Enhancement Belief Function with Ontology to Solve the Conflict for the System of Systems

Abstract

System of Systems (SoS) is a huge system consisting of small stand-alone systems to offer unique abilities. In such complex construction, managing achievement of SoS objectives along with those of its Component Systems (CS) is a challenge. Whereas the integration of CS may cause an extended impact issue called conflict. In this research, the principles of basic belief function were used to measure conflict in the SoS. This is intended to detect conflicts between CS in an attempt to resolve it. Moreover, we have based on the ontology in the SoS integration. In addition to the identification of relationships between the CS semantically. We have proposed a method to resolve conflicts called Ontology Belief Function System of Systems (OBFSoS). As a homogenous SoS, we apply the OBFSoS on Real-lime Strategy (RTS) game. We depend on the use of a learning agent. It reduces conflicts and saves time due to attempts. For execution, we got the result of 10 attempts with about 30 systems (players) in the SoS. It giving a good decision in a short time with an accuracy of approximately 87%.

Enhancing semantic belief function to handle decision conflicts in SoS using k-means clustering

“…SoS is constructed from many CS, which conflicts among them cause SoS to be inefficient. In 2019, researchers (Younes, Ahmed & Elsayed, 2019) have adopted a method to get the best Decision for SoS while avoiding conflicts between CS. The algorithm used was ontology with the Belief Function equation that modified to suit systems.…”

“…The proposed method SSBFCSoS considers an enhancement of the OBFSoS method (Younes, Ahmed & Elsayed, 2019). Both methods have adopted the concept of better decision making for SoS by addressing conflicts between CS.…”

“…It is the first part of the SSBFCSoS algorithm. Conflicts are detected and addressed within each subsystem in the OBFSoS algorithm (Younes, Ahmed & Elsayed, 2019), then comes the second part of the clustering method; aggregating all S-SoSs into G-SoS, in stages that mainly depend on the number of S-SoSs.…”

“…One of these instances and the experimental setup used for our empirical analysis is described. This case has been selected and set up to match previous work (Younes, Ahmed & Elsayed, 2019). Ontology is useful for systems with similar components.…”