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.

Does studying logic improve logical reasoning

Abstract: There has long been debate over whether studying mathematics improves one’s logical reasoning skills. In fact, it is even unclear whether studying logic improves one’s logical reasoning skills. A previous study found no improvement in conditional reasoning behaviour in students taking a semester long course in logic. However, the reasoning task employed in that study has since been criticised, and may not be a valid measure of reasoning. Here, we investigated the development of abstract conditional reasoning skills in students taking a course in formal logic, using a more sophisticated measure. Students who had previous experience of logic improved significantly, while students with no previous experience did not improve. Our results suggest that it is possible to teach logical thinking, given a certain degree of exposure.

Intellectual gravity and elective attractions: the provenance of Peirce's categories in Friedrich von Schiller

Abstract: The paper’s methodological prolegomena eschews narrow-gauge nominalistic approaches to Peirce in favor of his own synoptic (synechisticsynergistic) style of constructing his categorial architectonic in dialogue with the major ideas in the history of philosophy. As a “first” case in point, this paper focuses upon the provenance of and convergence with Friedrich von Schiller’s Aesthetic Letters in Peirce’s earliest Tuism and in his remembrance of Schiller in his mature phase. In-depth exegesis reveals that Schiller’s classic contains the seeds of Peirce’s trichotomic categorization of experience in three confluent strands of his developing system: 1) his phenomenological category of Firstness—corresponding to Schiller’s sense of “pure appearance” in the Spiel-trieb, as its plays out in Peirce’s prioritizing of abductive inference in inquiry and in the tychastic component of his cosmological metaphysics; 2) Esthetics as the “first” of the Normative Sciences; and, 3) the concept of Pure Play as “Musement” in his ‘A Neglected Argument for the Reality of God’ (1908) and in ‘An Essay toward Improving Our Reasoning in Security and in Uberty’ (1913)

Towards Scientific Incident Response

Abstract: A scientific incident analysis is one with a methodical, justifiable approach to the human decision-making process. Incident analysis is a good target for additional rigor because it is the most human-intensive part of incident response. Our goal is to provide the tools necessary for specifying precisely the reasoning process in incident analysis. Such tools are lacking, and are a necessary (though not sufficient) component of a more scientific analysis process. To reach this goal, we adapt tools from program verification that can capture and test abductive reasoning. As Charles Peirce coined the term in 1900, “Abduction is the process of forming an explanatory hypothesis. It is the only logical operation which introduces any new idea.” We reference canonical examples as paradigms of decision-making during analysis. With these examples in mind, we design a logic capable of expressing decision-making during incident analysis. The result is that we can express, in machine-readable and precise language, the abductive hypotheses than an analyst makes, and the results of evaluating them. This result is beneficial because it opens up the opportunity of genuinely comparing analyst processes without revealing sensitive system details, as well as opening an opportunity towards improved decision-support via limited automation.

On Explainable AI and Abductive Inference

Abstract: Modern explainable AI (XAI) methods remain far from providing human-like answers to ‘why’ questions, let alone those that satisfactorily agree with human-level understanding. Instead, the results that such methods provide boil down to sets of causal attributions. Currently, the choice of accepted attributions rests largely, if not solely, on the explainee’s understanding of the quality of explanations. The paper argues that such decisions may be transferred from a human to an XAI agent, provided that its machine-learning (ML) algorithms perform genuinely abductive inferences. The paper outlines the key predicament in the current inductive paradigm of ML and the associated XAI techniques, and sketches the desiderata for a truly participatory, second-generation XAI, which is endowed with abduction.