Abstract
To cope with increasingly complex business, political, and economic environments, agent-based simulations (ABS) have been proposed for modeling complex systems such as human societies, transport systems, and markets. ABS enable experts to assess the influence of exogenous parameters (e.g., climate changes or stock market prices), as well as the impact of policies and their long-term consequences. Despite some successes, the use of ABS is hindered by a set of interrelated factors. First, ABS are mainly created and used by researchers and experts in academia and specialized consulting firms. Second, the results of ABS are typically not automatically integrated into the corresponding business process. Instead, the integration is undertaken by human users who are responsible for adjusting the implemented policy to take into account the results of the ABS. These limitations are exacerbated when the results of the ABS affect multi-party agreements (e.g., contracts) since this requires all involved actors to agree on the validity of the simulation, on how and when to take its results into account, and on how to split the losses/gains caused by these changes. To address these challenges, this paper explores the integration of ABS into enterprise application landscapes. In particular, we present an architecture that integrates ABS into cross-organizational enterprise resource planning (ERP) processes. As part of this, we propose a multi-agent systems simulator for the Hyperledger blockchain and describe an example supply chain management scenario type to illustrate the approach.
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Notes
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Note that JS-son does not require a BDI approach; instead, beliefs can activate plans right away.
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For a comprehensive overview of agent platforms, see Kravari and Bassiliades [15].
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Potentially, product quality could be dynamically adjusted as well.
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Other concerns are performance [26] and security issues.
References
Abar, S., Theodoropoulos, G.K., Lemarinier, P., O’Hare, G.M.: Agent based modelling and simulation tools: a review of the state-of-art software. Comput. Sci. Rev. 24, 13–33 (2017)
Androulaki, E., et al.: Hyperledger fabric: a distributed operating system for permissioned blockchains. In: Proceedings of the Thirteenth EuroSys Conference, p. 30. ACM (2018)
Bañgate, J., Dugdale, J., Beck, E., Adam, C.: A multi-agent system approach in evaluating human spatio-temporal vulnerability to seismic risk using social attachment. WIT Trans. Eng. Sci. 121, 47–58 (2018)
Berger, T., Schreinemachers, P., Woelcke, J.: Multi-agent simulation for the targeting of development policies in less-favored areas. Agric. Syst. 88(1), 28–43 (2006)
Bessghaier, N., Zargayouna, M., Balbo, F.: Management of urban parking: an agent-based approach. In: Ramsay, A., Agre, G. (eds.) AIMSA 2012. LNCS (LNAI), vol. 7557, pp. 276–285. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33185-5_31
Bordini, R.H., Hübner, J.F., Wooldridge, M.: Programming Multi-Agent Systems in AgentSpeak Using Jason. Wiley Series in Agent Technology. Wiley, Hoboken (2007)
Calvaresi, D., Dubovitskaya, A., Calbimonte, J.P., Taveter, K., Schumacher, M.: Multi-agent systems and blockchain: results from a systematic literature review. In: Demazeau, Y., An, B., Bajo, J., Fernández-Caballero, A. (eds.) PAAMS 2018. LNCS (LNAI), vol. 10978, pp. 110–126. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94580-4_9
Davidsson, P.: Agent based social simulation: a computer science view. J. Artif. Soc. Soc. Simul. 5(1), 1–7 (2002)
Drozd, O., Lazur, Y., Serbin, R.: Theoretical and legal perspective on certain types of legal liability in cryptocurrency relations. Baltic J. Econ. Stud. 3(5), 221–228 (2018)
Gregor, K.: IBM wants to make 2017 the year of blockchain enterprise deployment (2017). https://www.ibm.com/blockchain/in-en/assets/IDC_Report__IBM_wants_to_make_2017_the_year_of_BlockChain_Enterprise__Deployment.pdf (2017)
Haarmann, S., Batoulis, K., Nikaj, A., Weske, M.: DMN decision execution on the ethereum blockchain. In: Krogstie, J., Reijers, H.A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 327–341. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91563-0_20
