Daphne: An Intelligent Assistant for Architecting Earth Observing Satellite Systems

Bang, Hyunseung; Martin, Antoni Viros; Prat, Arnau; Selva, Daniel

doi:10.2514/6.2018-1366

Cited by 16 publications

(11 citation statements)

References 36 publications

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“…Most research in AI-supported design hews to the perspective of functional equivalence. For example, systems help humans evaluate designs [25], answer queries about a design space [5], and make suggestions [31]. Interactive genetic Figure 1: Users of our interface can partition a state into districts using an interactive map (top left).…”

Section: What Should An Intelligent Design Agent Do?mentioning

confidence: 99%

Design Intention Inference for Virtual Co-Design Agents

Law

Kwatra

Dhawan

et al. 2020

Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents

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We address the challenge of inferring the design intentions of a human by an intelligent virtual agent that collaborates with the human. First, we propose a dynamic Bayesian network model that relates design intentions, objectives, and solutions during a human's exploration of a problem space. We then train the model on design behaviors generated by a search agent and use the model parameters to infer the design intentions in a test set of real human behaviors. We find that our model is able to infer the exact intentions across three objectives associated with a sequence of design outcomes 31.3% of the time. Inference accuracy is 50.9% for the top two predictions and 67.2% for the top three predictions. For any singular intention over an objective, the model's mean F1-score is 0.719. This provides a reasonable foundation for an intelligent virtual agent to infer design intentions purely from design outcomes toward establishing joint intentions with a human designer. These results also shed light on the potential benefits and pitfalls in using simulated data to train a model for human design intentions. CCS CONCEPTS • Human-centered computing → Human computer interaction (HCI).

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Section: What Should An Intelligent Design Agent Do?mentioning

confidence: 99%

Design Intention Inference for Virtual Co-Design Agents

Law

Kwatra

Dhawan

et al. 2020

Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents

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show abstract

“…All backends and data sources can run independently of one another and we are currently in the process of making the whole system run in different machines, making Daphne scale horizontally, as the microservices design allows for. These software components are described in detail in both [1] and [2], but summaries of all of them are provided below for completeness.…”

Section: Daphne: System Architecture Overviewmentioning

confidence: 99%

“…Different features and versions of Daphne have been described both in [1] and [2]. This article will describe the current version of Daphne as it applies to DSM design, including: 1) how the user can interact with Daphne, both in traditional ways and using the VA functionality; 2) how the system processes user requests and sends them to different roles; 3) how all those roles can interact directly with the user through the use of the new proactive functionalities described in [2].…”

mentioning

confidence: 99%

Daphne: A Virtual Assistant for Designing Earth Observation Distributed Spacecraft Missions

Martin

Selva

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This article describes Daphne, a virtual assistant for designing Earth observation distributed spacecraft missions. It is, to the best of our knowledge, the first virtual assistant for such application. The article provides a thorough description of Daphne, including its question answering system and the main features we have implemented to help system engineers design distributed spacecraft missions. In addition, the article describes a study performed at NASA's Jet Propulsion Laboratory (JPL) to assess the usefulness of Daphne in this use case. The study was conducted with N = 9 subjects from JPL, who were asked to work on a mission design task with two versions of Daphne, one that was fully featured implementing the cognitive assistance functions, and one that only had the features one would find in a traditional design space exploration tool. After the task, they filled out a standard user experience survey, completed a test to assess how much they learned about the task, and were asked a number of questions in a semi-structured exit interview. Results of the study suggest that Daphne can help improve performance during system design tasks compared to traditional tools, while keeping the system usable. However, the study also raises some concerns with respect to a potential reduction in human learning due to the use of the cognitive assistant. The article ends with a list of suggestions for future development of virtual assistants for space mission design. Index Terms-Distributed spacecraft missions, earth observation, machine learning, mixed initiative, virtual assistant. I. INTRODUCTION M OTIVATED by the challenges of system architecture in general and architecting distributed satellite missions (DSM) in particular, and inspired by the success of commercial virtual assistants (VA), such as Siri, Google Assistant, Alexa, or Mycroft, we have developed Daphne, the first VA-to the best of our knowledge-to support the high-level design of DSM. This article describes how Daphne can be used to design DSM and includes a quantitative validation study performed at NASA JPL where the test subjects had expertise in mission design. The contribution of this article is two fold. First, we introduce Daphne as a complete, open-source package for DSM tradespace analysis that includes the standard VA functionality, such as Manuscript

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“…Most of these works are essentially targeted toward procedural content generation in computer games. While there are recent advances in aerospace design domains such as the Daphne system [34], there is currently little known about how human-computer co-creativity could be materialized for engineering and product design domains in early conceptualization. Our work is a step toward filling this knowledge gap.…”

Section: Identification Of Gapsmentioning

confidence: 99%

Mini-Map: Mixed-Initiative Mind-Mapping via Contextual Query Expansion

Chen

Subramanian

Krishnamurthy

2019

AIAA Scitech 2019 Forum

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We present Mini-Map, a novel web-based workflow for mixed-initiative mind-mapping using query expansions enabled by ConceptNet-a large graph-based representation of "commonsense" knowledge. Mind-maps are now widely recognized as thinking tools for creative tasks such as conceptual design. Nonetheless, creating a mind-map can be challenging especially for beginners either due to low domain knowledge or personal inhibition. Despite extensive knowledge databases on the web, there is currently limited computational support to augment users' ability to fully explore the vast information sources available at their disposal. Our work demonstrates that by using the rich relational ontology offered by ConceptNet, we can faithfully emulate human-like collaborative behavior toward useful exploration of ideas in early design. We present a comparative analysis of human-human collaborative mind-mapping and the Mini-Map workflow.

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Daphne: An Intelligent Assistant for Architecting Earth Observing Satellite Systems

Cited by 16 publications

References 36 publications

Design Intention Inference for Virtual Co-Design Agents

Design Intention Inference for Virtual Co-Design Agents

Daphne: A Virtual Assistant for Designing Earth Observation Distributed Spacecraft Missions

Mini-Map: Mixed-Initiative Mind-Mapping via Contextual Query Expansion

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