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Representing and Reasoning with Multi-Stakeholder Qualitative Preference Queries

European Conference on Artificial Intelligence (ECAI), 2023
Abstract

Many decision-making scenarios, e.g., public policy, healthcare, business, and disaster response, require accommodating the preferences of multiple stakeholders. We offer the first formal treatment of reasoning with multi-stakeholder qualitative preferences in a setting where stakeholders express their preferences in a qualitative preference language, e.g., CP-net, CI-net, TCP-net, CP-Theory. We introduce a query language for expressing queries against such preferences over sets of outcomes that satisfy specified criteria, e.g., \mlangprefψ1ψ2A\mlangpref{\psi_1}{\psi_2}{A} (read loosely as the set of outcomes satisfying ψ1\psi_1 that are preferred over outcomes satisfying ψ2\psi_2 by a set of stakeholders AA). Motivated by practical application scenarios, we introduce and analyze several alternative semantics for such queries, and examine their interrelationships. We provide a provably correct algorithm for answering multi-stakeholder qualitative preference queries using model checking in alternation-free μ\mu-calculus. We present experimental results that demonstrate the feasibility of our approach.

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