Publications

@inproceedings{cailliere:hal-01329606,

title = {Managing energy markets in future smart grids using bilateral contracts},

author = {Romain Calliere and Samir Aknine and Antoine Nongaillard and Sarvapali Ramchurn},

url = {https://hal.archives-ouvertes.fr/hal-01329606},

year  = {2016},

date = {2016-01-01},

booktitle = {European Conference on Artificial Intelligence (ECAI)},

address = {The Hague, Netherlands},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/smartgridcomm/KoufakisRBR16,

title = {Towards an optimal EV charging scheduling scheme with V2G and 

 V2V energy transfer},

author = {Alexandros - and Emmanouil S Rigas and Nick Bassiliades and Sarvapali D Ramchurn},

url = {https://doi.org/10.1109/SmartGridComm.2016.7778778},

doi = {10.1109/SmartGridComm.2016.7778778},

year  = {2016},

date = {2016-01-01},

booktitle = {2016 IEEE International Conference on Smart Grid Communications, 

 SmartGridComm 2016, Sydney, Australia, November 6-9, 2016},

pages = {302–307},

crossref = {DBLP:conf/smartgridcomm/2016},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps382948,

title = {CrowdAR: augmenting live video with a real-time crowd},

author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn},

url = {http://eprints.soton.ac.uk/382948/},

year  = {2015},

date = {2015-11-01},

booktitle = {HCOMP 2015: Third AAAI Conference on Human Computation and Crowdsourcing},

abstract = {Finding and tracking targets and events in a live video feed is important for many commercial applications, from CCTV surveillance used by police and security firms, to the rapid mapping of events from aerial imagery. However, descriptions of targets are typically provided in natural language by the end users, and interpreting these in the context of a live video stream is a complex task. Due to current limitations in artificial intelligence, especially vision, this task cannot be automated and instead requires human supervision. Hence, in this paper, we consider the use of real-time crowdsourcing to identify and track targets given by a natural language description. In particular we present a novel method for augmenting live video with a real-time crowd.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bistaffaetal2015,

title = {Recommending Fair Payments for Large-Scale Social Ridesharing},

author = {Alessandro Farinelli Georgios Chalkiadakis Filippo Bistaffa and Sarvapali D. Ramchurn},

url = {https://www.sramchurn.com/wp-content/uploads/2017/02/2015recsys.pdf},

year  = {2015},

date = {2015-09-16},

booktitle = {ACM Conference on Recommender Systems (Recsys)},

keywords = {Coalition Formation, mas, Ridesharing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps378696,

title = {Managing energy tariffs with agents: a field study of a future smart energy system at home},

author = {Alper T. Alan and Enrico Costanza and Sarvapali Ramchurn and Joel Fischer and Tom Rodden and N. R. Jennings},

url = {http://eprints.soton.ac.uk/378696/},

year  = {2015},

date = {2015-07-01},

booktitle = {Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps378696b,

title = {Managing energy tariffs with agents: a field study of a future smart energy system at home},

author = {Alper T. Alan and Enrico Costanza and Sarvapali Ramchurn and Joel Fischer and Tom Rodden and N. R. Jennings},

url = {http://eprints.soton.ac.uk/378696/},

year  = {2015},

date = {2015-07-01},

booktitle = {Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (Ubicomp)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{fischer:etal:2015,

title = {Building a Bird's Eye View: Collaborative Work},

author = {Tom Rodden Stuart Reeves Joel E. Fischer and David Jones},

url = {https://www.sramchurn.com/wp-content/uploads/2015/01/pn1018-fischerA.pdf},

year  = {2015},

date = {2015-05-01},

booktitle = {Proceedings of SIGCHI (To appear)},

keywords = {Disaster Management},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{bistaffa:etal:2015,

title = {Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing},

author = {Sarvapali D. Ramchurn Alessandro Farinelli Filippo Bistaffa},

url = {http://eprints.soton.ac.uk/372048/},

year  = {2015},

date = {2015-01-25},

booktitle = {Proceedings of the AAAI Conference},

abstract = {We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).},

keywords = {Coalition Formation, optimisation, Ridesharing, Social Networks},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zhao:etal:2015,

