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2015
Tran-Thanh, Avi Rosenfeld Trung Dong Huynh Long
Crowdsourcing Complex Workflows under Budget Constraints Proceedings Article
In: Proceedings of the AAAI Conference, AAAI, 2015.
Abstract | Links | BibTeX | Tags: Applications, crowdsourcing, human-agent collectives
@inproceedings{tranh:Etal:2015,
title = {Crowdsourcing Complex Workflows under Budget Constraints},
author = {Avi Rosenfeld Trung Dong Huynh Long Tran-Thanh},
url = {http://eprints.soton.ac.uk/372107/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
publisher = {AAAI},
abstract = {We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.},
keywords = {Applications, crowdsourcing, human-agent collectives},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali
Real-time opinion aggregation methods for crowd robotics Proceedings Article
In: Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015.
Abstract | Links | BibTeX | Tags: crowdsourcing, uav
@inproceedings{eps375287,
title = {Real-time opinion aggregation methods for crowd robotics},
author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/375287/},
year = {2015},
date = {2015-01-01},
booktitle = {Autonomous Agents and Multiagent Systems (AAMAS 2015)},
abstract = {Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios},
keywords = {crowdsourcing, uav},
pubstate = {published},
tppubtype = {inproceedings}
}
Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
HAC-ER: A disaster response system based on human-agent collectives Proceedings Article
In: 14th International Conference on Autonomous Agents and Multi-Agent Systems, 2015.
Abstract | Links | BibTeX | Tags: Coordination, crowdsourcing, human-agent collectives, human-agent interaction, multi-agent systems, uav
@inproceedings{eps374070,
title = {HAC-ER: A disaster response system based on human-agent collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/374070/},
year = {2015},
date = {2015-01-01},
booktitle = {14th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.},
keywords = {Coordination, crowdsourcing, human-agent collectives, human-agent interaction, multi-agent systems, uav},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.
2012
Ebden, Mark; Huynh, Trung Dong; Moreau, Luc; Ramchurn, Sarvapali; Stephen, Roberts
Network analysis on provenance graphs from a crowdsourcing application Proceedings Article
In: Groth, Paul; Frew, James (Ed.): 4th International Provenance and Annotation Workshop, pp. 168–182, 2012.
Abstract | Links | BibTeX | Tags: collabmap, crowdsourcing, densification, evacuation, graph diameters, maps, network analysis, node degree, provenance, provenance graphs
@inproceedings{eps340068,
title = {Network analysis on provenance graphs from a crowdsourcing application},
author = {Mark Ebden and Trung Dong Huynh and Luc Moreau and Sarvapali Ramchurn and Roberts Stephen},
editor = {Paul Groth and James Frew},
url = {http://eprints.soton.ac.uk/340068/},
year = {2012},
date = {2012-01-01},
booktitle = {4th International Provenance and Annotation Workshop},
volume = {7525},
pages = {168–182},
series = {0302-9743},
abstract = {Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.},
keywords = {collabmap, crowdsourcing, densification, evacuation, graph diameters, maps, network analysis, node degree, provenance, provenance graphs},
pubstate = {published},
tppubtype = {inproceedings}
}
Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.
1.
Tran-Thanh, Avi Rosenfeld Trung Dong Huynh Long
Crowdsourcing Complex Workflows under Budget Constraints Proceedings Article
In: Proceedings of the AAAI Conference, AAAI, 2015.
@inproceedings{tranh:Etal:2015,
title = {Crowdsourcing Complex Workflows under Budget Constraints},
author = {Avi Rosenfeld Trung Dong Huynh Long Tran-Thanh},
url = {http://eprints.soton.ac.uk/372107/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
publisher = {AAAI},
abstract = {We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.
2.
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali
Real-time opinion aggregation methods for crowd robotics Proceedings Article
In: Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015.
@inproceedings{eps375287,
title = {Real-time opinion aggregation methods for crowd robotics},
author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/375287/},
year = {2015},
date = {2015-01-01},
booktitle = {Autonomous Agents and Multiagent Systems (AAMAS 2015)},
abstract = {Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios
3.
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
HAC-ER: A disaster response system based on human-agent collectives Proceedings Article
In: 14th International Conference on Autonomous Agents and Multi-Agent Systems, 2015.
@inproceedings{eps374070,
title = {HAC-ER: A disaster response system based on human-agent collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/374070/},
year = {2015},
date = {2015-01-01},
booktitle = {14th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.
