We study agents expressing propositional goals over a set of binary issues to reach a collective decision. We adapt properties and rules from the literature on Social Choice Theory to our setting, providing an axiomatic characterisation of a majority rule for goal-based voting. We study the computational complexity of finding the outcome of our rules (i.e., winner determination), showing that it ranges from Nondeterministic Polynomial Time (NP) to Probabilistic Polynomial Time (PP).
CITATION STYLE
Novaro, A., Grandi, U., Longin, D., & Lorini, E. (2018). Goal-based collective decisions: Axiomatics and computational complexity. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2018-July, pp. 468–474). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/65
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