An epistemic and dynamic approach to abductive reasoning: Abductive problem and abductive solution

Velázquez–Quesada, Fernando R.; Toscano, Fernando Soler; Nepomuceno–Fernández, Ángel

doi:10.1016/j.jal.2013.07.002

Cited by 19 publications

(7 citation statements)

References 20 publications

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“…Conner et al (2014), using Toulmin's argumentation model, emphasise abductive, deductive, inductive, and analogical reasoning. Furthermore, Velázquez-Quesada et al (2013) explain that abductive reasoning is an activity that follows the phase of recognising the existence of abductive problems; identifying candidates for solutions; selecting 'the best' solutions, and assimilating the chosen solution.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Many researchers assume that abductive reasoning takes many roles in the development of science. These roles include building hypotheses (Kwon et al, 2006), generalizing models (Park & Lee, 2016), supporting the induction process (Rivera & Becker, 2007), increasing reasoning ability (Shodikin, 2017), generating new ideas (O'Reilly, 2016), building new schemes (Norton, 2008), solving mathematical problems (Cifarelli, 2016), being the main trigger for mathematical inquiry (Park & Lee, 2018), making claims about the validity of questions (Wu et al, 2016), and diagnosing medical errors (Velázquez-Quesada et al, 2013). Meanwhile, abductive reasoning itself is conjectural reasoning, whose opinions or conclusions are obtained based on incomplete information, where the conjecture itself is characterised as explicit statements that may be "right or wrong" (Norton, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Students’ Thinking Process When Using Abductive Reasoning in Problem Solving

et al. 2021

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Background: Abductive reasoning is the process of making conjectures to explain surprising observations. Although this conjecture is not certain to be true, in solving a problem, this reasoning is very helpful to determine the best solution strategy. Objectives: The study aims to investigate whether all types of abductive reasoning lead to the formation of new schemes. Design: This research used a qualitative approach with a descriptive exploratory design. Setting and Participants: A total of 41 students of the research degree in mathematics education programme were involved in solving a task. Then, eight of them were chosen for an in-depth interview, representing the undercoded and overcoded abductive reasoning types. Data collection and analysis: The data collected were the results of the students' works and task-based interviews. Piaget's schema theory was used to analyse students' thinking processes using abductive reasoning. The analysis was carried out at all steps of problem solving, namely understanding the problem, devising a plan, carrying out the plan, and looking back. Results: Those who carried out overcoded abductive reasoning used this reasoning to determine problem solving strategies. Meanwhile, those who carried out undercoded abductive reasoning used it to determine problem solving strategies as well as to form new schemes. Conclusions: The results showed that students who did abductive reasoning did not always produce new schemes. This study also notes that the truth value of answers from the application of abductive reasoning in problem solving was open and the importance of the look back step to perform accommodation.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Students’ Thinking Process When Using Abductive Reasoning in Problem Solving

et al. 2021

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“…Note that contrastive reasons are a subset of all possible abductive reasons. We adopt the definition of abduction from [59] who define abduction as a process of belief change that is triggered by an observation and guided by the knowledge and belief that an agent has the ability to derive. Hence, contrast is a measure of change created by an input image x in the parameters of a trained network against the contrast class.…”

Section: Contrastive Featuresmentioning

confidence: 99%

Contrastive Explanations In Neural Networks

Prabhushankar

Kwon

Temel

et al. 2020

2020 IEEE International Conference on Image Processing (ICIP)

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Neural networks represent data as projections on trained weights in a high dimensional manifold. The trained weights act as a knowledge base consisting of causal class dependencies. Inference built on features that identify these dependencies is termed as feed-forward inference. Such inference mechanisms are justified based on classical cause-to-effect inductive reasoning models. Inductive reasoning based feed-forward inference is widely used due to its mathematical simplicity and operational ease. Nevertheless, feed-forward models do not generalize well to untrained situations. To alleviate this generalization challenge, we propose using an effect-to-cause inference model that reasons abductively. Here, the features represent the change from existing weight dependencies given a certain effect. We term this change as contrast and the ensuing reasoning mechanism as contrastive reasoning. In this paper, we formalize the structure of contrastive reasoning and propose a methodology to extract a neural network's notion of contrast. We demonstrate the value of contrastive reasoning in two stages of a neural network's reasoning pipeline : in inferring and visually explaining decisions for the application of object recognition. We illustrate the value of contrastively recognizing images under distortions by reporting an improvement of 3.47%, 2.56%, and 5.48% in average accuracy under the proposed contrastive framework on CIFAR-10C, noisy STL-10, and VisDA datasets respectively.

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“…During the reasoning process, a type of logical inference referred to as abductive reasoning is adopted. Abductive reasoning was proposed by Pierce, and is usually used to determine possible and reasonable explanations for a given problem and introduce new ideas into design [38][39][40][41][42][43]. As shown in Figure 9, the generation of solution ideas is reasoned backwards in the equation from the current problem, which is the "value" that needs to be achieved.…”

Section: Solution Generationmentioning

confidence: 99%

Triple-Helix Structured Model Based on Problem-Knowledge-Solution Co-evolution for Innovative Product Design Process

Qian

Tao

et al. 2020

Chin. J. Mech. Eng.

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A design problem with deficient information is generally described as wicked or ill-defined. The information insufficiency leaves designers with loose settings, free environments, and a lack of strict boundaries, which provides them with more opportunities to facilitate innovation. Therefore, to capture the opportunity behind the uncertainty of a design problem, this study models an innovative design as a composite solving process, where the problem is clarified and resolved from fuzziness to satisfying solutions by interplay among design problems, knowledge, and solutions. Additionally, a triple-helix structured model for the innovative product design process is proposed based on the co-evolution of the problem, solution, and knowledge spaces, to provide designers with a distinct design strategy and method for innovative design. The three spaces interact and co-evolve through iterative mappings, including problem structuring, knowledge expansion, and solution generation. The mappings carry the information processing and decision-making activities of the design, and create the path to satisfying solutions. Finally, a case study of a reactor coolant flow distribution device is presented to demonstrate the practicability of this model and the method for innovative product design.

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An epistemic and dynamic approach to abductive reasoning: Abductive problem and abductive solution

Cited by 19 publications

References 20 publications

Students’ Thinking Process When Using Abductive Reasoning in Problem Solving

Students’ Thinking Process When Using Abductive Reasoning in Problem Solving

Contrastive Explanations In Neural Networks

Triple-Helix Structured Model Based on Problem-Knowledge-Solution Co-evolution for Innovative Product Design Process

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