Efficient inference and computation of optimal alternatives for preference languages based on lexicographic models
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International Joint Conferences on Artificial Intelligence
We analyse preference inference, through consistency, for general preference languages based on lexicographic models. We identify a property, which we call strong compositionality , that applies for many natural kinds of preference statement, and that allows a greedy algorithm for determining consistency of a set of preference statements. We also consider different natural definitions of optimality, and their relations to each other, for general preference languages based on lexicographic models. Based on our framework, we show that testing consistency, and thus inference, is polynomial for a specific preference language L′ pqT , which allows strict and non-strict statements, comparisons between outcomes and between partial tuples, both ceteris paribus and strong statements, and their combination. Computing different kinds of optimal sets is also shown to be polynomial; this is backed up by our experimental results.
Preference inference techniques , Preference inference , Artificial intelligence (AI) , AI technology , Knowledge representation , Reasoning and logic: preferences
Wilson, N. and George, A.-M. (2017) 'Efficient Inference and Computation of Optimal Alternatives for Preference Languages Based On Lexicographic Models', IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia 19-25 August, pp. 1311-1317. doi: 10.24963/ijcai.2017/182
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