In this chapter we take as our reference twenty-five years of scientific and technical results presented at the Workshop on Objects, and explore the development of rational agents and integration with machine learning (ML) techniques, discussing their transition from pure symbolic to subsymbolic and neurosymbolic systems. Given the growing importance of combining rational agent reasoning with ML, we first outline the current state of technology by highlighting key milestones and breakthroughs. Pinpointing logics and logic programming as the main foundational tool for the design and implementation of rational agents, we discuss successful implementations and applications of logic-based agents, then we identify some of the main integration strands of subsymbolic techniques within rational agents. In particular, we focus on symbolic knowledge injection (SKI) and symbolic knowledge extraction (SKE) as some of the most relevant neurosymbolic techniques, and on their impact on intelligent agents and multi-agent systems (MASs). Current gaps and challenges in the integration of rational agents with ML are finally discussed along with future research directions.

In the context of Information-Centric Networking, Interest Flooding Attacks (IFAs) represent a new and dangerous sort of distributed denial of service. Since existing proposals targeting IFAs mainly focus on local information, in this paper we propose GNN4IFA as the first mechanism exploiting complex non-local knowledge for IFA detection by leveraging Graph Neural Networks (GNNs) handling the overall network topology.In order to test GNN4IFA, we collect SPOTIFAI, a novel dataset filling the current lack of available IFA datasets by covering a variety of IFA setups, including ~40 heterogeneous scenarios over three network topologies. We show that GNN4IFA performs well on all tested topologies and setups, reaching over 99% detection rate along with a negligible false positive rate and small computational costs. Overall, GNN4IFA overcomes state-of-the-art detection mechanisms both in terms of raw detection and flexibility, and - unlike all previous solutions in the literature - also enables the transfer of its detection on network topologies different from the one used in its design phase.

Although popular and effective, large language models (LLM) are characterised by a performance vs. transparency trade-off that hinders their applicability to sensitive scenarios. This is the main reason behind many approaches focusing on local post-hoc explanations recently proposed by the XAI community. However, to the best of our knowledge, a thorough comparison among available explainability techniques is currently missing, mainly for the lack of a general metric to measure their benefits. We compare state-of-the-art local post-hoc explanation mechanisms for models trained over moral value classification tasks based on a measure of correlation. By relying on a novel framework for comparing global impact scores, our experiments show how most local post-hoc explainers are loosely correlated, and highlight huge discrepancies in their results—their “quarrel” about explanations. Finally, we compare the impact scores distribution obtained from each local post-hoc explainer with human-made dictionaries, and point out that there is no correlation between explanation outputs and the concepts humans consider as salient.