2023
Krook, Joshua; Williams, Jennifer; Seabrooke, Tina; Schneiders, Eike; Blockx, Jan; Middleton, Stuart E; Ramchurn, Sarvapali
AI large language models inquiry: TASHub Response Miscellaneous
2023.
@misc{soton481740,
title = {AI large language models inquiry: TASHub Response},
author = {Joshua Krook and Jennifer Williams and Tina Seabrooke and Eike Schneiders and Jan Blockx and Stuart E Middleton and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/481740/},
year = {2023},
date = {2023-08-01},
publisher = {University of Southampton},
abstract = {Policy submission to the Consultation by Communications and Digital Committee, House of Lords, AI Large Language Models Inquiry.ensuremath
},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Policy submission to the Consultation by Communications and Digital Committee, House of Lords, AI Large Language Models Inquiry.ensuremath<br/ensuremath>
Thavanesan, Navamayooran; Bodala, Indu; Walters, Zoe; Ramchurn, Sarvapali; Underwood, Timothy J; Vigneswaran, Ganesh
Machine learning to predict curative multidisciplinary team treatment decisions in oesophageal cancer Journal Article
In: European Journal of Surgical Oncology, 2023, (Publisher Copyright: copyright 2023 The Author(s)).
@article{soton479497,
title = {Machine learning to predict curative multidisciplinary team treatment decisions in oesophageal cancer},
author = {Navamayooran Thavanesan and Indu Bodala and Zoe Walters and Sarvapali Ramchurn and Timothy J Underwood and Ganesh Vigneswaran},
url = {https://eprints.soton.ac.uk/479497/},
year = {2023},
date = {2023-07-01},
journal = {European Journal of Surgical Oncology},
abstract = {ensuremathBackground: Rising workflow pressures within the oesophageal cancer (OC) multidisciplinary team (MDT) can lead to variability in decision-making, and health inequality. Machine learning (ML) offers a potential automated data-driven approach to address inconsistency and standardize care. The aim of this experimental pilot study was to develop ML models able to predict curative OC MDT treatment decisions and determine the relative importance of underlying decision-critical variables. Methods: Retrospective complete-case analysis of oesophagectomy patients $pm$ neoadjuvant chemotherapy (NACT) or chemoradiotherapy (NACRT) between 2010 and 2020. Established ML algorithms (Multinomial Logistic regression (MLR), Random Forests (RF), Extreme Gradient Boosting (XGB)) and Decision Tree (DT) were used to train models predicting OC MDT treatment decisions: surgery (S), NACT + S or NACRT + S. Performance metrics included Area Under the Curve (AUC), Accuracy, Kappa, LogLoss, F1 and Precision -Recall AUC. Variable importance was calculated for each model. Results: We identified 399 cases with a male-to-female ratio of 3.6:1 and median age of 66.1yrs (range 32?83). MLR outperformed RF, XGB and DT across performance metrics (mean AUC of 0.793 [$pm$0.045] vs 0.757 [$pm$0.068], 0.740 [$pm$0.042], and 0.709 [$pm$0.021] respectively). Variable importance analysis identified age as a major factor in the decision to offer surgery alone or NACT + S across models (p < 0.05). Conclusions: ML techniques can use limited feature-sets to predict curative UGI MDT treatment decisions. Explainable Artificial Intelligence methods provide insight into decision-critical variables, highlighting underlying subconscious biases in cancer care decision-making. Such models may allow prioritization of caseload, improve efficiency, and offer data-driven decision-assistance to MDTs in the future.ensuremath },
note = {Publisher Copyright:
copyright 2023 The Author(s)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>Background: Rising workflow pressures within the oesophageal cancer (OC) multidisciplinary team (MDT) can lead to variability in decision-making, and health inequality. Machine learning (ML) offers a potential automated data-driven approach to address inconsistency and standardize care. The aim of this experimental pilot study was to develop ML models able to predict curative OC MDT treatment decisions and determine the relative importance of underlying decision-critical variables. Methods: Retrospective complete-case analysis of oesophagectomy patients $pm$ neoadjuvant chemotherapy (NACT) or chemoradiotherapy (NACRT) between 2010 and 2020. Established ML algorithms (Multinomial Logistic regression (MLR), Random Forests (RF), Extreme Gradient Boosting (XGB)) and Decision Tree (DT) were used to train models predicting OC MDT treatment decisions: surgery (S), NACT + S or NACRT + S. Performance metrics included Area Under the Curve (AUC), Accuracy, Kappa, LogLoss, F1 and Precision -Recall AUC. Variable importance was calculated for each model. Results: We identified 399 cases with a male-to-female ratio of 3.6:1 and median age of 66.1yrs (range 32?83). MLR outperformed RF, XGB and DT across performance metrics (mean AUC of 0.793 [$pm$0.045] vs 0.757 [$pm$0.068], 0.740 [$pm$0.042], and 0.709 [$pm$0.021] respectively). Variable importance analysis identified age as a major factor in the decision to offer surgery alone or NACT + S across models (p < 0.05). Conclusions: ML techniques can use limited feature-sets to predict curative UGI MDT treatment decisions. Explainable Artificial Intelligence methods provide insight into decision-critical variables, highlighting underlying subconscious biases in cancer care decision-making. Such models may allow prioritization of caseload, improve efficiency, and offer data-driven decision-assistance to MDTs in the future.ensuremath</pensuremath>
Abioye, Ayodeji; Naiseh, Mohammad; Hunt, William; Clark, Jediah R; Ramchurn, Sarvapali D; Soorati, Mohammad
The effect of data visualisation quality and task density on human-swarm interaction Proceedings Article
In: Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE, 2023.
@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}
}
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.
