An official website of the United States government
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.
more »
« less
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.
more »
« less
- 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
More Like this
-
-
Connected and automated vehicles (CAVs) provide various valuable and advanced services to manufacturers, owners, mobility service providers, and transportation authorities. As a result, a large number of CAV applications have been proposed to improve the safety, mobility, and sustainability of the transportation system. With the increasing connectivity and automation, cybersecurity of the connected and automated transportation system (CATS) has raised attention to the transportation community in recent years. Vulnerabilities in CAVs can lead to breakdowns in the transportation system and compromise safety (e.g., causing crashes), performance (e.g., increasing congestion and reducing capacity), and fairness (e.g., vehicles fooling traffic signals). This paper presents our perspective on CATS cybersecurity via surveying recent pertinent studies focusing on the transportation system level, ranging from individual and multiple vehicles to the traffic network (including infrastructure). It also highlights threat analysis and risk assessment (TARA) tools and evaluation platforms, particularly for analyzing the CATS cybersecurity problem. Finally, this paper will provide valuable insights into developing secure CAV applications and investigating remaining open cybersecurity challenges that must be addressed.more » « less
-
null (Ed.)This paper studies congestion-aware route- planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on- demand mobility under mixed traffic conditions. Specifically, we first devise a network flow model to optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. Second, we capture reactive exogenous traffic consisting of private vehicles selfishly adapting to the AMoD flows in a user- centric fashion by leveraging an iterative approach. Finally, we showcase the effectiveness of our framework with a case- study considering the transportation sub-network in New York City. Our results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows, whilst the combination of AMoD with walking or micromobility options can significantly improve the overall system performance.more » « less
-
Vehicle mobility data is significant for many types of smart city applications such as smart transportation, logistics, urban planning, and carbon dioxide emissions reduction. Particularly, microscopic mobility data, in which the location, direction, and speed of each vehicle are included, is promising for cyber-physical systems services and applications. Nevertheless, there are only limited datasets available to the public due to the difficulty of collecting the data from each real vehicle due to cost, privacy, and many other reasons. To address the issue, this position paper introduces our challenge of generating microscopic mobility data using traffic simulator as well as publicly available statistical and measured traffic data in Japan. We hope this approach contributes to those researchers and service designers to move beyond the limitation that comes from the microscopic mobility dataset unavailability.more » « less
-
Kariz is a new architecture for caching data from datalakes accessed, potentially concurrently, by multiple analytic platforms. It integrates rich information from analytics platforms with global knowledge about demand and resource availability to enable sophisticated cache management and prefetching strategies that, for example, combine historical run time information with job dependency graphs (DAGs), information about the cache state and sharing across compute clusters. Our prototype supports multiple analytic frameworks (Pig/Hadoop and Spark), and we show that the required changes are modest. We have implemented three algorithms in Kariz for optimizing the caching of individual queries (one from the literature, and two novel to our platform) and three policies for optimizing across queries from, potentially, multiple different clusters. With an algorithm that fully exploits the rich information available from Kariz, we demonstrate major speedups (as much as 3×) for TPC-H and TPC-DS.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SMARTCOMP.2018.00029