title = {Balanced Trade Reduction for Dual-Role Exchange Markets},

author = {Enrico H. Gerding Sarvapali D. Ramchurn Dengji Zhao and Nicholas R. Jennings},

url = {http://eprints.soton.ac.uk/372050/},

year  = {2015},

date = {2015-01-25},

booktitle = {Proceedings of the AAAI Conference},

abstract = {We consider dual-role exchange markets, where traders can offer to both buy and sell the same commodity in the exchange but, if they transact, they can only be either a buyer or a seller, which is determined by the market mechanism. To design desirable mechanisms for such exchanges, we show that existing solutions may not be incentive compatible, and more importantly, cause the market maker to suffer a significant deficit. Hence, to combat this problem, following McAfee’s trade reduc- tion approach, we propose a new trade reduction mech- anism, called balanced trade reduction, that is incen- tive compatible and also provides flexible trade-offs be- tween efficiency and deficit.},

keywords = {Electric Vehicles, Energy, Game Theory, mechanism design, Ridesharing},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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{eps376618,

title = {A scalable, decentralised multi-issue negotiation protocol for energy exchange},

author = {Muddasser Alam and Enrico H. Gerding and Alex Rogers and Sarvapali D. Ramchurn},

url = {http://eprints.soton.ac.uk/376618/},

year  = {2015},

date = {2015-01-01},

booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},

abstract = {We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our protocol is concurrent, scalable and; under certain conditions; leads to Pareto-optimal outcomes.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps377186,

title = {Agile Planning for Real-World Disaster Response},

author = {Feng Wu and Sarvapali Ramchurn and Wenchao Jiang and Joel Fischer and Tom Rodden and Nicholas R. Jennings},

url = {http://eprints.soton.ac.uk/377186/},

year  = {2015},

date = {2015-01-01},

booktitle = {International Joint Conference on Artificial Intelligence},

abstract = {We consider a setting where an agent-based planner instructs teams of human emergency responders to perform tasks in the real world. Due to uncertainty in the environment and the inability of the planner to consider all human preferences and all attributes of the real-world, humans may reject plans computed by the agent. A na??ve solution that replans given a rejection is inefficient and does not guarantee the new plan will be acceptable. Hence, we propose a new model re-planning problem using a Multi-agent Markov Decision Process that integrates potential rejections as part of the planning process and propose a novel algorithm to efficiently solve this new model. We empirically evaluate our algorithm and show that it outperforms current benchmarks. Our algorithm is also shown to perform better in pilot studies with real humans.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps377185,

title = {A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments},

author = {Sarvapali Ramchurn and Joel Fischer and Yuki Ikuno and Feng Wu and Jack Flann and Antony Waldock},

url = {http://eprints.soton.ac.uk/377185/},

year  = {2015},

date = {2015-01-01},

booktitle = {International Joint Conference on Artificial Intelligence},

abstract = {We consider a setting where a team of humans oversee the coordination of multiple Unmanned Aerial Vehicles (UAVs) to perform a number of search tasks in dynamic environments that may cause the UAVs to drop out. Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting. To elucidate the resulting interactional issues, we compare manual and mixed-initiative task allocation in both static and dynamic environments in lab studies with 40 participants and observe that our mixed-initiative system results in lower workloads and better performance in re-planning tasks than one which only involves manual task allocation. Our analysis points to new insights into the way humans appropriate flexible autonomy.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Holyhead:2015:CTR:2768510.2768531,

title = {Consumer Targeting in Residential Demand Response Programmes},

author = {James C. Holyhead and Sarvapali D. Ramchurn and Alex Rogers},

url = {http://doi.acm.org/10.1145/2768510.2768531},

isbn = {978-1-4503-3609-3},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems},

pages = {7–16},

publisher = {ACM},

address = {Bangalore, India},

series = {e-Energy '15},

keywords = {Energy, optimisation, smart grid},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps360820,

title = {A field study of human-agent interaction for electricity tariff switching},

