Standard approaches for decision support are computing a maximum expected utility or solving a partially observable Markov decision process. To the best of our knowledge, in both approaches, external restrictions are not accounted for. However, restrictions to actions often exists, for example in the form of limited resources. We demonstrate that restrictions to actions can lead to a combinatorial explosion if performed on a ground level, making ground inference intractable. Therefore, we extend a formalism that solves a lifted maximum expected utility problem to handle restricted actions. To test its relevance, we apply the new formalism to enterprise architecture analysis.
CITATION STYLE
Gehrke, M., Braun, T., & Polovina, S. (2020). Restricting the Maximum Number of Actions for Decision Support Under Uncertainty. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12277 LNAI, pp. 145–160). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57855-8_11
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