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 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.

In multi-agent systems (MAS), agents can be governed by regulations. Due to an ever-evolving set of exogenous or endogenous changes, the ability of MAS to adapt regulations becomes crucial. In the MAS literature, there is a lack of comprehensive works defining models to adapt regulations. We propose a general regulation adaptation model for MAS that defines regulation adaptation representations (i.e., detect-fact, design-fact, and execute-fact) and regulation adaptation capabilities (i.e., detect, design, and execute). We also propose a method that uses constitutive and regulative norms together with the adaptation representations to govern the execution of the regulation adaptation capabilities. We illustrate the feasibility of our model by extending the SAI and NPL(s) normative engines to support regulation adaptation and integrating them into a normative agent architecture in the JaCaMo framework.