author = {Alper Alan and Enrico Costanza and J. Fischer and Sarvapali Ramchurn and T. Rodden and N. R. Jennings},

url = {http://eprints.soton.ac.uk/360820/},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems},

abstract = {Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.},

keywords = {electricity, Energy, hai, hci, human-agent interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps361173,

title = {Doing the laundry with agents: a field trial of a future smart energy system in the home},

author = {Enrico Costanza and Joel E Fischer and James A Colley and Tom Rodden and Sarvapali Ramchurn and Nicholas R. Jennings},

url = {http://eprints.soton.ac.uk/361173/},

year  = {2014},

date = {2014-01-01},

booktitle = {ACM CHI Conference on Human Factors in Computing Systems 2014},

pages = {813–822},

organization = {ACM},

keywords = {Energy, hai},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps362321,

title = {BudgetFix: budget limited crowdsourcing for interdependent task allocation with quality guarantees},

author = {Long Tran-Thanh and Trung Dong Huynh and A Rosenfield and Sarvapali Ramchurn and Nicholas R. Jennings},

url = {http://eprints.soton.ac.uk/362321/},

year  = {2014},

date = {2014-01-01},

booktitle = {13th International Conference on Autonomous Agents and Multi-Agent Systems},

publisher = {International Foundation for Autonomous Agents and Multiagent Systems},

keywords = {hai, mas},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{orchid192,

title = {Supporting Team Coordination on the Ground: Requirements from a Mixed Reality Game.},

author = {J. E. Fischer and W Jiang and A Kerne and C Greenhalgh and Sarvapali D Ramchurn and Steven Reece and N Pantidi and T Rodden},

year  = {2014},

date = {2014-01-01},

booktitle = {11th Int. Conference on the Design of Cooperative Systems (COOP ?14)},

howpublished = {http://www.orchid.ac.uk/eprints/192/1/COOP2014-Fischer-author-version.pdf},

keywords = {Applications, Disaster Recovery, Flexible Autonomy, human-agent interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{orchid191,

title = {Social Implications of Agent-based Planning Support for Human Teams.},

author = {W Jiang and J. E. Fischer and C Greenhalgh and Sarvapali D Ramchurn and Feng Wu and Nicholas R Jennings and T Rodden},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 Int. Conference on Collaboration Technologies and Systems},

howpublished = {http://www.orchid.ac.uk/eprints/191/1/CTS2014-Jiang-author-version.pdf},

keywords = {Applications, Disaster Recovery, Human-Computer Interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{orchid176,

title = {Coalition Structure Generation with the Graphics Processing Unit},

author = {Krzysztof Pawlowski and Karol Kurach and Kim Svensson and Sarvapali D Ramchurn and Tomasz Michalak and Talal Rahwan},

year  = {2014},

date = {2014-01-01},

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

keywords = {Agile Teaming, mechanism design},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps365510,

title = {Data quality assessment from provenance graphs},

author = {Trung Dong Huynh and Mark Ebden and Sarvapali Ramchurn and Stephen Roberts and Luc Moreau},

url = {http://eprints.soton.ac.uk/365510/},

year  = {2014},

date = {2014-01-01},

booktitle = {Provenance Analytics 2014},

abstract = {Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer data quality. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analyzing data based on the network metrics of provenance graphs to learn about such patterns and to relate them to data quality in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein.},

keywords = {analytics, data quality, machine learning, network metrics, provenance},

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{eps357187,

title = {Towards a smart home framework},

author = {Muddasser Alam and Alper Alan and Alex Rogers and Sarvapali D. Ramchurn},

url = {http://eprints.soton.ac.uk/357187/},

year  = {2013},

date = {2013-01-01},

booktitle = {5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys2013)},

abstract = {We present our Smart Home Framework (SHF) which simplifies the modelling, prototyping and simulation of smart infrastructure (i.e., smart home and smart communities). It provides the buildings blocks (e.g., home appliances) that can be extended and assembled together to build a smart infrastructure model to which appropriate AI techniques can be applied. This approach enables rapid modelling where new research initiatives can build on existing work.},

keywords = {Energy, simulation, smart home},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps346637,

