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Publications (10 of 64) Show all publications
Schäfer, D., Klessascheck, F., Kampik, T. & Pufahl, L. (2025). Can we leverage process data from ERP systems for business process sustainability analyses?. In: Andrea Delgado; Tijs Slaats (Ed.), Andrea Delgado and Tijs Slaats (Ed.), Process mining workshops: . Paper presented at ICPM 2024 International Workshops, Lyngby, Denmark, October 14–18, 2024 (pp. 764-777). Paper presented at ICPM 2024 International Workshops, Lyngby, Denmark, October 14–18, 2024. Cham: Springer Nature
Open this publication in new window or tab >>Can we leverage process data from ERP systems for business process sustainability analyses?
2025 (English)In: Process mining workshops / [ed] Andrea Delgado; Tijs Slaats, Cham: Springer Nature, 2025, p. 764-777Chapter in book (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Cham: Springer Nature, 2025
Series
Lecture Notes in Business Information Processing ; 533
Keywords
Sustainability, Green Business Process Management, Key Ecological Indicators, Process Data Analysis
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-237257 (URN)10.1007/978-3-031-82225-4_56 (DOI)2-s2.0-105002042355 (Scopus ID)
Conference
ICPM 2024 International Workshops, Lyngby, Denmark, October 14–18, 2024
Available from: 2025-04-03 Created: 2025-04-03 Last updated: 2025-05-06Bibliographically approved
Kampik, T. & Nieves, J. C. (2025). Disagree and commit: degrees of argumentation-based agreements. Autonomous Agents and Multi-Agent Systems, 39(1), Article ID 8.
Open this publication in new window or tab >>Disagree and commit: degrees of argumentation-based agreements
2025 (English)In: Autonomous Agents and Multi-Agent Systems, ISSN 1387-2532, E-ISSN 1573-7454, Vol. 39, no 1, article id 8Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Nature, 2025
Keywords
Formal argumentation, agreement technologies, multi-agent systems
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-235100 (URN)10.1007/s10458-025-09688-7 (DOI)001406623800001 ()2-s2.0-85218109624 (Scopus ID)
Funder
Knut and Alice Wallenberg FoundationWallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2025-02-06 Created: 2025-02-06 Last updated: 2025-03-05Bibliographically approved
Kirchdorfer, L., Blümel, R., Kampik, T., van der Aa, H. & Stuckenschmidt, H. (2025). Discovering multi-agent systems for resource-centric business process simulation. Process Science, 2(1), Article ID 4.
Open this publication in new window or tab >>Discovering multi-agent systems for resource-centric business process simulation
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2025 (English)In: Process Science, E-ISSN 2948-2178, Vol. 2, no 1, article id 4Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2025
Keywords
Process Simulation, Agent-based Simulation, Business Process Management
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-237415 (URN)10.1007/s44311-025-00009-5 (DOI)
Available from: 2025-04-08 Created: 2025-04-08 Last updated: 2025-04-08Bibliographically approved
Brand, J., Kampik, T., Okulmus, C. & Weidlich, M. (2025). One language to rule them all: behavioural querying of process data using SQL. In: Andrea Delgado; Tijs Slaats (Ed.), Andrea Delgado; Tijs Slaats (Ed.), Process mining workshops: . Paper presented at ICPM 2024 International Workshops, Lyngby, Denmark, October 14–18, 2024 (pp. 18-30). Paper presented at ICPM 2024 International Workshops, Lyngby, Denmark, October 14–18, 2024. Cham: Springer Nature
Open this publication in new window or tab >>One language to rule them all: behavioural querying of process data using SQL
2025 (English)In: Process mining workshops / [ed] Andrea Delgado; Tijs Slaats, Cham: Springer Nature, 2025, p. 18-30Chapter in book (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Cham: Springer Nature, 2025
Series
Lecture Notes in Business Information Processing ; 533
Keywords
Process Querying, Process Mining, Pattern Recognition
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-237256 (URN)10.1007/978-3-031-82225-4_2 (DOI)2-s2.0-105002040834 (Scopus ID)
Conference
ICPM 2024 International Workshops, Lyngby, Denmark, October 14–18, 2024
Available from: 2025-04-03 Created: 2025-04-03 Last updated: 2025-05-06Bibliographically approved
Mendez, J. A., Kampik, T., Aler Tubella, A. & Dignum, V. (2024). A clearer view on fairness: visual and formal representations for comparative analysis. In: Florian Westphal; Einav Peretz-Andersson; Maria Riveiro; Kerstin Bach; Fredrik Heintz (Ed.), 14th Scandinavian Conference on Artificial Intelligence, SCAI 2024: June 10-11, 2024, Jönköping, Sweden. Paper presented at 14th Scandinavian Conference on Artificial Intelligence, Jönköping, Sweden, June 10-11, 2024 (pp. 112-120). Jönköping University
Open this publication in new window or tab >>A clearer view on fairness: visual and formal representations for comparative analysis
2024 (English)In: 14th Scandinavian Conference on Artificial Intelligence, SCAI 2024: June 10-11, 2024, Jönköping, Sweden / [ed] Florian Westphal; Einav Peretz-Andersson; Maria Riveiro; Kerstin Bach; Fredrik Heintz, Jönköping University , 2024, p. 112-120Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Jönköping University, 2024
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 208
Keywords
Responsible artificial intelligence, Ethics in artificial intelligence, Formal representation of fairness
National Category
Software Engineering Computer Sciences
Research subject
Computer Science; Ethics
Identifiers
urn:nbn:se:umu:diva-232255 (URN)10.3384/ecp208013 (DOI)9789180757096 (ISBN)
Conference
14th Scandinavian Conference on Artificial Intelligence, Jönköping, Sweden, June 10-11, 2024
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2024-11-27 Created: 2024-11-27 Last updated: 2024-12-02Bibliographically approved
Kampik, T., Čyras, K., Rago, A. & Cocarascu, O. (Eds.). (2024). ArgXAI-24: Argumentation for eXplainable AI: Proceedings of the 2nd International Workshop on Argumentation for eXplainable AI co-located with the 10th International Conference on Computational Models of Argument (COMMA 2024). Paper presented at 2nd International Workshop on Argumentation for eXplainable AI, Hagen, Germany, September 16, 2024. CEUR
Open this publication in new window or tab >>ArgXAI-24: Argumentation for eXplainable AI: Proceedings of the 2nd International Workshop on Argumentation for eXplainable AI co-located with the 10th International Conference on Computational Models of Argument (COMMA 2024)
2024 (English)Conference proceedings (editor) (Refereed)
Place, publisher, year, edition, pages
CEUR, 2024
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 3768
Keywords
Formal argumentation, explainable AI
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-230507 (URN)
Conference
2nd International Workshop on Argumentation for eXplainable AI, Hagen, Germany, September 16, 2024
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2024-10-03 Created: 2024-10-03 Last updated: 2024-10-07Bibliographically approved
Mendez, J. A. & Kampik, T. (2024). Can proof assistants verify multi-agent systems?. In: : . Paper presented at 21st European Conference on Multi-Agent Systems, EUMAS 2024, Dublin, Ireland, August 26-28, 2024.
Open this publication in new window or tab >>Can proof assistants verify multi-agent systems?
2024 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

