Abstract
The CP-net and the LP-tree are two fundamental graphical models for representing user’s qualitative preferences. Constrained CP-nets have been studied in the past in which a very expensive operation, called dominance testing, between outcomes is required. In this paper, we propose a recursive backtrack search algorithm that we call Search-LP to find the most preferable feasible outcome for an LP-tree extended to a set of hard constraints. Search-LP instantiates the variables with respect to a hierarchical order defined by the LP-tree. Since the LP-tree represents a total order over the outcomes, Search-LP simply returns the first feasible outcome without performing dominance testing. We prove that this returned outcome is preferable to every other feasible outcome.
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Ahmed, S., Mouhoub, M. (2019). Lexicographic Preference Trees with Hard Constraints. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_31
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DOI: https://doi.org/10.1007/978-3-030-18305-9_31
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