A Pragmatic Logic of Scientific Discovery

Sallantin, Jean; Dartnell, Christopher; Afshar, Mohammad

doi:10.1007/11893318_24

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Moreover, modalities can be used to type the products of this learning activity. Sallantin et al (2006) define the set of modalities useful for the researcher and his or her assistant to exchange logical judgments all along the discovery process. This set, which is closed by negation since the negation of one of the modalities does not involve a new modality, is visually represented as a geometrical figure based on Aristotle's square of opposition.…”

Section: Figure 1 Human-machine Interaction Cyclementioning

confidence: 99%

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Human Discovery and Machine Learning

Dartnell

Martin

Hagège

et al. 2008

International Journal of Cognitive Informatics and Natural Intelligence

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Submission to IJCINI This paper studies machine learning paradigms from the point of view of human cognition. Indeed, conceptions in both mahine learning and human learning evolved from a passive to an active conception of learning. Our objective is to provide an interaction protocol suited to both humans and machines, to enable assisting human discoveries by learning machines. We identify the limitations of common machine learning paradigms in the context of scientific discovery, and we propose an extension inspired by game theory and multi-agent systems. We present individual cognitive aspects of this protocol as well as social considerations, and we relate encouraging results concerning a game implementing it.

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Section: Figure 1 Human-machine Interaction Cyclementioning

confidence: 99%

“…This paper attempts to synthesize our work in these domains. (Sallantin, Dartnell, & Afshar, 2006) described the minimal logical prerequisite to endow a problem solver with a pragmatic logic of scientific discovery in order to interact efficiently with a scientist. We will informally summarize these requirements in the first section.…”

Section: Introductionmentioning

confidence: 99%

Human Discovery and Machine Learning

Dartnell

Martin

Hagège

et al. 2008

International Journal of Cognitive Informatics and Natural Intelligence

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“…The logical framework presented in this paper is designed to take into account the two dynamics of scientific discovery [2] [3]. The first dynamic, which we refer to as the personal dynamic, embraces the supervision of a computer assistant by a scientist.…”

Section: Introductionmentioning

confidence: 99%

Aristotle’s Square Revisited to Frame Discovery Science

Afshar¹,

Dartnell²,

Luzeaux³

et al. 2007

JCP

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The paper attempts to give a formal framework to capture the entire process of scientific discovery including hypothesis formation, reasoning, identifying contradictions, peer reviewing, reformulating and so on. Data mining can be seen as one step in this complex process of interactive learning of an empirical theory This paper uses the terminology from paraconsistent logic and paracomplete logic that extends Aristotle square in a hypercube of oppositions which defines or substantiates any step of the discovery process. The central formal notions are validated on a mathematical scientific discovery game, and an industrial application in the field of Drug Discovery illustrates how the presented framework combines different learning processes to predict pharmaco-kinetic properties (ADME-T) and adverse side effects of therapeutic drug molecules.

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Use of Biological Fingerprints Versus Structure/Chemotypes to Describe Molecules

Mason

2010

Burger's Medicinal Chemistry and Drug Discovery

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Molecules are usually described by their chemical structure and by fingerprints derived from this. These range from 2D structure based, that only represent the underlying structure that gives rise to the properties recognized by a biological target to 3D pharmacophores or molecular interaction fields, that much better represent how the protein binding sites would “see” a molecule. However, all of these have many limitations, including conformation for the 3D structure‐based approaches. More recently, experimental profiling data have been generated that enables a molecule to be described by a fingerprint of binding affinity to a diverse set of biological targets (pharmacological and “antitargets” such as CYP450 metabolic enzymes). These results show that small changes in structure can cause large changes in broad biological profile, and that a structure‐based analysis/clustering of compounds, such as different hits, leads, or clinical candidates, often does not provide a differentiation that is relevant in biological space. The data show that “selective” versus “nonselective” compounds, and the type of off‐target effects are not evident from a “chemotype” approach. The concept of “biological fingerprints” as a better way to describe compounds of biological interest is described in this chapter, focusing on the power of these descriptors versus structure‐based descriptors to differentiate compounds and enable the selection of the best lead compounds.

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A Pragmatic Logic of Scientific Discovery

Cited by 4 publications

References 9 publications

Human Discovery and Machine Learning

Human Discovery and Machine Learning

Aristotle’s Square Revisited to Frame Discovery Science

Use of Biological Fingerprints Versus Structure/Chemotypes to Describe Molecules

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