The adoption of algorithms based on Artificial Intelligence (AI) has been rapidly increasing during the last few years. However, some aspects of AI techniques are under heavy scrutiny. For instance, in many use cases, it is not clear whether the decisions of an algorithm are well informed and conforming to human understanding. Having ways to address these concerns is crucial in many domains, especially whenever humans and intelligent (physical or virtual) agents must cooperate in a shared environment. In this paper, we apply an explainability method based on the creation of a Policy Graph (PG) based on discrete predicates that represent and explain a trained agent’s behaviour in a multi-agent cooperative environment. We show that from these policy graphs, policies for surrogate interpretable agents can be automatically generated. These policies can be used to measure the reliability of the explanations enabled by the PGs through a fair behavioural comparison between the original opaque agent and the surrogate one. The contributions of this paper represent the first use case of policy graphs in the context of explaining agent behaviour in cooperative multi-agent scenarios and present experimental results that sets this kind of scenario apart from previous implementations in single-agent scenarios: when requiring cooperative behaviour, predicates that allow representing observations about the other agents are crucial to replicate the opaque agent’s behaviour and increase the reliability of explanations.

In this paper we propose several possible modifications to the OAC-triclustering algorithms based on the prime operators. This method based on the framework of Formal Concept Analysis showed some rather promising results in the previous research. But while it is fast and efficient with respect to such measures as average density of the output, diversity, coverage, and noise- tolerance, it produces rather large number of triclusters. This makes it almost impossible for the expert to manually check the results. We show that the proposed post-processing techniques not only reduce the size of the output for this approach and keep the good values for the measures, but also keep the time complexity of the original algorithm

As the popularity of the field of big data continues to rise, the problem of the development of efficient algorithms with low time complexity and that ability to be parallelized is more and more frequently posed. This work is aimed at the development of an efficient single-pass algorithm for the triclustering of binary data that is suitable for use in the field of big data. As a result, a single-pass serial online OAC triclustering algorithm (triclustering of object-attribute-condition) was obtained. This algorithm not only has a very low complexity in time and memory, but it also can be effectively parallelized.

Studying misinformation and how to deal with unhealthy behaviours within online discussions has recently become an important field of research within social studies. With the rapid development of social media, and the increasing amount of available information and sources, rigorous manual analysis of such discourses has become unfeasible. Many approaches tackle the issue by studying the semantic and syntactic properties of discussions following a supervised approach, for example using natural language processing on a dataset labeled for abusive, fake or bot-generated content. Solutions based on the existence of a ground truth are limited to those domains which may have ground truth. However, within the context of misinformation, it may be difficult or even impossible to assign labels to instances. In this context, we consider the use of temporal dynamic patterns as an indicator of discussion health. Working in a domain for which ground truth was unavailable at the time (early COVID-19 pandemic discussions) we explore the characterization of discussions based on the the volume and time of contributions. First we explore the types of discussions in an unsupervised manner, and then characterize these types using the concept of ephemerality, which we formalize. In the end, we discuss the potential use of our ephemerality definition for labeling online discourses based on how desirable, healthy and constructive they are.