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Roberto Casadei

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4 papers

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AAMAS Conference 2026 Conference Paper

Leveraging Context-Oriented Programming to Implement Normative Rules in Autonomous Systems

Roberto Casadei
Martina De Sanctis
Gianluca Filippone
Sara Pettinari
Gian Luca Scoccia
Nicolas Troquard

Theincreasingsensitivitytohumanaspectsinautonomoussystems engineering calls for principled approaches to embed normative concerns into their behaviour. Recent research has focused on expressing and validating sets of social, legal, ethical, empathetic, and cultural (SLEEC) concerns as rules, and on verifying that a system design adheres to them. However, to date, there is limited work related to the actual implementation and actuation of SLEECaware behaviours. Yet, we believe that operationalising SLEEC rules can enable responsible behaviour of autonomous systems and advance research on the topic. For this purpose, we propose an operational solution for ethical-aware autonomous systems. Specifically, we devise a principled approach, which we call CO-SLEEC (Context-Oriented SLEEC), connecting the normative setting of SLEEC rules to context-oriented programming (COP). CO-SLEEC promotes runtime adaptation and exhibition of context-dependent ethical behaviour through a modular and transparent design.

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AAMAS Conference 2026 Conference Paper

Macro-Programming Multi-Agent Systems: A Framework for Artificial Collective Intelligence

Roberto Casadei

This paper elaborates on the opportunity and idea of multi-agent system (MAS) macro-programming, i. e. , programming in terms of macroscopic denotations of system goals, structure, or behaviour. Indeed, macro-descriptions with explicit macro-to-micro mapping can be a formidable way to harness the complexity of emergent collective behaviour (for humans) and to provide a structure for guiding optimisation, learning, and generative artificial intelligence (AI) processes (for computers). Despite contributions about meso-level (e. g. , organisational), multi-level (e. g. , holonic) and declarative (e. g. , goal-oriented, normative) paradigms exist in the MAS literature, research is fragmented and the topic arguably overlooked by both the scientific and software engineering viewpoints. Recent survey works on macro-programming spanning areas from sensor networkstoswarmroboticssuggestthatasynthesisispossible, despite the variety of methods, techniques, and abstractions. With reference to early and recent literature both within and outside the MAS community, this paper motivates that multi-scale and especially macroscopic descriptions are possible, useful, and timely—opening up to research opportunities and community debate.

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TAAS Journal 2022 Journal Article

A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems

Danilo Pianini
Federico Pettinari
Roberto Casadei
Lukas Esterle

We focus on the online multi-object k -coverage problem (OMOkC), where mobile robots are required to sense a mobile target from k diverse points of view, coordinating themselves in a scalable and possibly decentralised way. There is active research on OMOkC, particularly in the design of decentralised algorithms for solving it. We propose a new take on the issue: Rather than classically developing new algorithms, we apply a macro-level paradigm, called aggregate computing, specifically designed to directly program the global behaviour of a whole ensemble of devices at once. To understand the potential of the application of aggregate computing to OMOkC, we extend the Alchemist simulator (supporting aggregate computing natively) with a novel toolchain component supporting the simulation of mobile robots. This way, we build a software engineering toolchain comprising language and simulation tooling for addressing OMOkC. Finally, we exercise our approach and related toolchain by introducing new algorithms for OMOkC; we show that they can be expressed concisely, reuse existing software components and perform better than the current state-of-the-art in terms of coverage over time and number of objects covered overall.

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EAAI Journal 2021 Journal Article

Engineering collective intelligence at the edge with aggregate processes

Roberto Casadei
Mirko Viroli
Giorgio Audrito
Danilo Pianini
Ferruccio Damiani

Edge computing promotes the execution of complex computational processes without the cloud, i. e. , on top of the heterogeneous, articulated, and possibly mobile systems composed of IoT and edge devices. Such a pervasive smart fabric augments our environment with computing and networking capabilities. This leads to a complex and dynamic ecosystem of devices that should not only exhibit individual intelligence but also collective intelligence—the ability to take group decisions or process knowledge among autonomous units of a distributed environment. Self-adaptation and self-organisation mechanisms are also typically required to ensure continuous and inherent toleration of changes of various kinds, to distribution of devices, energy available, computational load, as well as faults. To achieve this behaviour in a massively distributed setting like edge computing demands, we seek for identifying proper abstractions, and engineering tools therefore, to smoothly capture collective behaviour, adaptivity, and dynamic injection and execution of concurrent distributed activities. Accordingly, we elaborate on a notion of “aggregate process” as a concurrent collective computation whose execution and interactions are sustained by a dynamic team of devices, whose spatial region can opportunistically vary over time. We ground this notion by extending the aggregate computing model and toolchain with new constructs to instantiate aggregate processes and regulate key aspects of their lifecycle. By virtue of an open-source implementation in the ScaFi framework, we show basic programming examples as well as case studies of edge computing, evaluated by simulation in realistic settings.

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