title = {Cooperative energy exchange for the efficient use of energy and resources in remote communities. [Winner, Best Student Paper Award at AAMAS2013]},

author = {Muddasser Alam and Sarvapali Ramchurn and Alex Rogers},

url = {http://eprints.soton.ac.uk/346637/},

year  = {2013},

date = {2013-01-01},

booktitle = {Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},

keywords = {battery, cooperative exchange, energy exchange, off-grid, storage},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps350941,

title = {Interdependent multi-issue negotiation for energy exchange in remote communities},

author = {Muddasser Alam and Alex Rogers and Sarvapali Ramchurn},

url = {http://eprints.soton.ac.uk/350941/},

year  = {2013},

date = {2013-01-01},

booktitle = {Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13)},

abstract = {We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes in a decentralised manner. We empirically evaluate our protocol and show that, in this instance, a society of agents can (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%},

keywords = {complex negotiation, Energy, energy exchange, interdependent issues, mas, multi-agent systems, remote communities, smart home},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps357186,

title = {Interdependent multi-issue negotiation for energy exchange in remote communities},

author = {Muddasser Alam and Alex Rogers and Sarvapali D. Ramchurn},

url = {http://eprints.soton.ac.uk/357186/},

year  = {2013},

date = {2013-01-01},

booktitle = {International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE)},

keywords = {cooperative exchange, Energy, home energy management, mas, Multi-agent scheduling, smart home},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps351521,

title = {C-Link: a hierarchical clustering approach to large-scale near-optimal coalition formation},

author = {Alessandro Farinelli and Manuele Bicego and Sarvapali Ramchurn and Marco Zuchelli},

url = {http://eprints.soton.ac.uk/351521/},

year  = {2013},

date = {2013-01-01},

booktitle = {23rd International Joint Conference on Artificial Intelligence},

publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},

abstract = {Coalition formation is a fundamental approach to multi-agent coordination. In this paper we address the specific problem of coalition structure generation, and focus on providing good-enough solutions using a novel heuristic approach that is based on data clustering methods. In particular, we propose a hierarchical agglomerative clustering approach (C-Link), which uses a similarity criterion between coalitions based on the gain that the system achieves if two coalitions merge. We empirically evaluate C-Link on a synthetic benchmark data-set as well as in collective energy purchasing settings. Our results show that the C-link approach performs very well against an optimal benchmark based on Mixed-Integer Programming, achieving solutions which are in the worst case about 80% of the optimal (in the synthetic data-set), and 98% of the optimal (in the energy data-set). Thus we show that C-Link can return solutions for problems involving thousands of agents within minutes.},

keywords = {Energy, mas},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps346991,

title = {Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling},

author = {Joel E. Fischer and Sarvapali D. Ramchurn and Michael A. Osborne and Oliver Parson and Trung Dong Huynh and Muddasser Alam and Nadia Pantidi and Stuart Moran and Khaled Bachour and Steven Reece and Enrico Costanza and Tom Rodden and Nicholas R. Jennings},

url = {http://eprints.soton.ac.uk/346991/},

year  = {2013},

date = {2013-01-01},

booktitle = {International Conference on Intelligent User Interfaces},

pages = {383–394},

abstract = {We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.},

keywords = {Energy, hai, home energy management, human-agent interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps357199,

title = {Interpretation of Crowdsourced Activities Using Provenance Network Analysis},

author = {Trung Dong Huynh and Mark Ebden and Matteo Venanzi and Sarvapali Ramchurn and Stephen Roberts and Luc Moreau},

url = {http://eprints.soton.ac.uk/357199/},

year  = {2013},

date = {2013-01-01},

booktitle = {The First AAAI Conference on Human Computation and Crowdsourcing},

publisher = {Association for the Advancement of Artificial Intelligence},

abstract = {Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analysing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein.},

keywords = {hai, mas, multi-agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps350678,

title = {RMASBench: a benchmarking system for multi-agent coordination in urban search and rescue},

author = {Alexander Kleiner and Alessandro Farinelli and Sarvapali Ramchurn and Bing Shi and Fabio Mafioletti and Riccardo Refatto},

