Keynotes

Web of Things (WoT)

The Web in Semantic Web - from Linked Open Data to Solid
by Tobias Käfer, Karlsruhe Institute of Technology, Germany

Knowledge Graphs (KG)

Connected Graph Data Spaces in Industry
by Steffen Staab, University of Southampton, UK

Large industrial setups, such as industrial manufacturing or architecture, engineering, & construction (AEC), are characterized by the structuring of processes according to evolving capabilities and responsibilities. We describe two large-scale research projects, the DFG Cluster of Excellence on “Integrated Computational Design and Construction (IntCDC)” and the DFG Collaborative Research Center “Circular Factory”, where such structuring asks for autonomous but connected graph data spaces. In our talk, we describe a range of research efforts we undertake in these contexts, such as (i) management of identities, (ii) intentional forgetting, (iii) schema inference across data and programming spaces, (iv) management of uncertainties and (v) querying for beliefs. We conclude that knowledge representation has developed many versatile methods, but has missed the opportunity to undertake research and development that would put these methods into useful and usable tools.

Multi-Agent Systems (MAS)

Advances in Communication Meaning and The Future of Multiagent Software Research
by Amit Chopra, Lancaster University, UK

About three decades ago, pioneers in multiagent systems envisaged that multiagent abstractions and methodologies would be at the heart of designing intelligent distributed applications. In particular, with the aim of enabling flexible interactions between agents, they emphasized modeling communication meaning. After three decades of work, the program can claim little broad impact. Has the program been a failure?

No! The program has seen some remarkable but underappreciated advances, the biggest being the paradigm of information-based protocols, which finally makes it possible to realize the promise of modeling meaning via norms. I discuss these advances and how they address thorny, longstanding problems in systems, including in fields such as networks, distributed systems, and programming languages. These advances put multiagent abstractions at the heart of software systems research and raise deep and novel research questions.

Now is a great time for students to pursue research on multiagent software abstractions!

Multi-Agent Systems on the Web

Integrating Agent Technologies into Microservices architecture using the MAMS Architectural Style
by Rem Collier, University College Dublin, Ireland

Multi-Agent Systems (MAS) research has led to a number of innovative approaches to coordinating the activities of autonomous entities. Integrating such approaches into modern software systems is often not easy because they require the adoption of an agent-oriented view of the system which is not always realistic in practice. This talk introduces the Multi-Agent MicroServices (MAMS) architectural style, an approach to integrating MAS technologies into Microservices architecture that removes this need for the adoption of an agent-oriented view of the system. The approach is based on Representational State Transfer (REST) and adopts the view of agents as hypermedia entities that maintain a set of web resources. This allows non-agent-based Microservices to be integrated with agent-based Microservices via those resources, which are exposed as REST APIs. Adoption of Linked Data and Semantic Web technologies can further enhance this model, enabling the integration of disparate data services into a distributed knowledge graph that can be easily consumed by agents and other services. Further, the integration of hypermedia controls into representations of resources offer the potential for the creation of next generation self-describing APIs.

Industry

Powering Industry: The Collaborative Industrial AI Ecosystem
by Olivier Riou, Siemens, France

The rise of AI is transforming industry. This presentation explores the Industrial AI ecosystem, where collaboration is key. Businesses, researchers, and tech providers join forces, pooling knowledge, and resources. This fosters innovation and tackles shared challenges. We’ll delve into the drivers: tackling common hurdles with AI, fostering knowledge exchange for next-gen solutions, and exploring new business models. Together, the Industrial AI ecosystem unlocks the full potential of AI for a thriving future.