Robots equipped with artificial intelligence technology include learning functions. Purely inductive learning methods formulate general hypotheses by finding empirical regularities over the trainning examples. Purely analytical methods use prior knowledge to derive general hypotheses deductively. Therefore, when the physical environment of a robot is complex, there is a problem of increased computational time required for information processing. In particular, when a large number of robots transmit information, more computational time is required for information processing. The distance-based topological method proposed in this paper first constructs the topology based on the distances between robots, and then generates information weights according to the stages of the topology. The technique proposed in this paper has been experimentally confirmed to have excellent performance in environments with a large number of robots and complex physical conditions.

Abstract
1. 서 론
2. 물리 적 환경의 정보 통합
    2.1 토폴로지
    2.2 정보 통합
3. 실험결과
    3.1 실험환경
    3.2 실험분석
5. 결 론
References

• 김형일(School of IT, Korea Nazarene University) | Hyung-Il Kim Corresponding author