Representing and Reasoning with Multi-Stakeholder Qualitative Preference Queries
Basu, Samik, Honavar, Vasant, Santhanam, Ganesh Ram, Tao, Jia
–arXiv.org Artificial Intelligence
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., $\mlangpref{\psi_1}{\psi_2}{A}$ (read loosely as the set of outcomes satisfying $\psi_1$ that are preferred over outcomes satisfying $\psi_2$ 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 $\mu$-calculus. We present experimental results that demonstrate the feasibility of our approach.
arXiv.org Artificial Intelligence
Jul-30-2023
- Country:
- Asia > China
- Europe
- Denmark > Capital Region
- Copenhagen (0.04)
- United Kingdom > England
- Cambridgeshire > Cambridge (0.04)
- Greater London > London (0.04)
- Denmark > Capital Region
- North America > United States
- California > San Francisco County
- San Francisco (0.04)
- Indiana (0.04)
- Iowa > Story County
- Ames (0.04)
- New York > New York County
- New York City (0.04)
- Pennsylvania
- Centre County > University Park (0.04)
- Northampton County > Easton (0.04)
- California > San Francisco County
- Genre:
- Research Report (0.40)
- Industry:
- Technology: