Abstract
Existing Artificial Intelligence (AI) driven automation solutions in enterprises employ machine learning, natural language processing, and chatbots. There is an opportunity for AI Planning to be applied, which offers reasoning about action trajectories to help build automation blueprints. AI Planning is a problem-solving technique, where knowledge about available actions and their consequences is used to identify a sequence of actions, which, when applied in a given initial state, satisfy a desired goal. AI Planning has successfully been applied in a number of domains ranging from space applications, logistics and transportation, manufacturing, robotics, scheduling, e-learning, enterprise risk management, and service composition. In this paper, we discuss experience in building automation solutions that employ AI planning for use in enterprise IT and business services, such as change and event management, migration and transformation and RPA composition. We discuss challenges in adoption of AI planning across the enterprise from implementation and deployment perspectives.
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References
Ninth International Planning Competition (IPC-9): planner abstracts (2018)
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019). AAAI Press (2019)
van der Aa, H., Leopold, H., Reijers, H.A.: Detecting inconsistencies between process models and textual descriptions. In: Business Process Management - 13th International Conference, BPM 2015, Innsbruck, Austria, 31 August - 3 September, 2015, Proceedings, pp. 90–105 (2015)
Cenamor, I., de la Rosa, T., Fernández, F.: IBaCoP-2018 and IBaCoP2-2018. In: Ninth International Planning Competition (IPC-9): planner abstracts [1], pp. 9–10
Hassanzadeh, O., et al.: Answering binary causal questions through large-scale text mining: An evaluation using cause-effect pairs from human experts. In: IJCAI19 (2019)
Hoffmann, J., Weber, I., Kraft, F.M.: Sap speaks PDDL: exploiting a software-engineering model for planning in business process management. J. Artif. Int. Res. 44(1), 587–632 (2012). http://dl.acm.org/citation.cfm?id=2387933.2387946
Hull, R., Nezhad, H.R.M.: Rethinking BPM in a cognitive world: transforming how we learn and perform business processes. In: BPM 2016, Rio de Janeiro, Brazil, 18–22 September 2016, Proceedings (2016)
Jackson, M., Rofrano, J.J., Hwang, J., Vukovic, M.: Blueplan: a service for automated migration plan construction using AI. In: Service-Oriented Computing - ICSOC 2018 Workshops, Hangzhou, China, November 2018, Revised Selected Papers (2018)
Katz, M., Sohrabi, S., Samulowitz, H., Sievers, S.: Delfi: Online planner selection for cost-optimal planning. In: Ninth International Planning Competition (IPC-9): planner abstracts [1], pp. 57–64
Narayanan, S., McIlraith, S.A.: Simulation, verification and automated composition of web services. In: Proceedings of the 11th International Conference on World Wide Web, WWW 2002, pp. 77–88. ACM, New York (2002)
Nezhad, H.R.M., Akkiraju, R.: Towards cognitive BPM as the next generation BPM platform for analytics-driven business processes. In: Business Process Management Workshops - BPM 2014 International Workshops, Eindhoven, The Netherlands, 7–8 September 2014, Revised Papers, pp. 158–164 (2014)
Sievers, S., Katz, M., Sohrabi, S., Samulowitz, H., Ferber, P.: Deep learning for cost-optimal planning: task-dependent planner selection. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019). [2]
Sohrabi, S., Katz, M., Hassanzadeh, O., Udrea, O., Feblowitz, M.D., Riabov, A.: IBM scenario planning advisor: plan recognition as AI planning in practice. AI Commun. 32(1), 1–13 (2019)
Vaquero, T.S., Romero, V., Tonidandel, F., Silva, J.R.: itSIMPLE 2.0: an integrated tool for designing planning domains. In: Boddy, M., Fox, M., Thiébaux, S. (eds.) Proceedings of the Seventeenth International Conference on Automated Planning and Scheduling (ICAPS 2007), pp. 336–343. AAAI Press (2007)
Acknowledgments
We thank our colleagues: Stefan Pappe, Arvind Viswanathan, Valentina Salapura, Sridrar Thiruvengadam, Boby Philip, Sharon Alvarado Brenes, Joaquin Eduardo Bonilla Arias, and Sussana Ting.
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Vukovic, M. et al. (2019). Towards Automated Planning for Enterprise Services: Opportunities and Challenges. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_6
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DOI: https://doi.org/10.1007/978-3-030-33702-5_6
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