Reinstatement and the requirement of maximal specificity in argument systems

BODANZA, GUSTAVO ADRIÁN; ALESSIO, CLAUDIO ANDRÉS

LECTURE NOTES IN COMPUTER SCIENCE

Springer

Año: 2014 vol. 8652 p. 81 - 93

0302-9743

An argument is reinstated when all its defeaters are in turn ultimately defeated. This is a kind of principle governing most argument systems in AI. Nevertheless, some criticisms to this principle have been offered in the literature. Assuming that reinstatement is prima facie acceptable, we analyze some counterexamples in order to identify common causes. As a result, we found that the problem arises when arguments in a chain of attacks are related by specificity. We argue that the reason is that non-maximally specific arguments can be reinstated originating fallacious justifications. Following old intuitions by Carl Hempel about inductive explanations, we propose a requirement of maximal specificity on defeasible arguments and introduce ?undermining defeaters? which, in essence, facilitate the rejection of those arguments which do not satisfy the requirement. This ideas are formally defined using the DeLP system for defeasible logic programming.