7,298 research outputs found

    The Problem of Analogical Inference in Inductive Logic

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    We consider one problem that was largely left open by Rudolf Carnap in his work on inductive logic, the problem of analogical inference. After discussing some previous attempts to solve this problem, we propose a new solution that is based on the ideas of Bruno de Finetti on probabilistic symmetries. We explain how our new inductive logic can be developed within the Carnapian paradigm of inductive logic-deriving an inductive rule from a set of simple postulates about the observational process-and discuss some of its properties.Comment: In Proceedings TARK 2015, arXiv:1606.0729

    Determination, uniformity, and relevance: normative criteria for generalization and reasoning by analogy

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    This paper defines the form of prior knowledge that is required for sound inferences by analogy and single-instance generalizations, in both logical and probabilistic reasoning. In the logical case, the first order determination rule defined in Davies (1985) is shown to solve both the justification and non-redundancy problems for analogical inference. The statistical analogue of determination that is put forward is termed 'uniformity'. Based on the semantics of determination and uniformity, a third notion of "relevance" is defined, both logically and probabilistically. The statistical relevance of one function in determining another is put forward as a way of defining the value of information: The statistical relevance of a function F to a function G is the absolute value of the change in one's information about the value of G afforded by specifying the value of F. This theory provides normative justifications for conclusions projected by analogy from one case to another, and for generalization from an instance to a rule. The soundness of such conclusions, in either the logical or the probabilistic case, can be identified with the extent to which the corresponding criteria (determination and uniformity) actually hold for the features being related

    Evaluation of Analogical Inferences Formed from Automatically Generated Representations of Scientific Publications

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    Humans regularly exploit analogical reasoning to generate potentially novel and useful inferences. We outline the Dr Inventor model that identifies analogies between research publications, describing recent work to evaluate the inferences that are generated by the system. Its inferences, in the form of subjectverb-object triples, can involve arbitrary combinations of source and target information. We evaluate three approaches to assess the quality of inferences. Firstly, we explore an n-gram based approach (derived from the Dr Inventor corpus). Secondly, we use ConceptNet as a basis for evaluating inferences. Finally, we explore the use of Watson Concept Insights (WCI) to support our inference evaluation process. Dealing with novel inferences arising from an ever growing corpus is a central concern throughout

    Evaluation of Analogical Inferences Formed from Automatically Generated Representations of Scientific Publications

    Get PDF
    Humans regularly exploit analogical reasoning to generate potentially novel and useful inferences. We outline the Dr Inventor model that identifies analogies between research publications, describing recent work to evaluate the inferences that are generated by the system. Its inferences, in the form of subjectverb-object triples, can involve arbitrary combinations of source and target information. We evaluate three approaches to assess the quality of inferences. Firstly, we explore an n-gram based approach (derived from the Dr Inventor corpus). Secondly, we use ConceptNet as a basis for evaluating inferences. Finally, we explore the use of Watson Concept Insights (WCI) to support our inference evaluation process. Dealing with novel inferences arising from an ever growing corpus is a central concern throughout
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