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Appl. Sci. 2017, 7(2), 183; doi:10.3390/app7020183

Towards a Hybrid Approach to Context Reasoning for Underwater Robots

Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM), Campus Sur, Technical University of Madrid, 28031 Madrid, Spain
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Academic Editor: Antonio Fernández-Caballero
Received: 23 December 2016 / Revised: 3 February 2017 / Accepted: 8 February 2017 / Published: 15 February 2017
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Abstract

Ontologies have been widely used to facilitate semantic interoperability and serve as a common information model in many applications or domains. The Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project, aiming to facilitate coordination and cooperation between heterogeneous underwater vehicles, also adopts ontologies to formalize information that is necessarily exchanged between vehicles. However, how to derive more useful contexts based on ontologies still remains a challenge. In particular, the extreme nature of the underwater environment introduces uncertainties in context data, thus imposing more difficulties in context reasoning. None of the existing context reasoning methods could individually deal with all intricacies in the underwater robot field. To this end, this paper presents the first proposal applying a hybrid context reasoning mechanism that includes ontological, rule-based, and Multi-Entity Bayesian Network (MEBN) reasoning methods to reason about contexts and their uncertainties in the underwater robot field. The theoretical foundation of applying this reasoning mechanism in underwater robots is given by a case study on the oil spill monitoring. The simulated reasoning results are useful for further decision-making by operators or robots and they show that the consolidation of different reasoning methods is a promising approach for context reasoning in underwater robots. View Full-Text
Keywords: context reasoning; uncertainty; Multi-Entity Bayesian Network (MEBN); underwater robots; ontology; context awareness context reasoning; uncertainty; Multi-Entity Bayesian Network (MEBN); underwater robots; ontology; context awareness
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, X.; Martínez, J.-F.; Rubio, G. Towards a Hybrid Approach to Context Reasoning for Underwater Robots. Appl. Sci. 2017, 7, 183.

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