Theme:


Autonomous Systems (AS), such as robots, self-driving cars, and body sensor networks, whether enabled with Artificial Intelligence (AI) or not, are increasingly pervasive in our daily lives. They are rapidly transforming domains such as transportation, manufacturing, the built environment, and healthcare. Their effectiveness depends heavily on Information Systems (IS), which provide the information, infrastructures, and coordination mechanisms that enable autonomy.

In this context, AS act and generate massive streams of real-time data (sensor readings, environmental feedback, performance metrics). IS process and analyze this data, supporting decisions for both AS and human operators or users. Together, AS and IS form a continuous feedback loop where the AS act and produce data, and IS interpret the data to provide feedback to enable AS adaptation. Notably, this loop is more and more embedded within socio-technical systems, where humans and technology continuously interact to fulfill stakeholder requirements. Furthermore, AI is increasingly integrated within such loops, offering new solutions for autonomy and automation. This tight coupling, along with the proliferation of Large Language Models (LLMs) and Large Multimodal Models (LMMs), raises new challenges, such as ensuring trustworthy data governance, protecting systems from cyber threats, ensuring sustainable goals, addressing ethical concerns in decision-making, and aligning autonomy with organizational and social contexts.

This workshop aims to explore the synergy of AS and IS, focusing on how autonomy and information processes can be jointly designed, integrated, and governed in complex environments. We aim to bring together diverse research communities, including autonomous systems, information systems, artificial intelligence, software engineering, and business process management, to foster a holistic understanding of the challenges and opportunities of AS-IS connection.


The main topics relevant to the EASI workshop include, but are not limited to:

- Engineering IS for autonomous systems and autonomy-enabled IS

- Human-AS interaction and cooperation

- Evolution of AS in changing environments

- Applications of AI for AS

- LLM/LMM-driven autonomy in information systems

- Security and privacy in autonomous LLM/LMM-based systems

- Management of autonomous processes

- System automation and self-adaptation

- Process-aware architectures and adaptive workflows for AS

- Process mining and monitoring for autonomous operations

- Predictive and prescriptive approaches for AS

- Compliance and conformance of AS behavior

- Robustness of AS-IS (fault, data uncertainty, rule enactment prioritization)

- Explainability, trust, sustainability, and ethical considerations

- Case studies and applications of AS in domains such as healthcare, mobility, smart homes, industrial IoT, and space

- Lessons learned from implementing AS-IS systems