The Feasibility of Defeat in Defeasible Reasoning

1993 ◽  
pp. 359-380 ◽  
Author(s):  
Gerard Vreeswijk
Keyword(s):  
Defeasible Reasoning

scholarly journals A Graphical Rule Authoring Tool for Defeasible Reasoning in the Semantic Web

2005 ◽  
pp. 404-414
Author(s):  
Nick Bassiliades ◽  
Efstratios Kontopoulos ◽  
Grigoris Antoniou ◽  
Ioannis Vlahavas
Keyword(s):  
Semantic Web ◽  
Defeasible Reasoning ◽  
Authoring Tool

scholarly journals Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods and Results

2021 ◽  
Author(s):  
Sadnan Al Manir ◽  
Justin Niestroy ◽  
Maxwell Adam Levinson ◽  
Timothy Clark
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Series Data ◽  
Predictive Analytics ◽  
Defeasible Reasoning ◽  
Series Data ◽  
Inference Rules ◽  
Deep Networks ◽  
Evidence Graph ◽  
Over Time

Introduction: Transparency of computation is a requirement for assessing the validity of computed results and research claims based upon them; and it is essential for access to, assessment, and reuse of computational components. These components may be subject to methodological or other challenges over time. While reference to archived software and/or data is increasingly common in publications, a single machine-interpretable, integrative representation of how results were derived, that supports defeasible reasoning, has been absent. Methods: We developed the Evidence Graph Ontology, EVI, in OWL 2, with a set of inference rules, to provide deep representations of supporting and challenging evidence for computations, services, software, data, and results, across arbitrarily deep networks of computations, in connected or fully distinct processes. EVI integrates FAIR practices on data and software, with important concepts from provenance models, and argumentation theory. It extends PROV for additional expressiveness, with support for defeasible reasoning. EVI treats any com- putational result or component of evidence as a defeasible assertion, supported by a DAG of the computations, software, data, and agents that produced it. Results: We have successfully deployed EVI for very-large-scale predictive analytics on clinical time-series data. Every result may reference its own evidence graph as metadata, which can be extended when subsequent computations are executed. Discussion: Evidence graphs support transparency and defeasible reasoning on results. They are first-class computational objects, and reference the datasets and software from which they are derived. They support fully transparent computation, with challenge and support propagation. The EVI approach may be extended to include instruments, animal models, and critical experimental reagents.

scholarly journals A mathematical treatment of defeasible reasoning and its implementation

1992 ◽  
Vol 53 (2-3) ◽  
pp. 125-157 ◽  
Author(s):  
Guillermo R. Simari ◽  
Ronald P. Loui
Keyword(s):  
Defeasible Reasoning ◽  
Mathematical Treatment

Defeasible Reasoning and Moral Dilemmas

1997 ◽  
pp. 205-222 ◽  
Author(s):  
Robert C. Koons ◽  
T. K. Seung
Keyword(s):  
Moral Dilemmas ◽  
Defeasible Reasoning
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