GameOfFlows: Process Instance Adaptation in Complex, Dynamic and Potentially Adversarial Domains
RIS ID
136548
Abstract
Business processes often need to be executed in complex settings where a range of environmental factors can conspire to impede the execution of the process. Gou et al. [1] view process execution as an adversarial game between the process player and the environment player. While useful, their approach leaves open the question of the role of the original process design in the story. Process designs encode significant specialist knowledge and have significant investments in process infrastructure associated with them. We provide a machinery that involves careful deliberation on when and where to deviate from a process design. We conceive of a process engine that frequently (typically after executing each task) re-considers the next task or sequence of tasks to execute. It performs trade-off analysis by comparing the following: (1) the likelihood of successful completion by conforming to the mandated process design against (2) the likelihood of success if it were to deviate from the design by executing a compensation (i.e., an alternative sequence of tasks that takes the process from the current state to completion).
Publication Details
Gou, Y., Ghose, A. & Dam, H. Khanh. (2019). GameOfFlows: Process Instance Adaptation in Complex, Dynamic and Potentially Adversarial Domains. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11483 479-493. 31st International Conference, CAiSE 2019