url = {http://eprints.soton.ac.uk/350678/},

year  = {2013},

date = {2013-01-01},

booktitle = {International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2013)},

abstract = {This demonstration paper illustrates RMASBench, a new benchmarking system based on the RoboCup Rescue Agent simulator. The aim of the system is to facilitate benchmarking of coordination approaches in controlled settings for dynamic rescue scenario. In particular, the key features of the systems are: i) programming interfaces to plug-in coordination algorithms without the need for implementing and tuning low-level agents? behaviors, ii) implementations of state-of-the art coordination approaches: DSA and MaxSum, iii) a large scale crowd simulator, which exploits GPUs parallel architecture, to simulate the behaviour of thousands of agents in real time.},

keywords = {Disaster Management, mas, multi-agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps349815,

title = {AgentSwitch: towards smart electricity tariff selection},

author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},

url = {http://eprints.soton.ac.uk/349815/},

year  = {2013},

date = {2013-01-01},

booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},

publisher = {International Foundation for Autonomous Agents and Multiagent Systems},

abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},

keywords = {electricity, Energy, group buying, optimisation, provenance, recommender systems, smart grid},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps350677,

title = {Collabmap: crowdsourcing maps for emergency planning},

author = {Sarvapali D. Ramchurn and Trung Dong Huynh and Matteo Venanzi and Bing Shi},

url = {http://eprints.soton.ac.uk/350677/},

year  = {2013},

date = {2013-01-01},

booktitle = {The 5th Annual ACM Web Science Conference},

pages = {326–335},

abstract = {In this paper, we present a software tool to help emergency planners at Hampshire County Council in the UK to create maps for high-fidelity crowd simulations that require evacuation routes from buildings to roads. The main feature of the system is a crowdsourcing mechanism that breaks down the problem of creating evacuation routes into microtasks that a contributor to the platform can execute in less than a minute. As part of the mechanism we developed a concensus-based trust mechanism that filters out incorrect contributions and ensures that the individual tasks are complete and correct. To drive people to contribute to the platform, we experimented with different incentive mechanisms and applied these over different time scales, the aim being to evaluate what incentives work with different types of crowds, including anonymous contributors from Amazon Mechanical Turk. The results of the 'in the wild' deployment of the system show that the system is effective at engaging contributors to perform tasks correctly and that users respond to incentives in different ways. More specifically, we show that purely social motives are not good enough to attract a large number of contributors and that contributors are averse to the uncertainty in winning rewards. When taken altogether, our results suggest that a combination of incentives may be the best approach to harnessing the maximum number of resources to get socially valuable tasks (such for planning applications) performed on a large scale.},

keywords = {mas},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps356081,

title = {Congestion management for urban EV charging systems},

author = {Emmanouil Rigas and Sarvapali Ramchurn and Nick Bassiliades and Georgios Koutitas},

url = {http://eprints.soton.ac.uk/356081/},

year  = {2013},

date = {2013-01-01},

booktitle = {4th IEEE International Conference on Smart Grid Communications (SmartGridComm)},

volume = {4},

publisher = {IEEE},

abstract = {We consider the problem of managing Electric Vehicle (EV) charging at charging points in a city to ensure that the load on the charging points remains within the desired limits while minimizing the inconvenience to EV owners. We develop solutions that treat charging points and EV users as self-interested agents that aim to maximize their profit and minimize the impact on their schedule. In particular, we propose variants of a decentralised and dynamic approach as well as an optimal centralised static approach. We evaluated these solutions in a real setting based on the road network and the location of parking garages of a UK city and show that the optimal centralised (non-dynamic) solution manages the congestion the best but does not scale well, while the decentralised solutions scale to thousands of agents.},

keywords = {Energy, mas},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps352204,

title = {Solving the coalition structure generation problem on a GPU},

author = {Kim Svensson and Sarvapali Ramchurn and Francisco Cruz and Juan-Antonio Rodriguez-Aguilar and Jesus Cerquides},

