상세정보
학술대회
Hypergraph based Multi-Agents Representation Learning for Similarity Analysis
- 발행일
- 202110
- 출처
- International Conference on Control, Automation and Systems (ICCAS) 2021, pp.1686-1689
- 초록
- As a lot of agents with diverse sensing capabilities are expected to collaborate in the near future, the needs to process a huge number of multi-modal data are emerging to recognize global situations, events or environment. In this paper, we propose a hypergraph based multi-agents representation learning (HMARL) to obtain agent embedding vectors, which can be used to classify agents in the same region and correlate the collected data of similar properties. To this end, the proposed HMARL transforms the multi-modal data into the same graph structure with nodes and their relations. Then, a hypergraph is constructed to integrate local graphs and a hypergraph random walk is applied to obtain the sequence of adjacent agents, which is used to train the agent embedding vectors. Experiments on public datasets are provided for similarity analysis on agents and their collected data.
- KSP 제안 키워드
- Graph structure, Public Datasets, Random walk, Representation learning, multi-agent, multimodal data, similarity analysis