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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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Journal ArticleDOI
TL;DR: The possibility of designing AI that can learn logical or non-logical inference rules from data is considered, and an induction algorithm LF1T, which learns logic programs from interpretation transitions, successfully produces deductive inferencerules from input data.
Abstract: This paper considers the possibility of designing AI that can learn logical or non-logical inference rules from data. We first provide an abstract framework for learning logics. In this framework, an agent $${{{\mathcal {A}}}}$$ provides training examples that consist of formulas S and their logical consequences T. Then a machine $${{{\mathcal {M}}}}$$ builds an axiomatic system that makes T a consequence of S. Alternatively, in the absence of an agent $$\mathcal{A}$$ , a machine $${{{\mathcal {M}}}}$$ seeks an unknown logic underlying given data. We next consider the problem of learning logical inference rules by induction. Given a set S of propositional formulas and their logical consequences T, the goal is to find deductive inference rules that produce T from S. We show that an induction algorithm LF1T, which learns logic programs from interpretation transitions, successfully produces deductive inference rules from input data. Finally, we consider the problem of learning non-logical inference rules. We address three case studies for learning abductive inference, frame axioms, and conversational implicature. Each case study uses machine learning techniques together with metalogic programming.

1 citations

Journal ArticleDOI
TL;DR: First, the inference mechanism using realistic abductive reasoning model is briefly described and then probability is assigned to each of the explanations so as to pick up the explanations in the decreasing order of plausibility.
Abstract: In this paper, we give a method for probabilistic assignment to the Realistic Abductive Reasoning Model, The knowledge is assumed to be represented in the form of causal chaining, namely, hyper-bipartite network. Hyper-bipartite network is the most generalized form of knowledge representation for which, so far, there has been no way of assigning probability to the explanations, First, the inference mechanism using realistic abductive reasoning model is briefly described and then probability is assigned to each of the explanations so as to pick up the explanations in the decreasing order of plausibility.

1 citations

Book ChapterDOI
01 Jan 2009
TL;DR: In this paper, the authors argue that truth does not disappear in a model which puts abductive reasoning at the heart of the connection between ideas and reality, and that the kernel of religion, namely the existence and activity of God, is not reduced to human processes.
Abstract: The logic of science coincides, according to Popper, with a logical account of the method of empirical science This thesis is disputed by other philosophers of science, such as Norwood Russell Hanson The discovery of theories in science is characterised by the logic of abduction or retroduction This logic of abduction is integrated by Charles Ragin in a unitary model of empirical science Ragin gives abductive reasoning a place next to deductive and inductive reasoning in the process of connecting ideas to reality Quantitative, qualitative and comparative research are the three types of empirical research in theology Two fundamental objections to this unitary model are raised: does truth not disappear in a model which puts abductive reasoning at the heart of the connection between ideas and reality? Is the kernel of religion, namely the existence and activity of God, not reduced to human processes?Keywords: comparative research; empirical research; God; human processes; qualitative research; quantitative research; religion; unitary model

1 citations

Book ChapterDOI
01 Jan 2020
TL;DR: In this article, a theory of socially engineered cyber deception and theft (SECT), with routine activity theory, crime displacement theory, the space transition theory, and empirical review as its foundation, is proposed.
Abstract: Socially engineered cyber deception and theft seems to have gained prominence in cybercrime. Given the contextual background of inadequate theoretical explanations of socially engineered cyber deception and theft cybercrime, there is the need for theory to better explain and possibly predict activities involved in socially engineered cyber deception and theft. This chapter proposes a theory of socially engineered cyber deception and theft (SECT), with routine activity theory, crime displacement theory, the space transition theory, and empirical review as its foundation. It iteratively combines deductive and inductive approaches to infer the occurrence of socially engineered cyber deception and theft. While the deductive approach serves the deduction leading to the inference, the inductive approach extracts and suggests empirical evidence for a deterministic prediction of the crime occurrence. It is recommended that the theory is further validated to test its applicability.

1 citations


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