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.

Mixed Methods and the Problem of the Logic of Inquiry

Abstract: The idea that “ irreconcilable epistemological differences ” exist between qualitative and quantitative methods places a very serious dampener on any proper debate about mixed methods, as well as upon any real progress that might be made in establishing a mature methodology for human and social inquiry. One possible solution is to take a more radical position, beginning by seeing the distinction between qualitative and quantitative as little more than a red herring. By focussing upon the type(s) of data we are collecting, we are merely polarizing the contrasts between different methodological approaches and deflecting attention away from what must be the underlying critical issue – which is the logic of inquiry . It needs to be understood that it is the issue of the logic of inquiry that lies at the heart of mixed methods. Any broad tradition of research can be seen as involving: assumptions, strategic decisions, methods of data collection, analysis, and critical evaluation, but will differ in the way each of these are implemented. At least three fundamentally different logics of inquiry can be distinguished: (i) theory-driven , (ii) data-driven , and (iii) explanation-driven , each with its own inherent pattern of logical reasoning, i.e. deductive , inductive and abductive inference, respectively. It is here that “mixed methods” faces its foremost challenge. The different logics of inquiry have radical implications for the phrasing of the research question(s), as well as the strategies adopted with respect to design, sampling, data collection, analysis and critical reflection. Combining different logics of inquiry into one research program must consider all of these factors carefully. However, in facing up to these various issues, the pay-off might be a much more authentic picture of what even “the scientific method” might actually entail.

Abductive Logic Programming with CIFF

Abstract: Abduction has found broad application as a powerful tool for hypothetical reasoning with incomplete knowledge, which can be handled by labelling some pieces of information as abducibles, i.e. as possible hypotheses which can be assumed to hold, provided that they are consistent with the given knowledge base. Abductive Logic Programming (ALP) [4] combines abduction with logic programming enriched with integrity constraints to further restrict the range of possible hypotheses. We introduce a new proof procedure for abductive logic programming which we call CIFF. Our procedure extends the IFF procedure of Fung and Kowalski [3] by integrating abductive reasoning with constraint solving. Another feature of our approach is that we do not attempt to provide a static characterisation of the class of allowed inputs on which the procedure can operate correctly, but rather check allowedness dynamically during a derivation. This allows us to cover a larger class of inputs.

Production Inference, Nonmonotonicity and Abduction.

Abstract: We introduce a general formalism of production inference relations that posses both a standard monotonic semantics and a natural nonmonotonic semantics. The resulting nonmonotonic system is shown to provide a syntax-independent representation of abductive reasoning. Abduction is a reasoning from facts to their possible explanations that is widely used now in many areas of AI, including diagnosis, truth maintenance, knowledge assimilation, database updates and logic programming. In this study we are going to show that this kind of reasoning can be given a formal, syntax-independent representation in terms of production inference relations that constitute a particular formalization of input-output logics [MdT00]. Among other things, such a representation will clarify the relation between abduction and nonmonotonic reasoning, as well as show the expressive capabilities of production inference as a general-purpose nonmonotonic formalism. We will assume that our basic language is a classical propositional language with the usual connectives and constants {∧,∨,¬,→, t, f}. 2 will denote the classical entailment, and Th the associated provability operator. 1 Production Inference Relations We begin with the following general notion of production inference.