Causality and Prior Knowledge in Rational Models of Inductive Reasoning

Only seeing is believing – the power of evidence and reason

Postępy Biochemii ◽

10.18388/pb.2016_2 ◽

2016 ◽

Vol 62 (3) ◽

pp. 250-256

Author(s):

Bernhard Rupp

Keyword(s):

Prior Knowledge ◽

Inductive Reasoning ◽

Empirical Science ◽

Evidence Based ◽

Human Element ◽

Scientific Epistemology ◽

Biomolecular Crystallography ◽

Mature Science ◽

Physical Principles ◽

Strong Claim

Biomolecular crystallography is a mature science that provides an instructive example for modern inductive reasoning as a model for Bayesian epistemology in empirical science. Fundamental scientific epistemology requires that a strong claim is supported by strong and convincing proof. Biomolecular crystallography, based on solid foundations of rich experimental data and extensive prior knowledge provides a prime example for modern, evidence based reasoning that strongly relies on assessments of plausibility based on prior knowledge while at the same time constantly delivering some of the most novel and exciting results based on new experimental evidence. As a consequence of the solid underlying physical principles and its mathematical rigor, crystallography as a mature science could be almost fool proof â were it not for the human element.

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Students’ Inductive Reasoning Skills and the Relevance of Prior Knowledge: An Exploratory Study With a Computer-Based Training Course on the Topic ofAcne Vulgaris

Teaching and Learning in Medicine ◽

10.1080/10401334.2011.561698 ◽

2011 ◽

Vol 23 (2) ◽

pp. 130-136

Author(s):

Sabine Horn-Ritzinger ◽

Johannes Bernhardt ◽

Michael Horn ◽

Josef Smolle

Keyword(s):

Prior Knowledge ◽

Exploratory Study ◽

Inductive Reasoning ◽

Computer Based Training ◽

Training Course ◽

Computer Based ◽

Reasoning Skills

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Learning Ex Nihilo

10.29007/ggcf ◽

2020 ◽

Author(s):

Selmer Bringsjord ◽

Naveen Sundar Govindarajulu ◽

John Licato ◽

Michael Giancola

Keyword(s):

Prior Knowledge ◽

State Of The Art ◽

Inductive Reasoning ◽

Formal Proof ◽

Higher Order ◽

The Novel ◽

Ex Nihilo ◽

Implementation Level ◽

Novel Concept

This paper introduces, philosophically and to a degree formally, the novel concept of learn- ing ex nihilo, intended (obviously) to be analogous to the concept of creation ex nihilo. Learning ex nihilo is an agent’s learning “from nothing”, by the suitable employment of inference schemata for deductive and inductive reasoning. This reasoning must be in machine-verifiable accord with a formal proof/argument theory in a cognitive calculus (i.e., here, roughly, an intensional higher-order multi-operator quantified logic), and this reasoning is applied to percepts received by the agent, in the context of both some prior knowledge, and some prior and current interests. Learning ex nihilo is a challenge to con- temporary forms of ML, indeed a severe one, but the challenge is here offered in the spirit of seeking to stimulate attempts, on the part of non-logicist ML researchers and engineers, to collaborate with those in possession of learning-ex nihilo frameworks, and eventually attempts to integrate directly with such frameworks at the implementation level. Such integration will require, among other things, the symbiotic interoperation of state-of-the- art automated reasoners and high-expressivity planners, with statistical/connectionist ML technology.

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