url = {http://eprints.soton.ac.uk/352204/},

year  = {2013},

date = {2013-01-01},

booktitle = {6th International Workshop on Optimisation in Multi-Agent Systems},

abstract = {We develop the first parallel algorithm for Coalition Structure Generation (CSG), which is central to many multi-agent systems applications. Our approach involves distributing the key steps of a dynamic programming approach to CSG across computational nodes on a Graphics Processing Unit (GPU) such that each of the thousands of threads of computation can be used to perform small computations that speed up the overall process. In so doing, we solve important challenges that arise in solving combinatorial optimisation problems on GPUs such as the efficient allocation of memory and computational threads to every step of the algorithm. In our empirical evaluations on a standard GPU, our results show an improvement of orders of magnitude over current dynamic programming approaches with an ever increasing divergence between the CPU and GPU-based algorithms in terms of growth. Thus, our algorithm is able to solve the CSG problem for 29 agents in one hour and thirty minutes as opposed to three days for the current state of the art dynamic programming algorithms.},

keywords = {Coalition Formation, mas, Multi-agent scheduling},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps351242,

title = {Forecasting multi-appliance usage for smart home energy management},

author = {Ngoc Cuong Truong and James McInerney and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},

url = {http://eprints.soton.ac.uk/351242/},

year  = {2013},

date = {2013-01-01},

booktitle = {23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)},

keywords = {Energy, hai, home energy management, human-agent interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps351238,

title = {Activity prediction for agent-based home energy management},

author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and D. Sarvapali Ramchurn},

url = {http://eprints.soton.ac.uk/351238/},

year  = {2013},

date = {2013-01-01},

booktitle = {Agent Technologies for Energy Systems (ATES 2013)},

keywords = {Energy, hai},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps351240,

title = {Towards appliance usage prediction for home energy management},

author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},

url = {http://eprints.soton.ac.uk/351240/},

year  = {2013},

date = {2013-01-01},

booktitle = {ACM E-Energy 2013},

keywords = {home energy management, usage prediction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eps338804,

title = {Understanding domestic energy consumption through interactive visualisation: a field study},

author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},

url = {http://eprints.soton.ac.uk/338804/},

year  = {2012},

date = {2012-01-01},

booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},

pages = {216–225},

abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},

keywords = {electricity, Energy, hai, home energy management, human-agent interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{soton446459,

title = {Responsibility ascription in trustworthy autonomous systems},

author = {Vahid Yazdanpanah and Enrico H Gerding and Sebastian Stein and Mehdi Dastani and Catholijn M Jonker and Timothy J Norman and Sarvapali D Ramchurn},

url = {https://eprints.soton.ac.uk/446459/},

year  = {2021},

date = {2021-02-01},

booktitle = {Embedding AI in Society (18/02/21 - 19/02/21)},

abstract = {To develop and effectively deploy Trustworthy Autonomous Systems (TAS), we face various social, technological, legal, and ethical challenges in which different notions of responsibility can play a key role. In this work, we elaborate on these challenges, discuss research gaps, and show how the multidimensional notion of responsibility can play a key role to bridge them. We argue that TAS requires operational tools to represent and reason about the responsibilities of humans as well as AI agents.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{soton445607,

title = {Combining machine learning and human experts to predict match outcomes in football: A baseline model},

author = {Ryan James Beal and Stuart Middleton and Timothy Norman and Sarvapali Ramchurn},

url = {https://eprints.soton.ac.uk/445607/},

year  = {2021},

date = {2021-02-01},

booktitle = {The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (02/02/21 - 09/02/21)},

abstract = {In this paper, we present a new application-focused benchmark dataset and results from a set of baseline Natural Language Processing and Machine Learning models for prediction of match outcomes for games of football (soccer). By doing so we give a baseline for the prediction accuracy that can be achieved exploiting both statistical match data and contextual articles from human sports journalists. Our dataset is focuses on a representative time-period over 6 seasons of the English Premier League, and includes newspaper match previews from The Guardian. The models presented in this paper achieve an accuracy of 63.18% showing a 6.9% boost on the traditional statistical methods.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{soton447983,

