29,414 research outputs found
Interactive Execution Monitoring of Agent Teams
There is an increasing need for automated support for humans monitoring the
activity of distributed teams of cooperating agents, both human and machine. We
characterize the domain-independent challenges posed by this problem, and
describe how properties of domains influence the challenges and their
solutions. We will concentrate on dynamic, data-rich domains where humans are
ultimately responsible for team behavior. Thus, the automated aid should
interactively support effective and timely decision making by the human. We
present a domain-independent categorization of the types of alerts a plan-based
monitoring system might issue to a user, where each type generally requires
different monitoring techniques. We describe a monitoring framework for
integrating many domain-specific and task-specific monitoring techniques and
then using the concept of value of an alert to avoid operator overload. We use
this framework to describe an execution monitoring approach we have used to
implement Execution Assistants (EAs) in two different dynamic, data-rich,
real-world domains to assist a human in monitoring team behavior. One domain
(Army small unit operations) has hundreds of mobile, geographically distributed
agents, a combination of humans, robots, and vehicles. The other domain (teams
of unmanned ground and air vehicles) has a handful of cooperating robots. Both
domains involve unpredictable adversaries in the vicinity. Our approach
customizes monitoring behavior for each specific task, plan, and situation, as
well as for user preferences. Our EAs alert the human controller when reported
events threaten plan execution or physically threaten team members. Alerts were
generated in a timely manner without inundating the user with too many alerts
(less than 10 percent of alerts are unwanted, as judged by domain experts)
Challenges in Collaborative HRI for Remote Robot Teams
Collaboration between human supervisors and remote teams of robots is highly
challenging, particularly in high-stakes, distant, hazardous locations, such as
off-shore energy platforms. In order for these teams of robots to truly be
beneficial, they need to be trusted to operate autonomously, performing tasks
such as inspection and emergency response, thus reducing the number of
personnel placed in harm's way. As remote robots are generally trusted less
than robots in close-proximity, we present a solution to instil trust in the
operator through a `mediator robot' that can exhibit social skills, alongside
sophisticated visualisation techniques. In this position paper, we present
general challenges and then take a closer look at one challenge in particular,
discussing an initial study, which investigates the relationship between the
level of control the supervisor hands over to the mediator robot and how this
affects their trust. We show that the supervisor is more likely to have higher
trust overall if their initial experience involves handing over control of the
emergency situation to the robotic assistant. We discuss this result, here, as
well as other challenges and interaction techniques for human-robot
collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019
Workshop: The Challenges of Working on Social Robots that Collaborate with
People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing
Systems, May 2019, Glasgow, U
Suitable task allocation in intelligent systems for assistive environments
The growing need of technological assistance to provide support to
people with special needs demands for systems more and more efficient and with better performances. With this aim, this work tries to advance in a multirobot platform that allows the coordinated control of different agents and other elements in the environment to achieve an autonomous behavior based on the user’s needs or will. Therefore, this environment is structured according to the potentiality of each agent and elements of this environment and of the dynamic context, to generate the adequate actuation plans and the coordination of their execution.Peer ReviewedPostprint (author's final draft
The archive solution for distributed workflow management agents of the CMS experiment at LHC
The CMS experiment at the CERN LHC developed the Workflow Management Archive
system to persistently store unstructured framework job report documents
produced by distributed workflow management agents. In this paper we present
its architecture, implementation, deployment, and integration with the CMS and
CERN computing infrastructures, such as central HDFS and Hadoop Spark cluster.
The system leverages modern technologies such as a document oriented database
and the Hadoop eco-system to provide the necessary flexibility to reliably
process, store, and aggregate (1M) documents on a daily basis. We
describe the data transformation, the short and long term storage layers, the
query language, along with the aggregation pipeline developed to visualize
various performance metrics to assist CMS data operators in assessing the
performance of the CMS computing system.Comment: This is a pre-print of an article published in Computing and Software
for Big Science. The final authenticated version is available online at:
https://doi.org/10.1007/s41781-018-0005-
Observation of large-scale multi-agent based simulations
The computational cost of large-scale multi-agent based simulations (MABS)
can be extremely important, especially if simulations have to be monitored for
validation purposes. In this paper, two methods, based on self-observation and
statistical survey theory, are introduced in order to optimize the computation
of observations in MABS. An empirical comparison of the computational cost of
these methods is performed on a toy problem
Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems
This paper presents a human-robot trust integrated task allocation and motion
planning framework for multi-robot systems (MRS) in performing a set of tasks
concurrently. A set of task specifications in parallel are conjuncted with MRS
to synthesize a task allocation automaton. Each transition of the task
allocation automaton is associated with the total trust value of human in
corresponding robots. Here, the human-robot trust model is constructed with a
dynamic Bayesian network (DBN) by considering individual robot performance,
safety coefficient, human cognitive workload and overall evaluation of task
allocation. Hence, a task allocation path with maximum encoded human-robot
trust can be searched based on the current trust value of each robot in the
task allocation automaton. Symbolic motion planning (SMP) is implemented for
each robot after they obtain the sequence of actions. The task allocation path
can be intermittently updated with this DBN based trust model. The overall
strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask
automata
Modelling the relationship between planning, control, perception and execution behaviours in interactive worksystems
This paper presents a model of planning carried out by interactive worksystems which attempts: 1. To describe the relationship between planning, control, perception and execution behaviours; 2. To make explicit how these may be distributed across the user and physically separate devices. Such a model, it is argued, is more suitable to support HCI design practice than theories of planning in cognitive science which focus on problem-solving methods and representations. To demonstrate the application of the model to work situations, it is illustrated by examples drawn from an observational study of secretarial office administration
Human-agent collectives
We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People’s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented
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