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.

Keywords
Engineering Multi-Agent Systems, Formal Verification, Proof Automation
National Category
Computer Sciences Computer Engineering Computer Systems
Research subject
Computer Science; Computer Systems; Mathematical Logic
Identifiers
urn:nbn:se:umu:diva-232383 (URN)
Conference
21st European Conference on Multi-Agent Systems, EUMAS 2024, Dublin, Ireland, August 26-28, 2024
Available from: 2024-11-28 Created: 2024-11-28 Last updated: 2025-02-04
Kampik, T., Čyras, K. & Alarcón, J. R. (2024). Change in quantitative bipolar argumentation: sufficient, necessary, and counterfactual explanations. International Journal of Approximate Reasoning, 164, Article ID 109066.
Open this publication in new window or tab >>Change in quantitative bipolar argumentation: sufficient, necessary, and counterfactual explanations
2024 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 164, article id 109066Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2024
Keywords
quantitative argumentation, explainable AI, formal methods
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-216331 (URN)10.1016/j.ijar.2023.109066 (DOI)001112723700001 ()2-s2.0-85176266551 (Scopus ID)
Available from: 2023-11-09 Created: 2023-11-09 Last updated: 2025-04-24Bibliographically approved
Kampik, T., Potyka, N., Yin, X., Čyras, K. & Toni, F. (2024). Contribution functions for quantitative bipolar argumentation graphs: a principle-based analysis. International Journal of Approximate Reasoning, 173, Article ID 109255.
Open this publication in new window or tab >>Contribution functions for quantitative bipolar argumentation graphs: a principle-based analysis
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2024 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 173, article id 109255Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Automated reasoning, Explainable AI, Quantitative argumentation
National Category
Mathematical Analysis
Identifiers
urn:nbn:se:umu:diva-228071 (URN)10.1016/j.ijar.2024.109255 (DOI)001280749100001 ()2-s2.0-85199304547 (Scopus ID)
Funder
EU, Horizon 2020, 101020934
Available from: 2024-07-31 Created: 2024-07-31 Last updated: 2025-04-24Bibliographically approved
Corea, C., Kampik, T. & Montali, M. (2024). Explainable DMN. In: Andrea Marrella; Manuel Resinas Mieke Jans; Michael Rosemann (Ed.), Business Process Management Forum: BPM 2024 Forum, Krakow, Poland, September 1–6, 2024, Proceedings. Paper presented at Business Process Management Forum (BPM 2024 Forum), Krakow, Poland, September 1–6, 2024. (pp. 55-71). Springer
Open this publication in new window or tab >>Explainable DMN
2024 (English)In: Business Process Management Forum: BPM 2024 Forum, Krakow, Poland, September 1–6, 2024, Proceedings / [ed] Andrea Marrella; Manuel Resinas Mieke Jans; Michael Rosemann, Springer, 2024, p. 55-71Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2024
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 526
Keywords
DMN, Explainability, Shapley Values
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-229359 (URN)10.1007/978-3-031-70418-5_4 (DOI)001316097300004 ()978-3-031-70417-8 (ISBN)978-3-031-70418-5 (ISBN)
Conference
Business Process Management Forum (BPM 2024 Forum), Krakow, Poland, September 1–6, 2024.
Available from: 2024-09-08 Created: 2024-09-08 Last updated: 2025-04-24Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6458-2252

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