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%.

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Background The endeavouring to offer complex special functions from individual systems gave rise to what is known as the System of Systems (SoS). SoS co-integrating systems together while allowing for absorbing more systems in the future. SoS as an integrated system simplifies operations, reduces costs, and ensures efficiency. However, conflict may result while co-integrating systems, violating the main benefits of SoS. This paper is concerned with enhancing the time required to detect and solve such conflicts. Methods We adopted the k-means clustering technique to enhance the detection and solving of conflict resulting while co-integrating new systems into an existing SoS. Instead of dealing with SoS as a single entity, we partition it into clusters. Each cluster contains nearby systems according to pre-specified criteria. We can consider each cluster a Sub SoS (S-SoS). By doing so, the conflict that may arise while co-integrating new systems can be detected and solved in a shorter time. We propose the Smart Semantic Belief Function Clustered System of Systems (SSBFCSoS), which is an enhancement of the Ontology Belief Function System of Systems (OBFSoS). Results The proposed method proved the ability to rapidly detect and resolve conflicts. It showed the ability to accommodate more systems as well, therefore achieving the objectives of SoS. In order to test the applicability of the SSBFCSoS and compare its performance with other approaches, two datasets were employed. They are (Glest & StarCraft Brood War). With each dataset, 15 test cases were examined. We achieved, on average, 89% in solving the conflict compared to 77% for other approaches. Moreover, it showed an acceleration of up to proportionality over previous approaches for about 16% in solving conflicts as well. Besides, it reduced the frequency of the same conflicts by approximately 23% better than the other method, not only in the same cluster but even while combining different clusters.

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