Abstract
Defeasible reasoning has always been a central interest of researchers in the fields of Artificial Intelligence (AI) and Multi-Agent Systems (MAS). In fact, this kind of reasoning is central to dealing with conflicting knowledge or beliefs that agents may hold without causing inconsistencies. In the context of languages for Knowledge Representation, many formal approaches have been proposed specifically in Description Logics (DLs) to deal with this phenomenon. With a perspective towards human-centred and agentive AI and building on the DL paradigm we pursue an approach informed by results coming from fields such as linguistics, philosophy and cognitive science. A central problem in the general area of defeasible DLs is to give a principled solution to the question of where preferences originate from in order to provide a notion of defeasibility. To address this issue, a core aspect of our approach is to compute preferences from the knowledge presented in a knowledge base (with standard semantics) itself. We thus present a non-monotonic DL based on a combination of ideas from prototype theory, weighted DLs (aka ‘tooth logic’), and earlier work on justifiable exceptions. A central ingredient in the new framework is the notion of a prototype description, i.e. weighted characterisations of concepts based on the typical features of its members. We show that through such descriptions it is possible to compute a typicality score which allows to define a preference order over models, useful to solve conflicts across exceptional instances. We define two principle ways of computing such preferences, discuss some core semantic properties and finally outline a translation into Answer Set Programming.