Symbolic logic framework for situational awareness in mixed autonomy

Projektinformationen

SymAware

ID Finanzhilfevereinbarung: 101070802

DOI

10.3030/101070802

Projekt abgeschlossen

EK-Unterschriftsdatum 22 Juni 2022

Startdatum 1 Oktober 2022

Enddatum 31 März 2026

Finanziert unter

• The European Innovation Council (EIC)

Gesamtkosten

€ 3 980 291,25

EU-Beitrag

€ 3 980 291,00

Koordiniert durch

MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV
Germany

Periodic Reporting for period 2 - SymAware (Symbolic logic framework for situational awareness in mixed autonomy)

Berichtszeitraum: 2023-10-01 bis 2026-03-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Modern society is increasingly relying on autonomous systems such as self-driving vehicles, drones, and collaborative robots. These systems are expected to operate safely alongside humans in complex, dynamic environments. However, a major challenge remains: current autonomous systems lack a robust and transparent form of situational awareness. They often struggle to understand uncertain environments, anticipate risks, and communicate their intentions effectively to human users. This creates barriers to trust, safety, and large-scale deployment.

The SymAware project addresses this challenge by developing a novel framework for "awareness inside" autonomous systems. The project introduces a fundamentally new approach based on symbolic logic and formal reasoning, enabling autonomous agents to represent, reason about, and communicate different types of awareness, including knowledge, space and time, risk, and social interaction. This approach goes beyond traditional data-driven methods by providing interpretable and verifiable models of decision-making, which are essential for safety-critical applications.

The overall objective of SymAware is to enable safe and trustworthy collaboration between humans and autonomous agents in mixed-autonomy environments. To achieve this, the project develops methods that allow agents to: (i) reason about their environment and tasks using formal logic; (ii) quantify and manage risks under uncertainty; (iii) adapt to dynamic situations through spatiotemporal reasoning; and (iv) communicate their internal state and intentions in a way that is understandable to humans. In addition, the project places strong emphasis on ethical and human-centered design, ensuring that transparency, fairness, and user trust are embedded into the technology.

The pathway to impact is based on integrating these capabilities into a unified, open and reusable software framework, validated through real-world inspired use cases in aviation and automotive domains. These include drone traffic management in urban environments and advanced autonomous driving scenarios. By demonstrating how aware systems can improve safety, reduce risks, and enhance human-machine interaction, the project contributes to the development of next-generation autonomous technologies.

SymAware aligns with European strategic priorities on trustworthy artificial intelligence, safe automation, and human-centric digital transformation. It also integrates insights from reseach on human-robot interactions and human factors, particularly in understanding user trust, risk perception, and interaction with autonomous systems. This interdisciplinary approach ensures that the developed technologies are not only technically advanced but also socially acceptable for real-world adoption.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

We are providing a condensed overview of the tasks completed and key accomplishments achieved throughout this reporting period (RP). These accomplishments pertain to research and development efforts. The content is structured according to work packages, tasks, and the teams involved. Additionally, a detailed overview of the work performed and main achievements is available in Deliverable D8.3.

The primary objective of WP1 is to create a foundation for situational awareness in multi-agent systems (MASs). This involves developing a framework to represent awareness mathematically and a toolkit for building MASs with this awareness. It will also act as an integration hub, gathering input from other work packages. The logical characterization of awareness will be done using temporal logic. KTH in collaboration with other partners have provided different modeling techniques of MASs. Drawing inspiration from the existing framework of awareness (from Deliverable 5.1) within this project, we have already developed the first framework for awareness in MAS. In the first version of the proposed architecture, the situational awareness includes elements related to the system's situational awareness, encompassing aspects such as the system's state, intent, uncertainty, and risk. Furthermore, the framework in question combines various components, including knowledge, perception, communication, planning, control, the physical condition of the system, and interfaces for communication with human agents. MPI-SWS is leading the development of the tool which will implement the proposed architecture. The first version of the tool is already prepared and made public through GitLab.


The SymAware project developed and validated a comprehensive framework for situational awareness in mixed-autonomy systems, combining symbolic logic, formal verification, and data-driven methods to enable safe and trustworthy human–agent interaction.

In WP1, a foundational framework and open-source toolbox for situational awareness in multi-agent systems were established. The modular architecture integrates perception, communication, reasoning, and control, and supports symbolic verification and controller synthesis. WP2 introduced methods for spatiotemporal awareness using formal languages, enabling scalable task decomposition, distributed control, and adaptive planning under uncertainty. WP3 developed approaches for risk-aware decision-making, including probabilistic risk assessment and user-centered risk communication. WP4 advanced knowledge-aware systems by combining symbolic representations with learning-based methods and formal guarantees. The developed methods were validated in WP5 through aviation and automotive use cases, demonstrating improved safety and performance in avionics traffic management and autonomous driving scenarios. WP6 addressed ethical and human-centered aspects, including transparency, trust, and human oversight.

Across all work packages, the project achieved strong scientific outcomes, including numerous peer-reviewed publications and validated methodologies. The developed framework provides formal guarantees on safety, robustness, and task satisfaction, while remaining applicable to real-world scenarios.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

The SymAware project delivers significant advances beyond the current state of the art in autonomous systems by introducing a fundamentally new approach to situational awareness. While existing systems primarily rely on data-driven or reactive methods, SymAware combines symbolic logic, formal verification, and data-driven techniques into a unified framework that enables autonomous agents to reason, explain, and act with awareness in complex and uncertain environments.

A key innovation of the project is the development of a formal, logic-based framework for awareness, integrating multiple dimensions such as spatiotemporal reasoning, risk awareness, and knowledge representation. This allows autonomous systems not only to make decisions but also to provide verifiable guarantees on safety, robustness, and task completion. Such guarantees are largely absent in current approaches, especially in multi-agent and human-interactive settings.

Another major advancement is the ability to quantify and manage uncertainty and risk in a principled way. In addition, SymAware bridges the gap between internal system reasoning and human understanding by developing mechanisms for communicating risk, intentions, and system confidence. Importantly, SymAware integrates human-centered and ethical considerations directly into the technical design. This interdisciplinary integration represents a step forward compared to existing technical solutions that often overlook these aspects.