title = {Decentralised control of intelligent devices: a healthcare facility study},

author = {Sacha Lhopital and Samir Aknine and Sarvapali Ramchurn and Vincent Thavonekham and Huan Vu},

editor = {Nick Bassiliades and Georgios Chalkiadakis and Dave Jonge},

url = {https://eprints.soton.ac.uk/447983/},

year  = {2021},

date = {2021-01-01},

booktitle = {Multi-Agent Systems and Agreement Technologies - 17th European Conference, EUMAS 2020, and 7th International Conference, AT 2020, Revised Selected Papers},

volume = {12520 LNAI},

pages = {20–36},

publisher = {Springer},

abstract = {ensuremath<pensuremath>We present a novel approach to the management of notifications from devices in a healthcare setting. We employ a distributed constraint optimisation (DCOP) approach to the delivery of notification for healthcare assistants that aims to preserve the privacy of patients while reducing the intrusiveness of such notifications. Our approach reduces the workload of the assistants and improves patient safety by automating task allocation while ensuring high priority needs are addressed in a timely manner. We propose and evaluate several DCOP models both in simulation and in real-world deployments. Our models are shown to be efficient both in terms of computation and communication costs.ensuremath</pensuremath>},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{soton449655,

title = {Optimising long-term outcomes using real-world fluent objectives: an application to football},

author = {James Beal Ryan and Georgios Chalkiadakis and Timothy Norman and Sarvapali Ramchurn},

url = {https://eprints.soton.ac.uk/449655/},

year  = {2021},

date = {2021-01-01},

booktitle = {20th International Conference on Autonomous Agents and Multiagent Systems (03/05/21 - 07/05/21)},

pages = {196–204},

abstract = {In this paper, we present a novel approach for optimising long-term tactical and strategic decision-making in football (soccer) by encapsulating events in a league environment across a given time frame. ensuremath<br/ensuremath>We model the teams' objectives for a season and track how these evolve as games unfold to give a fluent objective that can aid in decision-making games. We develop Markov chain Monte Carlo and deep learning-based algorithms that make use of the fluent objectives in order to learn from prior games and other games in the environment and increase the teams? long-term performance. Simulations of our approach using real-world datasets from 760 matches shows that by using optimised tactics with our fluent objective and prior games, we can on average increase teams mean expected finishing distribution in the league by up to 35.6%.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{soton449656,

title = {What happened next? Using deep learning to value defensive actions in football event-data},

author = {Charbel Merhej and James Beal Ryan and Tim Matthews and Sarvapali Ramchurn},

url = {https://eprints.soton.ac.uk/449656/},

year  = {2021},

date = {2021-01-01},

booktitle = {KDD 2021 (14/08/21 - 18/08/21)},

pages = {3394–3403},

abstract = {Objectively quantifying the value of player actions in football (soccer) is a challenging problem. To date, studies in football analytics have mainly focused on the attacking side of the game, while there has been less work on event-driven metrics for valuing defensive actions (e.g., tackles and interceptions). Therefore in this paper, we use deep learning techniques to define a novel metric that values such defensive actions by studying the threat of passages of play that preceded them. By doing so, we are able to value defensive actions based on what they prevented from happening in the game. Our Defensive Action Expected Threat (DAxT) model has been validated using real-world event-data from the 2017/2018 and 2018/2019 English Premier League seasons, and we combine our model outputs with additional features to derive an overall rating of defensive ability for players. Overall, we find that our model is able to predict the impact of defensive actions allowing us to better value defenders using event-data.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{soton446412,

title = {Mechanism design for efficient allocation of electric vehicles to charging stations},

author = {Emmanouil S Rigas and Enrico Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades},

