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Effective teamwork depends on teammates’ ability to maintain common ground: mutual knowledge about the relevant state of the world and the relevant status of teammates’ actions and plans. This ability integrates diverse skills of reasoning and communication: agents can track common ground by recognizing and registering public updates to ongoing activity, but when this evidence is incomplete, agents may need to describe what they are doing or ask what others are doing. In this paper, we introduce an architecture for integrating these diverse skills to maintain common ground in human–AI teamwork. Our approach offers unique advantages of simplicity, modularity, and extensibility by leveraging generic tools for plan recognition, planning, natural language understanding and generation, and dialogue management. Worked examples illustrate how linguistic and practical reasoning complement each other in the realization of key interactive skills.
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Mobilytics- An Extensible, Modular and Resilient Mobility Platform
Transportation management platforms provide communities the ability to integrate the available mobility options and localized transportation demand management policies. A central component of a transportation management platform is the mobility planning application. Given the societal relevance of these platforms, it is necessary to ensure that they operate resiliently. Modularity and extensibility are also critical properties that are required for manageability. Modularity allows to isolate faults easily. Extensibility enables update of policies and integration of new mobility modes or new routing algorithms. However, state of the art mobility planning applications like open trip planner, are monolithic applications, which makes it difficult to scale and modify them dynamically. This paper describes a microservices based modular multi-modal mobility platform Mobilytics, that integrates mobility providers, commuters, and community stakeholders. We describe our requirements, architecture, and discuss the resilience challenges, and how our platform functions properly in presence of failure. Conceivably, the patterns and principles manifested in our system can serve as guidelines for current and future practitioners in this field.
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- Award ID(s):
- 1647015
- PAR ID:
- 10075930
- Date Published:
- Journal Name:
- IEEE SmartComp
- Page Range / eLocation ID:
- 356 to 361
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SMARTCOMP.2018.00029
