# Random Graph Set and Evidence Pattern Reasoning Model

arXiv: https://arxiv.org/abs/2402.13058 · HTML: https://mlsanity.com/paper/2402.13058/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Maximum Entropy of Random Permutation Set](https://mlsanity.com/paper/2203.11941/) (2021)
- [Belief Evolution Network-based Probability Transformation and Fusion](https://mlsanity.com/paper/2110.03468/) (2021)

## Built on by (most cited first, 2 of 2)

- [Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting](https://mlsanity.com/paper/2405.06419/) (2024)
- [Random Walk in Random Permutation Set Theory](https://mlsanity.com/paper/2404.03978/) (2024)

## Abstract (arXiv)

Evidence theory is widely used in decision-making and reasoning systems. In previous research, Transferable Belief Model (TBM) is a commonly used evidential decision making model, but TBM is a non-preference model. In order to better fit the decision making goals, the Evidence Pattern Reasoning Model (EPRM) is proposed. By defining pattern operators and decision making operators, corresponding preferences can be set for different tasks. Random Permutation Set (RPS) expands order information for evidence theory. It is hard for RPS to characterize the complex relationship between samples such as cycling, paralleling relationships. Therefore, Random Graph Set (RGS) were proposed to model complex relationships and represent more event types. In order to illustrate the significance of RGS and EPRM, an experiment of aircraft velocity ranking was designed and 10,000 cases were simulated. The implementation of EPRM called Conflict Resolution Decision optimized 18.17% of the cases compared to Mean Velocity Decision, effectively improving the aircraft velocity ranking. EPRM provides a unified solution for evidence-based decision making.