# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 2 Related Work  
The concept of context-aware framework emerged in the 1990s when the first attempt to build context-aware frameworks focused on exploiting location data [19]. Thorough analyses of existing context-aware frameworks have been provided by a number of surveys [7; 9; 10; 20; 21]. Existing context-aware frameworks have been proposed mainly for Smart Homes, Smart Cities, Ambient Assisted Living, Smart Grids, etc. Introducing the technique of context-aware framework into the field of underwater robots is under-researched [12]. Only a few attempts, including Pandora [22], Trident [23], and CoCoRo [24], have been presented for delivering context awareness in underwater robots. However, they differ from each other in terms of context awareness level and they present unsatisfactory performance due to their lack of important data treatments [9]. Specifically, Pandora and Trident employ ontologies to represent contexts but fail to provide effective context reasoning to generate high-level contexts. CoCoRo does not include proper capabilities for modeling context data in a formalized manner so it considerably limits the potential of transparent context sharing and lacks support for context reasoning. Efficient context distribution methods are not considered in the Pandora and Trident context-aware frameworks. In addition, neither of them takes into account context uncertainty, either in the context modeling or context reasoning phase. Different from them, the context-aware framework proposed in the SWARMs project aims to provide a complete management covering the whole lifecycle of context awareness for underwater robots.  
Efforts at the managing uncertainty in context information have been made in the recent literature. For instance, [PERSON] and [PERSON] [25] summarized approaches to manage uncertainty and vagueness in description logics for the semantic web. [PERSON] et al. [26] used Bayesian theories to manage context uncertainties; a similar approach was adopted by [PERSON] et al. [27]. An approach to assess the ambiguity of context data was proposed by [28] using probabilities and fuzzy logic Probabilistic Datalog was employed to add probabilities and rules to Web Ontology Language (OWL) [29] Lite subsets in order to represent and infer context uncertainties [30]. In particular, focusing on the context reasoning phase, it is noted that all individual reasoning techniques have different weaknesses, explicitly described in [8; 13]. For instance, ontological and rule-based reasoning could not handle uncertainty in their reasoning process. The importance of employing multiple reasoning techniques to mitigate individual drawbacks by using others' advantages is highlighted [8; 31]. For example, [PERSON] et al. [32] applied ontological and BN reasoning to derive high-level contexts toward marine awareness. A combination of ontological and rule-based reasoning was adopted to intuit user activities [33]. [PERSON] et al. [34] also introduced fuzzy logic into ontologies to provide a combined reasoning. The emerging probabilistic reasoning method, MEBN, which is based on BN reasoning, shows good performance in reasoning over uncertainties in some domains, including procurement fraud detection [35], maritime awareness [36], knowledge-driven analysis for cultural heritage [37], and Robocup Soccer [38]. In the SWARMs project, a hybrid context reasoning mechanism is needed to deal with reasoning under uncertainties. Thus, this paper presents a proposal for applying ontological, rule-based, and MEBN reasoning to the SWARMs project. The combination of three different context reasoning methods could meet the different reasoning requirements in the underwater robot field. Notably, the applicability of the MEBN reasoning to reason about uncertain contexts for underwater robots is explored in this paper.