4.
Ebden, Mark; Huynh, Trung Dong; Moreau, Luc; Ramchurn, Sarvapali; Stephen, Roberts
Network analysis on provenance graphs from a crowdsourcing application Proceedings Article
In: Groth, Paul; Frew, James (Ed.): 4th International Provenance and Annotation Workshop, pp. 168–182, 2012.
@inproceedings{eps340068,
title = {Network analysis on provenance graphs from a crowdsourcing application},
author = {Mark Ebden and Trung Dong Huynh and Luc Moreau and Sarvapali Ramchurn and Roberts Stephen},
editor = {Paul Groth and James Frew},
url = {http://eprints.soton.ac.uk/340068/},
year = {2012},
date = {2012-01-01},
booktitle = {4th International Provenance and Annotation Workshop},
volume = {7525},
pages = {168–182},
series = {0302-9743},
abstract = {Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.
2015
Tran-Thanh, Avi Rosenfeld Trung Dong Huynh Long
Crowdsourcing Complex Workflows under Budget Constraints Proceedings Article
In: Proceedings of the AAAI Conference, AAAI, 2015.
Abstract | Links | BibTeX | Tags: Applications, crowdsourcing, human-agent collectives
@inproceedings{tranh:Etal:2015,
title = {Crowdsourcing Complex Workflows under Budget Constraints},
author = {Avi Rosenfeld Trung Dong Huynh Long Tran-Thanh},
url = {http://eprints.soton.ac.uk/372107/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
publisher = {AAAI},
abstract = {We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.},
keywords = {Applications, crowdsourcing, human-agent collectives},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali
Real-time opinion aggregation methods for crowd robotics Proceedings Article
In: Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015.
Abstract | Links | BibTeX | Tags: crowdsourcing, uav
@inproceedings{eps375287,
title = {Real-time opinion aggregation methods for crowd robotics},
author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/375287/},
year = {2015},
date = {2015-01-01},
booktitle = {Autonomous Agents and Multiagent Systems (AAMAS 2015)},
abstract = {Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios},
keywords = {crowdsourcing, uav},
pubstate = {published},
tppubtype = {inproceedings}
}
Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
HAC-ER: A disaster response system based on human-agent collectives Proceedings Article
In: 14th International Conference on Autonomous Agents and Multi-Agent Systems, 2015.
Abstract | Links | BibTeX | Tags: Coordination, crowdsourcing, human-agent collectives, human-agent interaction, multi-agent systems, uav
@inproceedings{eps374070,
title = {HAC-ER: A disaster response system based on human-agent collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/374070/},
year = {2015},
date = {2015-01-01},
booktitle = {14th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.},
keywords = {Coordination, crowdsourcing, human-agent collectives, human-agent interaction, multi-agent systems, uav},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.
2012
Ebden, Mark; Huynh, Trung Dong; Moreau, Luc; Ramchurn, Sarvapali; Stephen, Roberts
Network analysis on provenance graphs from a crowdsourcing application Proceedings Article
In: Groth, Paul; Frew, James (Ed.): 4th International Provenance and Annotation Workshop, pp. 168–182, 2012.
Abstract | Links | BibTeX | Tags: collabmap, crowdsourcing, densification, evacuation, graph diameters, maps, network analysis, node degree, provenance, provenance graphs
@inproceedings{eps340068,
title = {Network analysis on provenance graphs from a crowdsourcing application},
author = {Mark Ebden and Trung Dong Huynh and Luc Moreau and Sarvapali Ramchurn and Roberts Stephen},
editor = {Paul Groth and James Frew},
url = {http://eprints.soton.ac.uk/340068/},
year = {2012},
date = {2012-01-01},
booktitle = {4th International Provenance and Annotation Workshop},
volume = {7525},
pages = {168–182},
series = {0302-9743},
abstract = {Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.},
keywords = {collabmap, crowdsourcing, densification, evacuation, graph diameters, maps, network analysis, node degree, provenance, provenance graphs},
pubstate = {published},
tppubtype = {inproceedings}
}
Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.
1.
Tran-Thanh, Avi Rosenfeld Trung Dong Huynh Long
Crowdsourcing Complex Workflows under Budget Constraints Proceedings Article
In: Proceedings of the AAAI Conference, AAAI, 2015.
