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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.


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Book ChapterDOI
Arsha Ali1
01 Jan 2022
Journal ArticleDOI
TL;DR: The authors discuss distinctions between reasoning in statistics and mathematics, use diagrams of argumentation to illustrate these differences in practice, and propose that warrants, rebuttals, and qualifiers are important aspects of distinguishing arguments that involve statistical reasoning.
Abstract: Reasoning plays an important role in mathematics and statistics, but the kinds of reasoning used to establish results differ between mathematics and statistics. In general, we see more probabilistic and contextual reasoning in statistics, whereas in algebra and other areas of mathematics, results rely on deductive reasoning, perhaps after inductive or abductive reasoning is used to examine patterns. Secondary school teachers are expected to teach topics from both mathematics and statistics, and they are asked to use collective argumentation in their teaching. It is important for teachers to support their students in making arguments that use appropriate reasoning for the subject in which they are engaged. In this paper, we discuss distinctions between reasoning in statistics and mathematics, use diagrams of argumentation to illustrate these differences in practice, and propose that warrants, rebuttals, and qualifiers are important aspects of distinguishing arguments that involve statistical reasoning.
Book ChapterDOI
01 Jan 2022
Journal ArticleDOI
03 Apr 2020
TL;DR: This paper shows that the state-of-the-art language encoding models are especially bad at modeling directional relations between sentences by proposing a new evaluation task: Cause-and-Effect relation prediction task, and demonstrates that a mutual attention mechanism can guide the model to focus on capturing directional Relations between sentences when added to existing transformer-based models.
Abstract: Enabling the models with the ability of reasoning and inference over text is one of the core missions of natural language understanding Despite deep learning models have shown strong performance on various cross-sentence inference benchmarks, recent work has shown that they are leveraging spurious statistical cues rather than capturing deeper implied relations between pairs of sentences In this paper, we show that the state-of-the-art language encoding models are especially bad at modeling directional relations between sentences by proposing a new evaluation task: Cause-and-Effect relation prediction task Back by our curated Cause-and-Effect Relation dataset (Cℰℛ), we also demonstrate that a mutual attention mechanism can guide the model to focus on capturing directional relations between sentences when added to existing transformer-based models Experiment results show that the proposed approach improves the performance on downstream applications, such as the abductive reasoning task
Journal Article
TL;DR: A method for enabling the ILP framework to deal with quantitative information from some experimental data in systems biology by discretizing the evolution of concentrations of metabolites during experiments and transcribing enzymatic kinetics into logic rules.
Abstract: The study of systems biology through inductive logic programming (ILP) aims at improving the understanding of the physiological state of the cell by reasoning with rules and relations instead of ordinary differential equations. This paper presents a method for enabling the ILP framework to deal with quantitative information from some experimental data in systems biology. The method consist in both discretizing the evolution of concentrations of metabolites during experiments and transcribing enzymatic kinetics (for instance Michaelis-Menten kinetics) into logic rules. Kinetic rules are added to background knowledge, along with the topology of the metabolic pathway, whereas discretized concentrations are observations. Applying ILP allows for abduction and induction in such a system. A logical model of the glycolysis and pentose phosphate pathways of E. Coli is proposed to support our method description. Logical formulae on concentrations of some metabolites, which could not be measured during the dynamic state, are produced through logical abduction. Finally, as this results in a large number of hypotheses, they are ranked with an expectation maximization algorithm working on binary decision diagrams.

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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022103
202156
202059
201956
201867