We propose modeling different policies for behaviour diffusion in a network of agents considering conflict and dependency. In approaches based on the threshold-limited influence model, an agent adopts a behaviour if it has received enough influence from its social connections to surpass a given behaviour threshold. We also consider models where adopting a behaviour might depend on other behaviours and where behaviours might conflict. We assume a network with a fixed structure and undirected and same-weight connections between agents, i.e., a network where, at least in principle, any two agents have the same capacity to influence each other. We propose a minimal propositional dynamic logic language for reasoning about each diffusion policy, and we provide a sound and complete axiomatization for it.

AGM's belief revision is one of the main paradigms in the study of belief change operations. Despite its popularity and importance to the area, it is well recognised that AGM's work relies on a strong idealisation of the agent's capabilities and the nature of beliefs themselves. Particularly, it is recognised in the literature that Belief and Knowledge are hyperintensional attitudes, i.e. they can differentiate between contents that are necessarily equivalent, but to our knowledge, only a few works have explicitly considered how hyperintensionality affects belief change. This work investigates abstract operations of hyperintensional belief change and their connection to belief change in non-classical logics, such as belief contraction operations for Horn Logics and Description Logics. Our work points to hyperintensional belief change as a general framework to unify results in belief change for non-classical logics.

AGM’s belief revision is one of the main paradigms in the study of belief change operations. In this context, belief bases (prioritised bases) have been largely used to specify the agent’s belief state - whether representing the agent’s ‘explicit beliefs’ or as a computational model for her belief state. While the connection of iterated AGM-like operations and their encoding in dynamic epistemic logics have been studied before, few works considered how well-known postulates from iterated belief revision theory can be characterised by means of belief bases and their counterpart in dynamic epistemic logic. This work investigates how priority graphs, a syntactic representation of preference relations deeply connected to prioritised bases, can be used to characterise belief change operators, focusing on well-known postulates of Iterated Belief Change. We provide syntactic representations of belief change operators in a dynamic context, as well as new negative results regarding the possibility of representing an iterated belief revision operation using transformations on priority graphs.

The ability to follow learning progress and students' performance can be beneficial to teachers and researchers in modern educational systems actions, especially in e-learning environments. In this paper, an effort is made to investigate and utilize the learning features sets from the learning objectives and students' interactions in Learning Management Systems to follow the student's performance. This research aims to implement a software architecture called Student Academic Performance Evaluation System (SapeS) based on Learning Analytics and Learning Objectives to support the teacher in the evaluation process. Outcomes provided by SapeS should help educators in the promotion and improvement in the learning process.