FlexiTeams – An Interactive Visual Representation of AI-based Knowledge to Reorganize Operational Teams in Crises

Abstract: Crises, such as the COVID-19 pandemic, pose unprecedented challenges for governmental or healthcare organizations as well as for the entire industry and the service sector. For instance, shifts in business areas due to increased infection control regulations led to overburdened or underchallenged units within the organizations. Thus, the flexible and highly dynamic adaptation of work processes and team organizations to the changed conditions are essential for maintaining the economic and social infrastructure. The requirements for such a reorganization are constantly changing due to the parallel process of gaining knowledge about the actual risk factors in the spread of the pandemic and require an agile reorientation, which is associated with great effort and uncertainty.For this reason, we present the FlexiTeams framework that supports decision makers to manage staff allocation and workflow organization in the context of such time-sensitive situations using conversational artificial intelligence and agent-based simulation. Agent-based modeling and simulation are established in many disciplines as a new tool for the analysis of complex systems. In social simulation, agent-based models are often used to analyze emergent effects e.g., as phenomena of social contagion. In cognitive social simulation, mechanisms of sociology and psychology are combined to generate cognitive decision-making behavior within an agent as well as group dynamic behavior between agents. This allows the study of complex socio-digital systems in which humans and (semi-)autonomous information systems cooperate in knowledge-intensive processes. However, in a crisis situation such as the COVID-19 pandemic or severe weather disasters, it is necessary not only to capture specifications of a team or to evaluate their efficiency, it is rather important to react to current situations. Indeed, it is of special interest to assemble the available work force accordingly, which may be seconded from other tasks. The flexible allocation of resources depending on a current situation is an important field of research in Distributed Artificial Intelligence (DAI). DAI deals with systems of (partially) autonomous decision makers, so-called software agents, whose behavior are largely determined by situational decision-making, negotiation and coordination during the runtime of the system. In addition to the agent-based approach, the comprehensive knowledge representation of process-oriented case-based reasoning (POCBR) is well suited for the modeling and processing of experiential knowledge about team constellations and work processes with the aim to make suggestions for adjustments in the sense of reorganizations.One major success factor to benefit in time-critical situations from the provided guidance and suggestions is to keep the human in the loop regarding both, the AI’s decisions and its simulation’s results. Thus, one part of the framework is to design a suitable interface to enable users to understand and revise the AI’s results. In this paper, we introduce the general framework, discuss its novelty and present an initial demo prototype showcasing some UI design concepts relevant to this context. For instance, an overarching dashboard is designed to represent resources, profiles, and key performance indicators (KPIs) comprising economic, social, and health status.

Keywords: explainable artificial intelligence, crises management, person allocation

DOI: 10.54941/ahfe1004192

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