Abstract
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.
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Gehrke, M., Braun, T., Polovina, S. (2020). Restricting the Maximum Number of Actions for Decision Support Under Uncertainty. In: Alam, M., Braun, T., Yun, B. (eds) Ontologies and Concepts in Mind and Machine. ICCS 2020. Lecture Notes in Computer Science(), vol 12277. Springer, Cham. https://doi.org/10.1007/978-3-030-57855-8_11
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