@inproceedings{tranh:Etal:2015,
title = {Crowdsourcing Complex Workflows under Budget Constraints},
author = {Avi Rosenfeld Trung Dong Huynh Long Tran-Thanh},
url = {http://eprints.soton.ac.uk/372107/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
publisher = {AAAI},
abstract = {We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.
2.
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali
Real-time opinion aggregation methods for crowd robotics Proceedings Article
In: Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015.
@inproceedings{eps375287,
title = {Real-time opinion aggregation methods for crowd robotics},
author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/375287/},
year = {2015},
date = {2015-01-01},
booktitle = {Autonomous Agents and Multiagent Systems (AAMAS 2015)},
abstract = {Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios
3.
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
HAC-ER: A disaster response system based on human-agent collectives Proceedings Article
In: 14th International Conference on Autonomous Agents and Multi-Agent Systems, 2015.
@inproceedings{eps374070,
title = {HAC-ER: A disaster response system based on human-agent collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/374070/},
year = {2015},
date = {2015-01-01},
booktitle = {14th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.
4.
Ebden, Mark; Huynh, Trung Dong; Moreau, Luc; Ramchurn, Sarvapali; Stephen, Roberts
Network analysis on provenance graphs from a crowdsourcing application Proceedings Article
In: Groth, Paul; Frew, James (Ed.): 4th International Provenance and Annotation Workshop, pp. 168–182, 2012.
@inproceedings{eps340068,
title = {Network analysis on provenance graphs from a crowdsourcing application},
author = {Mark Ebden and Trung Dong Huynh and Luc Moreau and Sarvapali Ramchurn and Roberts Stephen},
editor = {Paul Groth and James Frew},
url = {http://eprints.soton.ac.uk/340068/},
year = {2012},
date = {2012-01-01},
booktitle = {4th International Provenance and Annotation Workshop},
volume = {7525},
pages = {168–182},
series = {0302-9743},
abstract = {Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.
Selected Publications
Multi-agent signal-less intersection management with dynamic platoon formation
No data available.
https://eprints.soton.ac.uk/478647/
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response
No data available.
https://eprints.soton.ac.uk/477553/
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The effect of data visualisation quality and task density on human-swarm interaction
No data available.
https://eprints.soton.ac.uk/479970/
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Demonstrating performance benefits of human-swarm teaming
Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.
https://eprints.soton.ac.uk/479903/
@inproceedings{soton479903,
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2015
Tran-Thanh, Avi Rosenfeld Trung Dong Huynh Long
Crowdsourcing Complex Workflows under Budget Constraints Proceedings Article
In: Proceedings of the AAAI Conference, AAAI, 2015.
@inproceedings{tranh:Etal:2015,
title = {Crowdsourcing Complex Workflows under Budget Constraints},
author = {Avi Rosenfeld Trung Dong Huynh Long Tran-Thanh},
url = {http://eprints.soton.ac.uk/372107/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
publisher = {AAAI},
abstract = {We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali
Real-time opinion aggregation methods for crowd robotics Proceedings Article
In: Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015.
@inproceedings{eps375287,
title = {Real-time opinion aggregation methods for crowd robotics},
author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/375287/},
year = {2015},
date = {2015-01-01},
booktitle = {Autonomous Agents and Multiagent Systems (AAMAS 2015)},
abstract = {Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
HAC-ER: A disaster response system based on human-agent collectives Proceedings Article
In: 14th International Conference on Autonomous Agents and Multi-Agent Systems, 2015.
@inproceedings{eps374070,
title = {HAC-ER: A disaster response system based on human-agent collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/374070/},
year = {2015},
date = {2015-01-01},
booktitle = {14th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.
2012
Ebden, Mark; Huynh, Trung Dong; Moreau, Luc; Ramchurn, Sarvapali; Stephen, Roberts
Network analysis on provenance graphs from a crowdsourcing application Proceedings Article
In: Groth, Paul; Frew, James (Ed.): 4th International Provenance and Annotation Workshop, pp. 168–182, 2012.
@inproceedings{eps340068,
title = {Network analysis on provenance graphs from a crowdsourcing application},
author = {Mark Ebden and Trung Dong Huynh and Luc Moreau and Sarvapali Ramchurn and Roberts Stephen},
editor = {Paul Groth and James Frew},
url = {http://eprints.soton.ac.uk/340068/},
year = {2012},
date = {2012-01-01},
booktitle = {4th International Provenance and Annotation Workshop},
volume = {7525},
pages = {168–182},
series = {0302-9743},
abstract = {Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.