url = {https://eprints.soton.ac.uk/446412/},

year  = {2020},

date = {2020-09-01},

booktitle = {SETN 2020: 11th Hellenic Conference on Artificial Intelligence},

pages = {10–15},

abstract = {The electrification of transport can significantly reduce CO2 emissions and their negative impact on the environment. In this paper, we study the problem of allocating Electric Vehicles (EVs) to charging stations and scheduling their charging. We develop an offline solution that treats EV users as self-interested agents that aim to maximise their profit and minimise the impact on their schedule. We formulate the problem of the optimal EV to charging station allocation as a Mixed Integer Programming (MIP) one and we propose two pricing mechanisms: A fixed-price one, and another that is based on the well known Vickrey-Clark-Groves (VCG) mechanism. We observe that the VCG mechanism services on average 1.5% more EVs than the fixed-price one. In addition, when the stations get congested, VCG leads to higher prices for the EVs and higher profit for the stations, but lower utility for the EVs. However, the VCG mechanism guarantees truthful reporting of the EVs? preferences.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aaai/BealCNR20,

title = {Learning the Value of Teamwork to Form Efficient Teams},

author = {Ryan Beal and Narayan Changder and Timothy D Norman and Sarvapali D Ramchurn},

url = {https://aaai.org/ojs/index.php/AAAI/article/view/6192},

year  = {2020},

date = {2020-01-01},

booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 

 2020, The Thirty-Second Innovative Applications of Artificial Intelligence 

 Conference, IAAI 2020, The Tenth AAAI Symposium on Educational 

 Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 

 February 7-12, 2020},

pages = {7063–7070},

publisher = {AAAI Press},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aaai/ChangderARD20,

title = {ODSS: Efficient Hybridization for Optimal Coalition Structure Generation},

author = {Narayan Changder and Samir Aknine and Sarvapali D Ramchurn and Animesh Dutta},

url = {https://aaai.org/ojs/index.php/AAAI/article/view/6194},

year  = {2020},

date = {2020-01-01},

booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 

 2020, The Thirty-Second Innovative Applications of Artificial Intelligence 

 Conference, IAAI 2020, The Tenth AAAI Symposium on Educational 

 Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 

 February 7-12, 2020},

pages = {7079–7086},

publisher = {AAAI Press},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/atal/BealCNR20,

title = {Optimising Game Tactics for Football},

author = {Ryan Beal and Georgios Chalkiadakis and Timothy J Norman and Sarvapali D Ramchurn},

editor = {Amal El Fallah Seghrouchni and Gita Sukthankar and Bo An and Neil Yorke -},

url = {https://dl.acm.org/doi/abs/10.5555/3398761.3398783},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the 19th International Conference on Autonomous Agents 

 and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 

 2020},

pages = {141–149},

publisher = {International Foundation for Autonomous Agents and Multiagent Systems},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/atal/OluwasujiM0R20,

title = {Solving the Fair Electric Load Shedding Problem in Developing Countries},

author = {Olabambo I Oluwasuji and Obaid Malik and Jie Zhang and Sarvapali D Ramchurn},

editor = {Amal El Fallah Seghrouchni and Gita Sukthankar and Bo An and Neil Yorke -},

url = {https://dl.acm.org/doi/abs/10.5555/3398761.3399108},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the 19th International Conference on Autonomous Agents 

 and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 

 2020},

pages = {2155–2157},

publisher = {International Foundation for Autonomous Agents and Multiagent Systems},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/ijcai/0001R20a,

title = {Monte-Carlo Tree Search for Scalable Coalition Formation},

author = {Feng Wu and Sarvapali D Ramchurn},

editor = {Christian Bessiere},

url = {https://doi.org/10.24963/ijcai.2020/57},

doi = {10.24963/ijcai.2020/57},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on 

 Artificial Intelligence, IJCAI 2020},

pages = {407–413},

publisher = {ijcai.org},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/setn/RigasG0RB20,

title = {Mechanism design for efficient allocation of electric vehicles to 

 charging stations},

author = {Emmanouil S Rigas and Enrico Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades},

editor = {Constantine D Spyropoulos and Iraklis Varlamis and Ion Androutsopoulos and Prodromos Malakasiotis},

url = {https://dl.acm.org/doi/10.1145/3411408.3411434},

year  = {2020},

date = {2020-01-01},

booktitle = {SETN 2020: 11th Hellenic Conference on Artificial Intelligence, 

 Athens, Greece, September 2-4, 2020},

pages = {10–15},

publisher = {ACM},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}