Uncertainty often pervades information and knowledge. For this reason, the handling of
uncertainty in inference systems has been an issue for a long time in artificial intelligence …

Collaborative decision making problems can be addressed through argumentative
discourse and collaboration among the users involved. Consensus is achieved through the …

A semantic network is a graph of the structure of meaning. This article introduces semantic
network systems and their importance in Artificial Intelligence, followed by I. the early …

The human brain is a dynamical system whose extremely complex sensor-driven neural
processes give rise to conceptual, logical cognition. Understanding the interplay between …

The paper presents a connectionist framework that is capable of representing and learning
propositional knowledge. An extended version of propositional calculus is developed and is …

Research on integrated neural-symbolic systems has made significant progress in the
recent past. In particular the understanding of ways to deal with symbolic knowledge within …

We present a new approach to deal with default information based on the theory of belief
functions. Our semantic structures, inspired by Adams' epsilon semantics, are epsilon-belief …

Connectionist networks with symmetric weights (like Hopfield networks and Boltzmann
Machines) use gradient descent to find a minimum for quadratic energy functions. We show …

This paper presents a new data classification approach which is based on the one hand on
deep learning neural networks for effectively extracting well defined categorical information …

Penalty logic, introduced by Pinkas [17], associates to each formula of a knowledge base the
price to pay if this formula is violated. Penalties may be used as a criterion for selecting …