Keith Purrington
ABSTRACT
CP-nets are an attractive model for representing individual preferences, in part because they allow us to find the best outcome for an agent in time that is proportional to just the number of features in an outcome. In this paper, we investigate whether similar efficiencies can apply to finding the best social outcome for agents whose individual preferences are captured in CP-nets. Because CP-nets provide only qualitative information, we adopt a way to compare outcomes across agents based on each outcome's relative standing in the individuals' spaces of possible outcomes. This in turn guides the search through the outcome preference graphs that are induced by the agents' CP-nets to find the optimal social outcome. Because these induced preference graphs are exponential in the number of features, we examine the conditions under which the agents can search directly using their CP-nets, and show that our approach yields near-optimal social outcomes in exponentially less time.
- Boutilier, C., Brafman, R. I., Domshlak, C., Hoos, H. H., and Poole, D. "CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements." Journal of Artificial Intelligence Research (JAIR), 2003. Google Scholar
Digital Library
- Rawls, J. A Theory of Justice. Cambridge, Massachusetts, Harvard University Press, 1971Google Scholar
- Rossi, F., Venable, K. B., Walsh, T. "mCP nets: Representing and Reasoning with Preferences of Multiple Agents." In: Procs. of the 19th Conf. on Artificial Intelligence, LNCS 749, AAAI Press 729--734, 2004 Google ScholarDigital Library
Index Terms
- Making social choices from individuals' CP-nets
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