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Sustainability is an increasingly important issue, which organizations need to take into account when assessing and improving their business processes. Doing so can contribute to enhancing an organisation’s overall sustainability. Green Business Process Management is a line of research concerned with supporting organisations to integrate a sustainability perspective into their processes. However, existing approaches that assess sustainability on activity and process levels using, for instance, Life-Cycle Assessment (LCA) are often time-consuming and complex. Therefore, this work explores whether Key Ecological Indicators (KEIs) used to assess the sustainability of a business process can be calculated using data already available within an organisation. Following a case study methodology, we analyse nine real-world datasets extracted from a business process analysis system of a large enterprise software vendor. Results indicate that current data availability is insufficient for exact assessments. To address this issue, we introduce a high-level conceptual model and provide recommendations for action based on the observations of the case study.

In cooperative human decision-making, agreements are often not total; a partial degree of agreement is sufficient to commit to a decision and move on, as long as one is somewhat confident that the involved parties are likely to stand by their commitment in the future, given no drastic unexpected changes. In this paper, we introduce the notion of agreement scenarios that allow artificial autonomous agents to reach such agreements, using formal models of argumentation, in particular abstract argumentation and value-based argumentation. We introduce the notions of degrees of satisfaction and (minimum, mean, and median) agreement, as well as a measure of the impact a value in a value-based argumentation framework has on these notions. We then analyze how degrees of agreement are affected when agreement scenarios are expanded with new information, to shed light on the reliability of partial agreements in dynamic scenarios. An implementation of the introduced concepts is provided as part of an argumentation-based reasoning software library.

Business process simulation (BPS) is a powerful tool for estimating process performance across different scenarios, offering critical support for organizational process redesign and optimization. Traditional BPS approaches predominantly rely on a control-flow-first perspective by enriching a process model with simulation parameters. While these approaches seem suitable for capturing centrally orchestrated processes, such as those managed by workflow systems, they fall short of accurately reflecting real-world processes characterized by decentralized decision-making and distinct resource behaviors. To overcome this limitation, we propose AgentSimulator, a resource-first BPS approach that discovers a multi-agent system from an event log. By modeling the distinct behaviors and interaction patterns of individual resources, AgentSimulator effectively simulates the underlying process. Our approach automatically identifies whether resource behavior is rather orchestrated or autonomous, adapting to the specific decision-making structure of the process. Experimental results reveal that AgentSimulator achieves state-of-the-art simulation accuracy while ensuring high adaptability to various process types.

State-of-the-art solutions for process mining rely on proprietary, domain-specific languages to query data recorded during business process execution. To support common analysis tasks, these languages focus on the definition of queries for behavioural patterns. Yet, the use of domain-specific languages for process mining has drawbacks: they require specific user training, lead to a decoupling of the query models for (i) data extraction and transformation, and (ii) the actual analysis, and induce engineering overhead through the development of a dedicated query engine. In this work, we therefore explore the use of standard SQL for process mining tasks. In particular, we demonstrate that the SQL concepts for row pattern recognition as realised by the MATCH_RECOGNIZE clause are sufficient to capture queries for behavioural patterns as specified in the SIGNAL language by SAP Signavio as well as the Process Querying Language (PQL) by Celonis. Based on a discussion of the respective language features, we outline a translation of SIGNAL and PQL queries into standard SQL. This way, we provide the basis for the adoption of widely used, general purpose query engines for process mining tasks.

The opaque nature of machine learning systems has raised concerns about whether these systems can guarantee fairness. Furthermore, ensuring fair decision making requires the consideration of multiple perspectives on fairness. 

At the moment, there is no agreement on the definitions of fairness, achieving shared interpretations is difficult, and there is no unified formal language to describe them. Current definitions are implicit in the operationalization of systems, making their comparison difficult.

In this paper, we propose a framework for specifying formal representations of fairness that allows instantiating, visualizing, and comparing different interpretations of fairness. Our framework provides a meta-model for comparative analysis. We present several examples that consider different definitions of fairness, as well as an open-source implementation that uses the object-oriented functional language Soda.

This paper presents the Soda language for verifying multi-agent systems. Soda is a high-level functional and object-oriented language that supports the compilation of its code not only to Scala, a strongly statically typed high-level programming language, but also to Lean, a proof assistant and programming language. Given these capabilities, Soda can implement multi-agent systems, or parts thereof, that can then be integrated into a mainstream software ecosystem on the one hand and formally verified with state-of-the-art tools on the other hand.

We provide a brief and informal introduction to Soda and the aforementioned interoperability capabilities, as well as a simple demonstration of how interaction protocols can be designed and verified with Soda. In the course of the demonstration, we highlight challenges with respect to real-world applicability.

This paper presents a formal approach to explaining change of inference in Quantitative Bipolar Argumentation Frameworks (QBAFs). When drawing conclusions from a QBAF and updating the QBAF to then again draw conclusions (and so on), our approach traces changes – which we call strength inconsistencies – in the partial order over argument strengths that a semantics establishes on some arguments of interest, called topic arguments. We trace the causes of strength inconsistencies to specific arguments, which then serve as explanations. We identify sufficient, necessary, and counterfactual explanations for strength inconsistencies and show that strength inconsistency explanations exist if and only if an update leads to strength inconsistency. We define a heuristic-based approach to facilitate the search for strength inconsistency explanations, for which we also provide an implementation.

We present a principle-based analysis of contribution functions for quantitative bipolar argumentation graphs that quantify the contribution of one argument to another. The introduced principles formalise the intuitions underlying different contribution functions as well as expectations one would have regarding the behaviour of contribution functions in general. As none of the covered contribution functions satisfies all principles, our analysis can serve as a tool that enables the selection of the most suitable function based on the requirements of a given use case.

We investigate means for the explainability of Decision Model and Notation (DMN) models. These are especially relevant for industrial settings, where the scale and complexity of DMN models can otherwise quickly make it unfeasible for companies to understand and maintain their decision logic. To this aim, we present a formal approach for measuring the impact of decision inputs on the decision output. In particular, we show how the decision logic of a DMN model can be transformed into a coalitional game based on (Datalog) queries over the decision tables, which allows one to apply the game-theoretic underpinning of Shapley values for measuring impact. Intuitively, the inputs of the decision act as the players of a coalitional game, and the payoff is the impact of an input/player on the decision output. The motivation of this work stems from real-life settings where means for understanding decision models are crucial, e.g., models of industrial complexity and domains such as fraud management. We implement our approach and evaluate it with real-life DMN models from the SAP-SAM dataset.