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Pedro Ribeiro

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3 papers

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FM Conference 2026 Conference Paper

The SLEEC Framework for Normative Requirements Engineering

Pedro Ribeiro
Radu Calinescu
Ana Cavalcanti
Marsha Chechik
Sinem Getir Yaman
Lina Marsso
Isobel Standen
Beverley Townsend

Abstract Autonomous agents are increasingly deployed in sensitive, human-centric domains—such as healthcare, assistive care, and emergency response—where their decision-making must align with complex human norms. These translate into Social, Legal, Ethical, Empathetic, and Cultural (SLEEC) requirements that are often nuanced and context-dependent, challenging traditional software engineering paradigms. Our tutorial paper presents a comprehensive, tool-supported methodology for managing the SLEEC requirements lifecycle, covering elicitation, well-formedness validation, and conformance verification of software design models against SLEEC requirements. We demonstrate the use of our methodology and associated tools through application to a robot-assisted dressing system, providing a guide for researchers and engineers to bridge the gap between abstract human norms and verifiable system designs.

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TCS Journal 2019 Journal Article

Angelic processes for CSP via the UTP

Pedro Ribeiro
Ana Cavalcanti

Demonic and angelic nondeterminism play fundamental roles as abstraction mechanisms for formal modelling. In contrast with its demonic counterpart, in an angelic choice failure is avoided whenever possible. Although it has been extensively studied in refinement calculi, in the context of process algebras, and of the Communicating Sequential Processes (CSP) algebra for refinement, in particular, it has been elusive. We show here that a semantics for an extended version of CSP that includes both demonic and angelic choice can be provided using Hoare and He's Unifying Theories of Programming (UTP). Since CSP is given semantics in the UTP via reactive designs (pre and postcondition pairs) we have developed a theory of angelic designs and a conservative extension of the CSP theory using reactive angelic designs. To characterise angelic nondeterminism appropriately in an algebra of processes, however, a notion of divergence that can undo the history of events needs to be considered. Taking this view, we present a model for CSP where angelic choice completely avoids divergence just like in the refinement calculi for sequential programs.

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IJCAI Conference 2018 Conference Paper

TensorCast: Forecasting Time-Evolving Networks with Contextual Information

Miguel Araújo
Pedro Ribeiro
Christos Faloutsos

Can we forecast future connections in a social network? Can we predict who will start using a given hashtag in Twitter, leveraging contextual information such as who follows or retweets whom to improve our predictions? In this paper we present an abridged report of TensorCast, an award winning method for forecasting time-evolving networks, that uses coupled tensors to incorporate multiple information sources. TensorCast is scalable (linearithmic on the number of connections), effective (more precise than competing methods) and general (applicable to any data source representable by a tensor). We also showcase our method when applied to forecast two large scale heterogeneous real world temporal networks, namely Twitter and DBLP.

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