A vision on hybrid AI for military applications

Judith Dijk; Klamer Schutte; Serena Oggero

doi:10.1117/12.2551893

23 October 2019 A vision on hybrid AI for military applications

Judith Dijk, Klamer Schutte, Serena Oggero

Proceedings Volume 11169, Artificial Intelligence and Machine Learning in Defense Applications; 111690D (2019) https://doi.org/10.1117/12.2551893
Event: SPIE Security + Defence, 2019, Strasbourg, France

Abstract

Application of different Artificial Intelligence technologies is increasing over the past couple of years. At a high conceptual level, we can divide these technologies in two different categories: symbolic and sub-symbolic. The term “Hybrid AI” denotes the combination of symbolic and sub-symbolic AI. By combining both semantic reasoning and data-driven machine learning both human specified and data derived knowledge can be combined in one system. In this paper we explore the concept of Hybrid AI by the hand of architectural patterns from literature. The added value of the architectural patterns is that they provide a way to discuss the different elements in the processing pipeline. They stimulate discussion what the input and output of the different processing blocks are, and how they work together. When applying the available design patterns to real military imaging applications, we noticed that we needed more detail in the different blocks to specify the type of data or algorithms that are applied. In future work we will investigate how components such as online learning can be presented in this design pattern framework. We identified the need to further develop this approach with a more intertwined interaction between the reasoning and the data-driven part of the pipelines, and use more world knowledge, domain knowledge and relations between objects in the reasoning part. Improvements are also needed for online learning, where the knowledge of the system performance will be used to ask the users relevant information.

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Judith Dijk, Klamer Schutte, and Serena Oggero "A vision on hybrid AI for military applications", Proc. SPIE 11169, Artificial Intelligence and Machine Learning in Defense Applications, 111690D (23 October 2019); https://doi.org/10.1117/12.2551893

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