Multi-agent Collaborative AI

WP1 covers 4 use cases:

In the domain of industry: 
Smart Control of Cooperating Robots / Control of Collaborating Robots
Smart Control of Machine Fleet (bar linkages setup and wind turbine setup) / Control of a Machine Park: Similar Machines Learning from each Other

In the domain of energy:  
Self-Sustainable Smart Grids 
Verbeterde opbrengst van windmolenparken

In the domain of government & citizens:  
Exchanging Data and Knowledge in a Healthcare Setting 
 
These use cases allow us to validate the newly developed architectures and algorithms in real-world scenarios, and act as a bridge between the researchers of the grand challenge and external stakeholders such as industry and government. 

To address the increasing demand for flexible robotization in several applications as manufacturing, warehousing, infrastructure, agriculture, rescue and disaster response, etc; the task complexity or flexibility is too high for a single agent to accomplish or the task is inherently distributed in space. Under these circumstances, building several resource-bounded agents or robots is much better than having a single complex robot. Besides their increased robustness through redundancy, one of the main advantages is that multiple agents can solve problems faster using parallelism. Traditional centralized control approaches cannot be used with these systems due to, e.g. centralized computational issues or issues with centralized data collection, actuation and communication and scale constraints. WP2 combines in a multilayer and centralized/decentralized control architecture model-based and model-free methods with human advice to obtain a team of cooperating agents and humans that are able to perform tasks safely from the start and improve their collective performance over time. As a result, the agents do not have a global view of the problem, while, depending on the degree of interaction that exists between the local subsystems, the agents or robots need to coordinate e.g., through strategic communication or via incentives.

WP3 studies aspects of hybrid multi-agent systems, which include human agents as well as digital agents. The presence of humans brings important extra challenges, which the ongoing COVID-19 pandemic has made more obvious than ever. The available measures such as social distancing or wearing masks are all heavily dependent on human compliance. Yet, very often our personal goals are not necessarily aligned with the common good. 
 
The goals of this WP are threefold. A first task proposes new techniques for modelling individual and collective behaviour and investigates how this behaviour needs to be integrated into agent systems to make successful hybrid systems. A second task aims to investigate how much trust plays a role in using hybrid MAS and which incentives need to be provided to ensure long-term relations between human participants and MAS. At the moment knowledge of hybrid MAS behaviour is underutilized in industry. Finally, a third task is focusing on how outcome of collective behaviour can be influenced, either through incentives or through exchange of information (which itself will be influenced by how much agents are willing to share their information and the reciprocity of the exchange).

The focus of WP4 is to investigate how to exchange privacy-sensitive data and knowledge in a multi-agent environment, where information requests need to be efficiently matched with data or information sources. When one party is willing to exchange some of their data with another party through an AI system, the parties must be able to communicate with the system. How can they specify to the AI what data to share, and what to keep private, without having to manually indicate everything explicitly, which is impractical? How can a party communicate in what structure or shape they would like to receive the data? How can data be exchanged, transformed, and queried automatically?