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.

Dynamic Process Accident Analysis: Comparison of Bow tie and Bayesian Network Models

Abstract: Background and Objectives: Accidents of the industrial processes have caused irreparable economic, social, environmental and even political loses in the country. To prevent such accidents, identifying, evaluating and analyzing the causes of these incidents with new approaches are required for designing preventive strategies is a necessity. Therefore, the objective of the present study was directed toward the identifying and dynamic analyzing of the root causes of the process accidents. The Bowtie (BT) model and Bayesian Network (BN) were implemented for analyzing the accidents. Materials and Methods: First, the accidents' scenarios were modelled quantitatively and quantitatively using the BT model, and then, the cause-consequence model of the accident scenarios was modelled in the BN using the proposed algorithm. Capabilities of the BN including, deductive, abductive reasoning and updated probability was used for dynamic analysis of the accident scenarios. Results: The results showed that deductive reasoning for estimating the occurrence probabilities of a scenario and its consequences is more accurate by BN than BT. BN model is capable of doing probability updating of root events using the precursor accident data through abductive reasoning, taking into account conditional dependency among root events, safety barriers and modelling of common cause’s failures. However, BT model does not have such capabilities. Conclusion: In the present study, a novel, dynamic and quantitative model was introduced that allows the continuous identification and monitoring of the safety risks in the process industries. Implementing the proposed model in the process industries can significantly reduce the risk of the industrial accidents and improve the level of safety. How to cite this article: Zarei E, Mohammadfam I, Azadeh A, Mirzaei-Aliabadi M. Dynamic Process Accident Analysis: Comparison of Bow tie and Bayesian Network Models. J Saf Promot Inj Prev. 2017; 5(4):201-12 .

Runtime Addition of Integrity Constraints in SCIFF

Abstract: Abductive Logic Programming is a computationally founded representation of abductive reasoning. In most ALP frameworks, integrity constraints express domain-specific logical relationships that abductive answers are required to satisfy. Integrity constraints are usually known a priori. However, in some applications (such as interactive abductive logic programming, multi-agent interactions, contracting) it makes sense to relax this assumption, in order to let the abductive reasoning start with incomplete knowledge of integrity constraints, and to continue without restarting when new integrity constraints become known. In this paper, we propose a declarative semantics for abductive logic programming with addition of integrity constraints during the abductive reasoning process. We propose an operational instantiation (with formal termination, soundness and completeness properties) and an implementation of such a framework based on the SCIFF language and proof procedure.

Abductive Theory of Cognition. The Eco-Cognitive Model

Abstract: Abduction is a procedure in which something that lacks classical explanatory epistemic virtue can be accepted because it possesses a virtue of another type: Gabbay and Woods contend (GW- Model) that abduction presents an ignorance-preserving or (ignorance-mitigating) character (GABBAY – WOODS 2005). From this point of view abductive reasoning is a response to an ignorance-problem. Abductive reasoning is an ignorance-preserving accommodation of the problem at hand. Is abduction really ignorance- preserving? To better answer this question I will describe my eco-cognitive model (EC-model) of abduction some examples taken from the areas of both philosophy and epistemology. It will be demonstrated that through abduction, knowledge can be enhanced, even when abduction is not considered an inference to the best explanation in the classical sense of the expression, that is an inference inevitably characterized by an empirical evaluation stage, or an inductive stage, as Peirce called it. Peirce provides various justifications of the knowledge-enhancing role of abduction, even when abduction is not conceived an inference to the best explanation, that is an inference inevitably characterized by an empirical evaluation phase, as I just said. These justifications basically resort to the conceptual use of evolutionary and metaphysical ideas, which resort to indicate that abduction is constitutively akin to truth, even if certainly always ignorance-preserving or mitigating in the sense that the “absolute truth” is never reached through abduction. Finally, other two examples of knowledge-enhancing abduction will be indicated: abducing conventions and abducing scientific models .

Organic Kinship or Incidental Analogy? Similar Meaning Clusters in and Correspondances Between Folklore Texts and Pieces of Poetry

Abstract: with the spookily accurate coincidences of erotic motifs of a famous poet and the Hungarian folk tradition, we try to portray the hidden framework behind the similarities of different motifs. Using Gell's subtle and holistic model interconnecting the forms and "legacy" of art with the abductive reasoning, we get a point, where lot of "second generation" research questions are raising. 1. INTRODUCTION "No reasonable person could suppose that art-like

Graphical Logic Paradigm and Reasoning Process Research

Abstract: Through the mental models, mental logic and raven's graphic reasoning test research, this paper deduced a series of graphic reasoning paradigm, and summarized the process of graphical reasoning. Through the demonstration of problem difficulty experiment and written report experiment, confirmed a high relevance between the graphic logic steps iteration and species and questions difficulty. Furthermore, reasoning order and default graphical reasoning paradigm have a high degree of agreement. Confirmed the rationality and widespread adaptability of graphic reasoning paradigms.