A solution of problems in multiagent systems involves representing beliefs of agents immersed in dynamic environments. Observation-based Defeasible Logic Programming (ODeLP) is an argument-based logic programming language that is used to represent an agent’s knowledge in the context of a…

In this research line we are exploring the possibility of having multiple preference criteria to compare arguments in in Defeasible Argumentation. In this work we are assuming a goaloriented Argumentative System, to which an application or an agent may seek…

The field of machine learning (ML) is concerned with the question of how to construct algorithms that automatically improve with experience. In recent years many successful ML applications have been developed, such as datamining programs, information-filtering systems, etc. Although ML…

The aim of this work is to study an argumentation-based formalism that an agent could use for constructing plans. Elsewhere we have introduced a formalism for agents to represent knowledge about their environment in Defeasible Logic Programming, and a set…