Agent System Mining: Vision, Benefits, and Challenges

Abstract

In the Agent-Based Modeling (ABM) paradigm, an organization is a Multi-Agent System (MAS) composed of autonomous agents inducing business processes. Process Mining automates the creation, update, and analysis of explicit business process models based on event data. Process Mining techniques make simplifying assumptions about the processes discovered from data. However, actual business processes are often more complex than those restricted by Process Mining assumptions. Several Process Mining approaches relax these standard assumptions by discovering more realistic process models. These approaches can discover more realistic process models. However, these models are often difficult to visualize and, consequently, to understand. Many MASs induce processes whose behaviors become more complex with each next embraced time step, while the complexities of these MASs remain constant. Thus, the ABM paradigm can cope naturally with the increasing complexity of the discovered process models. This paper proposes Agent System Mining (ASM) and ASM Framework. ASM combines Process Mining and ABM in the Business Process Management (BPM) context to infer MAS models of operational business processes from real-world event data, while ASM Framework maps ASM activities to different phases of the MAS modeling lifecycle. The paper also discusses the benefits of using ASM and outlines challenges associated with the implementation of the ASM Framework.

Governance of Autonomous Agents on the Web: Challenges and Opportunities

“…In practice, protocol design and protocol discovery can go hand-in-hand: in particular, Petri Net-based protocols (processes) can be mined from IT system event logs [140], which, for example, can be used for organisational compliance checking [30]. Recently, agent system mining has been proposed as a novel process mining variant that focuses on the agents that participate in one or several (organisational) processes, i.e., on the micro-level instead of on the macro-level process view that an organisation imposes [138]. In the service-oriented community, the notion of a choreography is similar to an interaction protocol [9], in that a choreography describes interactions between services from a global perspective.…”

Discovering Agent Models using Process Mining: Initial Approach and a Case Study

“…There exists a large volume of published studies on the integrated role of ABM and process mining [20]. Roughly speaking, literature can be divided into two categories.…”

“…As such, process mining provides a means of capturing complex processes which have formerly been simplified out of the model [19]. Using process mining with ABM could provide different perspectives for analyzing the macro level behavioral impact of micro level changes [20]. Process mining techniques can connect relevant activities, as recorded in event logs, and analyze the logs from different perspectives by ultimately, turning event data into insights and actions for improving processes (e.g., running more efficiently/effectively), and thus also agent decision-making.…”

“…Individual-level details can be observed/derived directly from the digital traces. Furthermore, those footprints may not only be limited to capturing micro level behaviors but could also include elements (e.g., actions and interactions) that span macro level behavior emerging from micro level behaviors [20], [21]. For example, an agent who is interacting with other agents may unravel patterns about other agents because of frequent interactions.…”

“…Besides that, as agent-based models are flexible, adaptable, and reconfigurable [22], ABM can cope intrinsically with complexities arising by comprising data analytics techniques. To some extent, this has already been exemplified by using process mining (e.g., see [20]). Previous work also showed that agents could adapt their decisions based on knowledge derived from discovered process models [23].…”