Reasoning With Conditional Ceteris Paribus Preference Statem
This work addresses the need for compact and natural preference representations in automated decision-making, though it appears incremental as it builds on existing ceteris paribus concepts with algorithmic improvements.
The paper tackles the problem of assessing user preferences qualitatively for automated decision tools by proposing a graphical representation that reflects conditional dependence and independence under a ceteris paribus interpretation, resulting in effective search algorithms for dominance testing, especially in specific network topologies like chain-, tree-structured networks, and polytrees.
In many domains it is desirable to assess the preferences of users in a qualitative rather than quantitative way. Such representations of qualitative preference orderings form an importnat component of automated decision tools. We propose a graphical representation of preferences that reflects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a representation is ofetn compact and arguably natural. We describe several search algorithms for dominance testing based on this representation; these algorithms are quite effective, especially in specific network topologies, such as chain-and tree- structured networks, as well as polytrees.