Effective document retrieval using domain knowledge and semantics is one of the major challenges in Information Retrieval. Over the last years, there has been a growing interest in ontologies as an artifact for human knowledge representation and a critical component…

The knowledge imperfections should be considered when modeling complex problems. A solution is to develop a model that reduces the complexity and another option is to represent the imperfections: uncertainty, vagueness and incompleteness in the knowledge base. This paper proposes…

In the last decade several ways to formalize defeasible reasoning have been studied. A particular approach, defeasible argumentation, has been particularly successful to achieve this goal.
The inference process of argument-based systems is based on the interaction of ar- guments…

In systems where agents are required to interact with a partially known and dynamic world, sensors can be used to obtain further knowledge about the environment. However, sensors may be unreliable, that is, they may deliver wrong information (due, e.g.,…

The design of intelligent agents is greatly influenced by the many different models that exist to represent knowledge. It is essential that such agents have computationally adequate mechanisms to manage its knowledge, which more often than not is incomplete and/or…

The studies on the formation of psychology's professors were locating us in the construction process of the professional knowledge in professors but in psychologists that exercise the teaching too, visualizing the paper that built knowledge assumed between both environments (academic…

The development of isolated agents involves the manipulation of components such as actions, mental attitudes and decisions. Multi-agent systems have to deal with these components from different agents, without to forget the privacy and autonomy of each agent. For programming…

In this work we present an approach to model an action-oriented system controlled by BDI agents using a defeasible argumentation formalism to represent its knowledge. Here, our main concern will be modelling the software agents that drive the physical robots.

Multiagent systems and online communities rely on rating systems to infer the reputation given to an individual within a particular context. The notion of reputation is essential for helping a given individual to trust in other individuals and for being…

Co-training can learn from datasets having a small number of labelled examples and a large number of unlabelled ones. It is an iterative algorithm where examples labelled in previous iterations are used to improve the classification of examples from the…