Published: 30th September 2023
DOI: 10.4204/EPTCS.391
ISSN: 2075-2180


Proceedings of the Third Workshop on
Agents and Robots for reliable Engineered Autonomy
Krakow, Poland, 1st October 2023

Edited by: Angelo Ferrando and Rafael Cardoso

Angelo Ferrando and Rafael C. Cardoso
Invited Talk: Talking Agents in the Virtual World
Viviana Mascardi
Invited Talk: Model-Based Reasoning under Uncertainty for Reliable Robot Mission Planning
Bruno Lacerda
Online Proactive Multi-Task Assignment with Resource Availability Anticipation
Déborah Conforto Nedelmann, Jérôme Lacan and Caroline Chanel
From Robot Self-Localization to Global-Localization: An RSSI Based Approach.
Athanasios Lentzas and Dimitris Vrakas
Safe and Robust Robot Behavior Planning via Constraint Programming
Jan Vermaelen and Tom Holvoet
Reasoning about Intuitionistic Computation Tree Logic
Davide Catta, Vadim Malvone and Aniello Murano
Runtime Verification for Trustworthy Computing
Robert Abela, Christian Colombo, Axel Curmi, Mattea Fenech, Mark Vella and Angelo Ferrando
The Impact of Strategies and Information in Model Checking for Multi-Agent Systems
Vadim Malvone
Adaptive Application Behaviour for Robot Swarms using Mixed-Criticality
Sven Signer and Ian Gray
Autonomous Systems' Safety Cases for use in UK Nuclear Environments
Christopher R. Anderson and Louise A. Dennis
Rollout Heuristics for Online Stochastic Contingent Planning
Oded Blumenthal and Guy Shani
Evaluating Heuristic Search Algorithms in Pathfinding: A Comprehensive Study on Performance Metrics and Domain Parameters
Aya Kherrour, Marco Robol, Marco Roveri and Paolo Giorgini
Multi-Robot Task Planning to Secure Human Group Progress
Roland Godet, Charles Lesire and Arthur Bit-Monnot
ORTAC+ : A User Friendly Domain Specific Language for Multi-Agent Mission Planning
Caroline Bonhomme and Jean-Louis Dufour


This volume encompasses the proceedings of the Third Workshop on Agents and Robots for reliable Engineered Autonomy (AREA 2023), co-located with the 26th European Conference on Artificial Intelligence (ECAI 2023).

The realm of autonomous agents, extensively studied for decades, has been a focal point from both design and implementation standpoints. Nevertheless, the practical utilization of agents in real-world scenarios has predominantly been within software-centric applications, with limited adoption in situations necessitating physical interactions. Concurrently, the utility of robots has transcended narrow industrial contexts and has expanded across various domains. These domains span from robotic assistants to search and rescue operations, wherein the operational context is dynamic and not fully specified. This context often involves intricate interactions between multiple robots and humans.

These circumstances pose notable challenges to conventional software engineering methods. Enhanced autonomy stands as a pivotal avenue for enabling effective functioning of robotic applications in such settings. Autonomous agents and multi-agent systems emerge as promising methodologies for their development. As the levels of autonomy and interaction escalate, ensuring reliable behavior becomes increasingly intricate, not only in robotic applications but also in conventional autonomous agent scenarios. Hence, there exists a demand for research into novel verification and validation approaches that can be seamlessly integrated into the developmental life cycle of these systems.

The primary objective of this workshop is to facilitate collaboration between researchers in the fields of autonomous agents and robotics. By amalgamating insights from these domains, innovative solutions could potentially emerge to tackle intricate challenges associated with the verification and validation of autonomous robotic systems.

In this third iteration of the workshop, a total of 12 submissions were received, of which 7 full papers and 5 short papers were accepted. We extend our gratitude to all authors who contributed their valuable work to the workshop.

Finally, we would like to thank our invited speakers, Viviana Mascardi and Bruno Lacerda. The title and abstract of their presentations can be found below.

We also express our appreciation to the 25 program committee members (the complete list is available below) for their valuable feedback, which contributed to the refinement of the papers. Our thanks also extend to the EPTCS staff for their support in compiling these proceedings.

For more information about the workshop, please check our website:

The AREA 2023 organisers,
Angelo Ferrando and Rafael C. Cardoso

Program Committee

Talking Agents in the Virtual World

Viviana Mascardi (University of Genoa)

What do chatbots and the metaverse have to do with reliability, cognitive agents, and robotic applications? In this talk I will explore the intriguing connections among them, presenting a small scale prototype of (reliable) "talking agents in the virtual world".

Model-Based Reasoning under Uncertainty for Reliable Robot Mission Planning

Bruno Lacerda (University of Oxford)

In this presentation, I will argue that the synergy between three factors is critical for creating reliable mission planning algorithms for autonomous robots operating in uncontrolled environments. These factors are (i) utilising historical data gathered online to enhance decision-making under uncertainty models, (ii) implementing principled planning techniques that explicitly reason about the epistemic uncertainty inherent to these data-driven models, and (iii) incorporating rich specifications that go beyond typical expected reward maximisation problems. Developing frameworks that unify these three factors is an open problem in the field of robotics, which is heavily dependent on the specific application domain. I will offer an overview of various works undertaken at the GOALS lab at the Oxford Robotics Institute that consider these three factors in distinct ways. Moreover, I will describe how these works provide a foundation for creating integrated approaches that enable long-term deployment of robots capable of acquiring parametrised environmental models from historical data, plan considering the epistemic uncertainty of such models, and effectively adapt their behaviour to in-mission observations, continuously refining their estimate over the epistemic uncertainty online.