| Title | Data Based Identification of Byzantine Robots for Collective Decision Making |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Petrenko, Vyacheslav, Tebueva, Fariza, Ryabtsev, Sergey, Antonov, Vladimir, Struchkov, Igor |
| Conference Name | 2022 13th Asian Control Conference (ASCC) |
| Keywords | Behavioral sciences, collective design making, composability, compositionality, Data models, decision making, Distributed Ledger Technology, Hardware, Information security, Observers, pubcrawl, swarm intelligence, swarm robotic system, swarm robotics |
| Abstract | The development of new types of technology actualizes the issues of ensuring their information security. The aim of the work is to increase the security of the collective decision-making process in swarm robotic systems from negative impacts by identifying malicious robots. It is proposed to use confidence in choosing an alternative when reaching a consensus as a criterion for identifying malicious robots - a malicious robot, having a special behavior strategy, does not fully take into account the signs of the external environment and information from other robots, which means that such a robot will change its mind with characteristic features for each malicious strategy, and its degree of confidence will be different from the usual voting robot. The modeling performed and the obtained experimental data on three types of malicious behavioral strategies demonstrate the possibility of using the degree of confidence to identify malicious robots. The advantages of the approach are taking into account a large number of alternatives and universality, which lies in the fact that the method is based on the mechanisms of collective decision-making, which proceed in the same way on various hardware platforms of swarm robotic systems. The proposed method can serve as a basis for the development of more complex security mechanisms in swarm robotic systems. |
| DOI | 10.23919/ASCC56756.2022.9828371 |
| Citation Key | petrenko_data_2022 |
Data Based Identification of Byzantine Robots for Collective Decision Making