# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 6 Conclusions and Future Work  
This paper has presented a conceptual proposal of a context-aware framework for underwater robots. The proposed context-aware framework aims to provide complete context management and is devoted to facilitating context awareness in the SWARMs project. The framework offers different data treatments for underwater robots, such as pre-processing context data, modeling heterogeneous contexts in ontologies, reasoning about contexts for more useful information, and disseminating contexts. Different logic components within the framework, including ontology model, data processor, semantic mapper, rules creator, semantic query, and context reasoner, have been described along with their main functionalities and interactions. In the underwater robot field, context reasoning, as a significant phase in realizing context awareness, is more challenging because the context data obtained by robots are prone to be uncertain. To tackle the challenge of reasoning under uncertainties, a hybrid context reasoning mechanism has been proposed to be used in the context reasoner component. Three reasoning methods, ontological, rule-based, and MEBN reasoning, have been proposed to use in the context reasoner so that it can provide different reasoning strategies to meet the different reasoning requirements. Ontological reasoning is able to provide deductive capabilities, such as context consistency and accuracy checking. Rule-based reasoning is more flexible and simpler than ontological reasoning, and enables the insertion of user-defined rules and inferring more useful information. However, neither ontological nor rule-based reasoning can deal with the uncertainties that are inherent to context data in the underwater robot field. Therefore, an emerging probabilistic reasoning method, MEBN, has been incorporated into the context reasoner in order to provide inferential functionalities dedicated to uncertain contexts. The hybrid context reasoning method has been described in detail and the specific process for using different context reasoning methods has been specified. A case study on oil spill detection has been presented to verify the usefulness and applicability of the hybrid context reasoning mechanism. The simulated results have shown that in theory the hybrid context reasoning method can make logical inferences in certain contexts and also probabilistically reason under uncertainties in the underwater robot field. Through this case study, a theoretical foundation of applying the proposed hybrid context reasoning method in the context reasoner component has been provided and has indicated that the hybrid method can be a promising approach to implement in the context reasoner component. Future work could focus on the following aspects:  
* The presented context-aware framework is still at the prototype level. Completing its implementation and testing it in real scenarios will be the next step. * Though the proposed context-aware framework is designed specifically for underwater robotics, it does present a generic solution for managing context and providing context awareness. Therefore, applying it to other robotic domains (e.g., on land and air) and application fields (e.g., smart homes and smart agriculture) could be a potential extension of its usage. * Other uncertainty reasoning algorithms, such as fuzzy logic and the Markov logic network, will be thoroughly studied. The possibilities of integrating them into the proposed reasoning mechanism for enhancement will be explored. * Context processing and reasoning are normally time-consuming and resource-intensive. How to achieve a trade-off between the load on managing context data and the actual benefit obtained from high-level context needs to be determined. The research leading to the presented results has been undertaken within the SWARMs European project (Smart and Networking Underwater Robots in Cooperation Meshes), under Grant AgreementNo.