Nowadays, Multi-Agent Systems (MAS) are broadening their applications to open environments, where heterogeneous agents could enter into the system, form agents’ organizations and interact. The high dynamism of open MAS gives rise to potential conflicts between agents and thus, to a need for a mechanism to reach agreements. Argumentation is a natural way of harmonizing conflicts of opinion that has been applied to many disciplines, such as Case-Based Reasoning (CBR) and MAS. Some approaches that apply CBR to manage argumentation in MAS have been proposed in the literature. These improve agents’ argumentation skills by allowing them to reason and learn from experiences. In this paper, we have reviewed these approaches and identified the current contributions of the CBR methodology in this area. As a result of this work, we have proposed several open issues that must be taken into consideration to develop a CBR framework that provides the agents of an open MAS with arguing and learning capabilities.

The automatic assessment of students’ free-text answers has recently received much attention, due to the necessity of exploring and taking advantage of new and more complex computer-based assessment methods. In this paper, a review of the state-of-art of the field is presented, focusing on the techniques that underpin these systems and their evaluation metrics. Although there is still a long way to go so as to reach the ideal system, the fact that the existing systems are already being used commercially and as a second opinion in exams such as GMAT proves the uptake of this field.

This paper will summarize and analyze the work of the different research groups who have recently made significant contributions in using Reinforcement Learning techniques to learn dialogue strategies for Spoken Dialogue Systems (SDSs). This use of stochastic planning and learning has become an important research area in the past 10 years, since it promises automatic data-driven optimization of the behavior of SDSs that were previously hand-coded by expert developers. We survey the most important developments in the field, compare and contrast the different approaches, and describe current open problems.