Elena Yan

As the complexity of software systems rises, explainability - i.e. the ability of systems to provide explanations of their behaviour - becomes a crucial property. This is true for any AI-based systems, including autonomous systems that exhibit decision making capabilities such as multi-agent systems. Although explainability is generally considered useful to increase the level of trust for end-users, we argue it is also an interesting property for software engineers, developers, and designers to debug and validate the system’s behaviour. In this paper, we propose a multi-level explainability framework for BDI agents to generate explanations of a running system from logs at different levels of abstraction, tailored to different users and their needs. We describe the mapping from logs to explanations, and present a prototype tool based on the JaCaMo platform which implements the framework.

In increasingly autonomous and highly distributed multi-agent systems, centralized coordination becomes impractical and raises the need for governance and enforcement mechanisms from an agent-centric perspective. In our conceptual view, sanctioning norm enforcement is part of this agent-centric perspective and they aim at promoting norm compliance while preserving agents’ autonomy. The few works dealing with sanctioning norm enforcement and sanctions from the agent-centric perspective present limitations regarding the representation of sanctions and the comprehensiveness of their norm enforcement process. To address these drawbacks, we propose the NPL(s), an extension of the NPL normative programming language enriched with the representation of norms and sanctions as first-class abstractions. We also propose a BDI normative agent architecture embedding an engine for processing the NPL(s) language and a set of capabilities for approaching more comprehensively the sanctioning norm enforcement process. We apply our contributions in a case study for improving the robustness of agents’ decision-making in a production automation system.

Regulating multi-agent system (MAS) to achieve a balance between the autonomy of agents and the control of the system is still a challenge. Regulation management in MAS has been conceptualized from various perspectives in the literature, whose intersections open up a wide spectrum of design options. We propose a unified view on regulation management in MAS that identifies design options with respect to three perspectives: the regulation capabilities, the multi-agent oriented programming dimensions, and the architectural style. We use our unified view for reviewing and classifying existing MAS frameworks in the literature, highlighting the dominant and underexplored views on regulation management in MAS.

In Multi-Agent Systems (MAS), the regulation of agents aims to define a balance between the control of the system and the agents’ autonomy. The ability of a MAS to adapt its regulations at run-time is an important feature that enables it to be flexible to changing situations. There is no unique approach to designing such ability. In this paper, we discuss the different options along the multi-agent oriented programming dimensions, i.e., agent, environment, interaction, and organization. We show that regulation adaptation can be managed within a single dimension or distributed in multiple dimensions. We use a case study in the manufacturing system domain to motivate the regulation adaptation in each of these dimensions.