NASA LogoNTRSNTRS - NASA Technical Reports ServerSearchmore_vertCollectionsAboutNewsHelpLogin Back to ResultsNASA Pilot-Engaged Expert Response Using IBM Watson Technology: Prototype Evaluation of Knowledge Retrieval SystemNASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.Document ID20180007515Acquisition SourceLangley Research CenterDocument TypeContractor Report (CR)AuthorsKatz, Graham*(International Business Machines Corp. Herndon, VA, United States)*Ding, Chengmin*(International Business Machines Corp. Herndon, VA, United States)*Doyle, Andrew*(International Business Machines Corp. Herndon, VA, United States)*Date AcquiredNovember 6, 2018Publication DateOctober 1, 2018Subject CategoryDocumentation And Information ScienceAeronautics (General)Report/Patent NumberNASA/CR-2018-220097NF1676L-31064Funding Number(s)WBS: WBS 340428.04.90.07.08CONTRACT_GRANT: NNG15SC15BDistribution LimitsPublicCopyrightPublic Use Permitted.KeywordsMachine learningData scienceWatson Discovery AdvisorMachine intelligenceWatsonBig dataData analysisDataCognitive computingArtificial intelligenceAvailable DownloadsNameType20180007515.pdfSTIcloud_downloadcontent_copyvisibilityRelated RecordsThere are no records associated with this record.visibility_offNo Preview Available
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