AIDBLOJul 30, 2023

Representing and Reasoning with Multi-Stakeholder Qualitative Preference Queries

arXiv:2307.16307v1h-index: 56
Originality Highly original
AI Analysis

This work addresses the challenge of accommodating diverse stakeholder preferences in complex decision-making, offering a foundational formal framework for applications in domains such as policy and business.

The paper tackles the problem of reasoning with multi-stakeholder qualitative preferences in decision-making scenarios like public policy and healthcare, introducing a formal query language and semantics, and provides a provably correct algorithm with experimental results demonstrating feasibility.

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., $\mlangpref{ψ_1}{ψ_2}{A}$ (read loosely as the set of outcomes satisfying $ψ_1$ that are preferred over outcomes satisfying $ψ_2$ by a set of stakeholders $A$). 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 $μ$-calculus. We present experimental results that demonstrate the feasibility of our approach.

Foundations

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