Summary of Distances Between Partial Preference Orderings, by Jean Dezert et al.
Distances Between Partial Preference Orderings
by Jean Dezert, Andrii Shekhovtsov, Wojciech Salabun
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores a novel method for calculating the distance between partial preference orderings in decision-making scenarios. The authors propose two approaches: a brute force method based on combinatorics, which generates all possible complete preference orderings and calculates the Frobenius distance; and a belief functions approach that models missing information and circumvents combinatorial complexity limitations. The paper demonstrates the effectiveness of both methods through simple examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about finding the right way to compare different options when you don’t have all the information. The authors came up with two ways to do this: one that looks at every possible combination, and another that uses special math tools to make it more efficient. They show how these methods work through simple examples. |