Kampik, T., Najjar, A., Calvaresi, D.: MAS-aided approval for bypassing decentralized processes: an architecture. In: 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 713–718. IEEE (2018)
Kampik, T., Nieves, J.C.: JS-son - a minimalistic JavaScript BDI agent library. In: 7th International Workshop on Engineering Multi-Agent Systems (EMAS 2019), Montreal, Canada, 13–14 May 2019 (2019)
Korpela, K., Hallikas, J., Dahlberg, T.: Digital supply chain transformation toward blockchain integration. In: Proceedings of the 50th Hawaii International Conference on System Sciences (2017)
Kravari, K., Bassiliades, N.: A survey of agent platforms. J. Artif. Soc. Soc. Simul. 18(1), 11 (2015)
Lee, J.H., Kim, C.O.: Multi-agent systems applications in manufacturing systems and supply chain management: a review paper. Int. J. Prod. Res. 46(1), 233–265 (2008)
López-Pintado, O., García-Bañuelos, L., Dumas, M., Weber, I.: Caterpillar: a blockchain-based business process management system. In: Proceedings of the BPM Demo Track and BPM Dissertation Award Co-Located with 15th International Conference on Business Process Modeling (BPM 2017), Barcelona, Spain (2017)
Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., Balan, G.: MASON: a multiagent simulation environment. Simulation 81(7), 517–527 (2005)
Mendling, J., et al.: Blockchains for business process management-challenges and opportunities. ACM Trans. Manag. Inf. Syst. (TMIS) 9(1), 4 (2018)
Najjar, A.: Multi-agent negotiation for QoE-aware cloud elasticity management. Ph.D. thesis, École nationale supérieure des mines de Saint-Étienne (2015)
Najjar, A., Mualla, Y., Boissier, O., Picard, G.: AQUAMan: QoE-driven cost-aware mechanism for SaaS acceptability rate adaptation. In: Proceedings of the International Conference on Web Intelligence, pp. 331–339. ACM (2017)
North, M.J., et al.: Complex adaptive systems modeling with repast simphony. Complex Adapt. Syst. Model. 1(1), 3 (2013)
Ogie, R., Adam, C., Perez, P.: A review of structural approach to flood management in coastal megacities of developing nations: current research and future directions. J. Environ. Plann. Manag. 1–21 (2019)
OMG: business process model and notation (BPMN), version 2.0, January 2011
OMG: decision model and notation (DMN), version 1.1, June 2016
Pongnumkul, S., Siripanpornchana, C., Thajchayapong, S.: Performance analysis of private blockchain platforms in varying workloads. In: 26th International Conference on Computer Communication and Networks (ICCCN), pp. 1–6. IEEE (2017)
Rimba, P., Tran, A.B., Weber, I., Staples, M., Ponomarev, A., Xu, X.: Comparing blockchain and cloud services for business process execution. In: 2017 IEEE International Conference on Software Architecture (ICSA), pp. 257–260. IEEE (2017)
Serrano, E., Iglesias, C.A.: Validating viral marketing strategies in Twitter via agent-based social simulation. Expert Syst. Appl. 50, 140–150 (2016)
Serrano, E., Iglesias, C.A., Garijo, M.: A survey of Twitter rumor spreading simulations. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015. LNCS (LNAI), vol. 9329, pp. 113–122. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24069-5_11
Sklar, E.: NetLogo, a multi-agent simulation environment (2007)
Sokolowski, J.A., Banks, C.M., Hayes, R.L.: Modeling population displacement in the Syrian city of Aleppo. In: Proceedings of the Winter Simulation Conference 2014, pp. 252–263. IEEE (2014)
Wood, G.: Ethereum: A secure decentralised generalised transaction ledger. Ethereum project yellow paper, vol. 151, pp. 1–32 (2014)
Acknowledgements
This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation and partially funded by the German Federal Ministry of Education and Research (BMBF) within the Framework Concept “Industrie 4.0 – Kollaborationen in dynamischen Wertschöpfungsnetzwerken (InKoWe)”/managed by the Project Management Agency Forschungszentrum Karlsruhe (PTKA).
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Kampik, T., Najjar, A. (2019). Integrating Multi-agent Simulations into Enterprise Application Landscapes. In: De La Prieta, F., et al. Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems. The PAAMS Collection. PAAMS 2019. Communications in Computer and Information Science, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-24299-2_9
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