Defeasible argumentation for multi-agent planning in ambient intelligence applications

This contribution presents a practical extension of a theoretical model for multi-agent planning based upon DeLP, an argumentation-based defeasible logic. Our framework, named DeLP-MAPOP, is implemented on a platform for open multi-agent systems and has been experimentally tested, among others, in applications of ambient intelligence in the field of health-care. DeLP-MAPOP is based on a multi-agent partial order planning paradigm in which agents have diverse abilities, use an argumentation-based defeasible reasoning to support their own beliefs and refute the beliefs of the others according to their knowledge during the plan search process. The requirements of Ambient Intelligence (AmI) environments featured by the imperfect nature of the context information and heterogeneity of the involved agents make defeasible argumentation be an ideal approach to resolve potential conflicts caused by the contradictory information coming from the ambient agents. Moreover, the ability of AmI systems to build a course of action to achieve the user’s needs is also a claiming capability in such systems. DeLPMAPOP shows to be an adequate approach to tackle AmI problems as it gathers together in a single framework the ability of planning while it allows agents to put forward arguments that support or argue upon the accuracy, unambiguity and reliability of the contextaware information.