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Explaining AI as an Exploratory Process: The Peircean Abduction Model Artificial Intelligence

Current discussions of "Explainable AI" (XAI) do not much consider the role of abduction in explanatory reasoning (see Mueller, et al., 2018). It might be worthwhile to pursue this, to develop intelligent systems that allow for the observation and analysis of abductive reasoning and the assessment of abductive reasoning as a learnable skill. Abductive inference has been defined in many ways. For example, it has been defined as the achievement of insight. Most often abduction is taken as a single, punctuated act of syllogistic reasoning, like making a deductive or inductive inference from given premises. In contrast, the originator of the concept of abduction---the American scientist/philosopher Charles Sanders Peirce---regarded abduction as an exploratory activity. In this regard, Peirce's insights about reasoning align with conclusions from modern psychological research. Since abduction is often defined as "inferring the best explanation," the challenge of implementing abductive reasoning and the challenge of automating the explanation process are closely linked. We explore these linkages in this report. This analysis provides a theoretical framework for understanding what the XAI researchers are already doing, it explains why some XAI projects are succeeding (or might succeed), and it leads to design advice.

Diagrams as Scaffolds for Abductive Insights

AAAI Conferences

Based on a typology of five basic forms of abduction, I propose a new definition of abductive insight that empha sizes in particular the inferential structure of a belief system that is able to explain a phenomenon after a new, abductive ly created component has been added to this system or the entire system has been abductively restructured. My thesis is, first, that the argumentative structure of the pursued problem solution guides abductive creativity and, second, that diagrammatic reasoning—if conceptualized according to the requirements defined by Charles Peirce—can support this guidance. This support is mainly possible based on the normative power of the system of representation that has to be used to construct diagrams and to perform experiments with them.

Breiman's "Two Cultures" Revisited and Reconciled Artificial Intelligence

In a landmark paper published in 2001, Leo Breiman described the tense standoff between two cultures of data modeling: parametric statistical and algorithmic machine learning. The cultural division between these two statistical learning frameworks has been growing at a steady pace in recent years. What is the way forward? It has become blatantly obvious that this widening gap between "the two cultures" cannot be averted unless we find a way to blend them into a coherent whole. This article presents a solution by establishing a link between the two cultures. Through examples, we describe the challenges and potential gains of this new integrated statistical thinking.

Peircean Induction and the Error-Correcting Thesis (Part I)


Today is C.S. Peirce's birthday. You should read him: he's a treasure chest on essentially any topic, and he anticipated several major ideas in statistics (e.g., randomization, confidence intervals) as well as in logic. Links to Parts 2 and 3 are at the end. It's written for a very general philosophical audience; the statistical parts are pretty informal. Peirce's philosophy of inductive inference in science is based on the idea that what permits us to make progress in science, what allows our knowledge to grow, is the fact that science uses methods that are self-correcting or error-correcting: Induction is the experimental testing of a theory.

On the Mechanization of Abductive Logic


Abduction is a basic form of logical inference, which is said to engender the use of plans, perceptual models, intuitions, and analogical reasoning - all aspects of Intelligent behavior that have so far failed to find representation in existing formal deductive systems. This paper explores the abductive reasoning process and develops a model for it s mechanization, .which consists of an embedding of deductive logic in an iterative hypothesis and test procedure. An application of the method to the problem of medical diagnosis is discussed.In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.