduction, abduction, abstraction, induction, argumentation, uncertainty and analogy. It also introduces the GEAR (General Engine for Automated Reasoning) inference engine, that has been developed to implement this vision.

Usefulness of Abductive Reasoning in Nursing Education: A Pilot Study.

Abstract: BACKGROUND Hypothetico-deductive reasoning used by novice nurses could limit their ability to explain a presenting care situation in its entirety. Hence, scholars recommend the use of abductive reasoning as an alternative approach. PURPOSE This study explored the effects of abductive reasoning training on baccalaureate nursing students' hypothesis generation abilities. METHOD Through a pretest-posttest study, we delivered educational training on abductive reasoning and examined hypothesis accuracy, expertise, and breadth. Participants generated scenario-specific hypotheses before and after the training. Academic content experts validated the scenarios, and 2 independent raters scored participants' hypotheses. RESULTS Twenty first- and second-year nursing students participated in this pilot study. Posttest scores showed a significant improvement in participants' hypothesis generation abilities: accuracy (P < .001), expertise (P < .001), and breadth (P = .006). CONCLUSION Abductive reasoning training in nursing education may improve students' hypothesis generation abilities.