Computational Law with synthetic epistemology

Joseph Dimos

Abstract

With recent developments in computational law, that which comprise updates in AI that apply to belief change and the refining of judgment in critical cases, the utilization of a revised AI toolkit is necessitated. The approach of this endeavor encompasses a Bayesian method for inferring causal connections from data sources that have notable features. The defining of a belief revision operator that is evaluated in conjunction with differences in temporality in events, thus can underpin the structure of rules in legal conventions. The approach of this talk is to introduce soundness upon a belief base that corresponds to derived time parameters. The application of belief revision (AGM) that is adjoint to a characterization of axiom of choice for representing differing states of a belief base is certain direction. The denoting of a case to include the efforts at PennSONG that align with the normative structure of belief change thus is a means to infer Alchourrón CE and ‘hierarchies of regulations and their logic’. To offer an extension to Governatori’s work on annulments and defeasible logic, however is an approach to consider that binds with abrogation and the constructing of rule format. Throughout, there will exist an algorithmic presentation that attributes to belief change operators over a classification schema in the setting of discourse analysis.