Demo Publications

@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}

}

@inproceedings{orchid175,

title = {Anytime Coalition Structure Generation on Synergy Graphs},

author = {Filippo Bistaffa and Alessandro Farinelli and Jesus Cerquides and Juan Antonio Rodriguez-Aguilar and Sarvapali D Ramchurn},

url = {http://aamas2014.lip6.fr/proceedings/aamas/p13.pdf},

year  = {2014},

date = {2014-01-01},

booktitle = {13th Int. Conf. on Autonomous Agents and Multi-Agent Systems},

pages = {13-20},

abstract = {We consider the coalition structure generation (CSG) problem on 

synergy graphs, which arises in many practical applications where 

communication constraints, social or trust relationships must be 

taken into account when forming coalitions. We propose a novel 

representation of this problem based on the concept of edge contraction, 

and an innovative branch and bound approach (CFSS), 

which is particularly efficient when applied to a general class of 

characteristic functions. This new model provides a non-redundant 

partition of the search space, hence allowing an effective parallelisation. 

We evaluate CFSS on two benchmark functions, the edge 

sum with coordination cost and the collective energy purchasing 

functions, comparing its performance with the best algorithm for 

CSG on synergy graphs: DyCE. The latter approach is centralised 

and cannot be efficiently parallelised due to the exponential memory 

requirements in the number of agents, which limits its scalability 

(while CFSS memory requirements are only polynomial). 

Our results show that, when the graphs are very sparse, CFSS is 

4 orders of magnitude faster than DyCE. Moreover, CFSS is the 

first approach to provide anytime approximate solutions with quality 

guarantees for very large systems (i.e., with more than 2700 

agents).},

keywords = {Coalition Formation, Coordination},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{orchid175,

title = {Anytime Coalition Structure Generation on Synergy Graphs},

author = {Filippo Bistaffa and Alessandro Farinelli and Jesus Cerquides and Juan Antonio Rodriguez-Aguilar and Sarvapali D Ramchurn},

url = {http://aamas2014.lip6.fr/proceedings/aamas/p13.pdf},

year  = {2014},

date = {2014-01-01},

booktitle = {13th Int. Conf. on Autonomous Agents and Multi-Agent Systems},

pages = {13-20},

abstract = {We consider the coalition structure generation (CSG) problem on 

synergy graphs, which arises in many practical applications where 

communication constraints, social or trust relationships must be 

taken into account when forming coalitions. We propose a novel 

representation of this problem based on the concept of edge contraction, 

and an innovative branch and bound approach (CFSS), 

which is particularly efficient when applied to a general class of 

characteristic functions. This new model provides a non-redundant 

partition of the search space, hence allowing an effective parallelisation. 

We evaluate CFSS on two benchmark functions, the edge 

sum with coordination cost and the collective energy purchasing 

functions, comparing its performance with the best algorithm for 

CSG on synergy graphs: DyCE. The latter approach is centralised 

and cannot be efficiently parallelised due to the exponential memory 

requirements in the number of agents, which limits its scalability 

(while CFSS memory requirements are only polynomial). 

Our results show that, when the graphs are very sparse, CFSS is 

4 orders of magnitude faster than DyCE. Moreover, CFSS is the 

first approach to provide anytime approximate solutions with quality 

guarantees for very large systems (i.e., with more than 2700 

agents).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}