Abstract
In this work, we investigate the use of Answer Set Programming (ASP) as a component in a multi-layered situation awareness system for the marine traffic domain. The State Transition Data Fusion (STDF) model which has been adopted for the situation assessment task enables performing each of the tasks at an appropriate level of abstraction. In this model, we delegate the lower-level analysis to an imperative modelling language called CoreASM; while the higher-level analysis for the impact assessment is handled through a reactive ASP system. The reactive answer set solver enables using dynamic input data to generate answer sets in an incremental fashion. Furthermore, ASP has a rich potential in representing domain rules as it is declarative and provides a compact and intuitive encoding of the domain expert’s knowledge within a non-monotonic framework.
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Vaseqi, Z., Delgrande, J. (2013). An Application of Answer Set Programming for Situational Analysis in a Maritime Traffic Domain. In: Zaïane, O.R., Zilles, S. (eds) Advances in Artificial Intelligence. Canadian AI 2013. Lecture Notes in Computer Science(), vol 7884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38457-8_32
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DOI: https://doi.org/10.1007/978-3-642-38457-8_32
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