Artificial Intelligence for the Early Design Phases of Space Missions

Abstract

Recent introduction of data mining methods has led to a paradigm shift in the way we can analyze space data. This paper demonstrates that Artificial Intelligence (AI), and especially the field of Knowledge Representation and Reasoning (KRR), could also be successfully employed at the start of the space mission life cycle via an Expert System (ES) used as a Design Engineering Assistant (DEA). An ES is an AI-based agent used to solve complex problems in particular fields. There are many examples of ES being successfully implemented in the aeronautical, agricultural, legal or medical fields. Applied to space mission design, and in particular, in the context of concurrent engineering sessions, an ES could serve as a knowledge engine and support the generation of the initial design inputs, provide easy and quick access to previous design decisions or push to explore new design options. Integrated to the User design environment, the DEA could become an active assistant following the design iterations and flagging model inconsistencies. Today, for space missions design, experts apply methods of concurrent engineering and Model-Based System Engineering, relying both on their implicit knowledge (i.e., past experiences, network) and on available explicit knowledge (i.e., past reports, publications, data sheets). The former knowledge type represents still the most significant amount of data, mostly unstructured, non-digital or digital data of various legacy formats. Searching for information through this data is highly time-consuming. A solution is to convert this data into structured data to be stored into a Knowledge Graph (KG) that can be traversed by an inference engine to provide reasoning and deductions on its nodes. Knowledge is extracted from the KG via a User Interface (UI) and a query engine providing reliable and relevant knowledge summaries to the Human experts. The DEA project aims to enhance the productivity of experts by providing them with new insights into a large amount of data accumulated in the field of space mission design. Natural Language Processing (NLP), Machine Learning (ML), Knowledge Management (KM) and Human-Machine Interaction (HMI) methods are leveraged to develop the DEA. Building the knowledge base manually is subjective, time-consuming, laborious and error bound. This is why the knowledge base generation and population rely on Ontology Learning (OL) methods. This OL approach follows a modified model of the Ontology Layer Cake. This paper describes the approach and the parameters used for the qualitative trade-off for the selection of the software to be adopted in the architecture of the ES. The study also displays the first results of the multi-word extraction and highlights the importance of Word Sense Disambiguation for the identification of synonyms in the context. This paper includes the detailed software architecture of both front and back-ends, as well as the tool requirements. Both architectures and requirements were refined after a set of interviews with experts from the European Space Agency. The paper finally presents the preliminary strategy to quantify and mitigate uncertainties within the ES.