Leveraging game-tree search for robust process enactment



Publication Details

Gou, Y., Ghose, A. & Dam, H. (2017). Leveraging game-tree search for robust process enactment. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10253 461-476.


Springer International Publishing AG 2017.A robust machinery for process enactment should ideally be able to anticipate and account for possible ways in which the execution environment might impede a process from achieving its desired effects or outcomes. At critical decision points in a process, it is useful for the enactment machinery to compute alternative flows by viewing the problem as an adversarial game pitting the process (or its enactment machinery) against the process execution environment. We show how both minimax search and Monte Carlo game tree search, coupled with a novel conception of an evaluation function, delivers useful results.

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