Constrained Derivation in Assumption-Based Argumentation

Abstract

Structured argumentation formalisms provide a rich framework to formalise and reason over situations where contradicting information is present. However, in most formalisms the integral step of constructing all possible arguments is performed in an unconstrained way. For this, it may not be possible to represent situations where the reasoning process is subject to various kinds of restrictions; for example, where the possibility of communication is limited in a multi-agent setting. In this work, we introduce a general approach that allows constraining the derivation of arguments for assumption-based argumentation. We show that, under certain conditions, this reduces to eliminating rules from the given knowledge base while letting the derivation of arguments unconstrained. For this as well as for the general approach to derivation constraining, we provide an encoding into Answer Set Programming.