Krook, Joshua; McAuley, Derek; Anderson, Stuart; Downer, John; Winter, Peter; Ramchurn, Sarvapali D
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response Miscellaneous
2023.
@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}
}
Krook, Joshua; Downer, John; Winter, Peter; Williams, Jennifer; Ives, Jonathan; Bratu, Roxana; Sheir, Stephanie; Williams, Robin; Anderson, Stuart; Li, Phoebe; Ramamoorthy, Subramanian; Ramchurn, Sarvapali
AI regulation: a pro-innovation approach ? policy proposals: TASHub Response Miscellaneous
2023.
@misc{soton478329,
title = {AI regulation: a pro-innovation approach ? policy proposals: TASHub Response},
author = {Joshua Krook and John Downer and Peter Winter and Jennifer Williams and Jonathan Ives and Roxana Bratu and Stephanie Sheir and Robin Williams and Stuart Anderson and Phoebe Li and Subramanian Ramamoorthy and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478329/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
abstract = {Response to open consultation from: Department for Science, Innovation and Technologyensuremath
and Office for Artificial Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Response to open consultation from: Department for Science, Innovation and Technologyensuremath<br/ensuremath>and Office for Artificial Intelligence
Hunt, William; Ryan, Jack; Abioye, Ayodeji O; Ramchurn, Sarvapali D; Soorati, Mohammad D
Demonstrating performance benefits of human-swarm teaming Proceedings Article
In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 3062–3064, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2023.
@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}
}
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.
Worrawichaipat, Phuriwat; Gerding, Enrico; Kaparias, Ioannis; Ramchurn, Sarvapali
Multi-agent signal-less intersection management with dynamic platoon formation Proceedings Article
In: 22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23), pp. 1542–1550, 2023.
@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}
}
Everett, Gregory; Beal, Ryan J; Matthews, Tim; Early, Joseph; Norman, Timothy J; Ramchurn, Sarvapali D
Inferring player location in sports matches: multi-agent spatial imputation from limited observations Miscellaneous
2023.
@misc{soton477020,
title = {Inferring player location in sports matches: multi-agent spatial imputation from limited observations},
author = {Gregory Everett and Ryan J Beal and Tim Matthews and Joseph Early and Timothy J Norman and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477020/},
year = {2023},
date = {2023-02-01},
abstract = {Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS problems typically use uniform timesteps with observations for all agents. In this work, we analyse the problem of agent location imputation, specifically posed in environments with non-uniform timesteps and limited agent observability (textttchar12695% missing values). Our approach uses Long Short-Term Memory and Graph Neural Network components to learn temporal and inter-agent patterns to predict the location of all agents at every timestep. We apply this to the domain of football (soccer) by imputing the location of all players in a game from sparse event data (e.g., shots and passes). Our model estimates player locations to within textttchar1266.9m; a textttchar12662% reduction in error from the best performing baseline. This approach facilitates downstream analysis tasks such as player physical metrics, player coverage, and team pitch control. Existing solutions to these tasks often require optical tracking data, which is expensive to obtain and only available to elite clubs. By imputing player locations from easy to obtain event data, we increase the accessibility of downstream tasks.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS problems typically use uniform timesteps with observations for all agents. In this work, we analyse the problem of agent location imputation, specifically posed in environments with non-uniform timesteps and limited agent observability (textttchar12695% missing values). Our approach uses Long Short-Term Memory and Graph Neural Network components to learn temporal and inter-agent patterns to predict the location of all agents at every timestep. We apply this to the domain of football (soccer) by imputing the location of all players in a game from sparse event data (e.g., shots and passes). Our model estimates player locations to within textttchar1266.9m; a textttchar12662% reduction in error from the best performing baseline. This approach facilitates downstream analysis tasks such as player physical metrics, player coverage, and team pitch control. Existing solutions to these tasks often require optical tracking data, which is expensive to obtain and only available to elite clubs. By imputing player locations from easy to obtain event data, we increase the accessibility of downstream tasks.
2022
Ahmed, Sarah; Azim, Tayyaba; Early, Joseph Arthur; Ramchurn, Sarvapali
Revisiting deep fisher vectors: using fisher information to improve object classification Proceedings Article
In: British Machine Vision Conference (21/11/22 - 24/11/22), 2022.
@inproceedings{soton471260,
title = {Revisiting deep fisher vectors: using fisher information to improve object classification},
author = {Sarah Ahmed and Tayyaba Azim and Joseph Arthur Early and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/471260/},
year = {2022},
date = {2022-11-01},
booktitle = {British Machine Vision Conference (21/11/22 - 24/11/22)},
abstract = {Although deep learning models have become the gold standard in achieving outstanding results on a large variety of computer vision and machine learning tasks, the use of kernel methods has still not gone out of trend because of its potential to beat deep learning performances at a number of occasions. Given the potential of kernel techniques, prior works have also proposed the use of hybrid approaches combining deep learning with kernel learning to complement their respective strengths and weaknesses. This work develops this idea further by introducing an improved version of Fisher kernels derived from the deep Boltzmann machines (DBM). Our improved deep Fisher kernel (IDFK) utilises an approximation of the Fisher information matrix to derive improved Fisher vectors. We show IDFK can be utilised to retain a high degree of class separability, making it appropriate for classification and retrieval tasks. The efficacy of the proposed approach is evaluated on three benchmark data sets: MNIST, USPS and Alphanumeric, showing an improvement in classification performance over existing kernel approaches, and comparable performance to deep learning methods, but with much reduced computational costs. Using explainable AI methods, we also demonstrate why our IDFK leads to better classification performance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Although deep learning models have become the gold standard in achieving outstanding results on a large variety of computer vision and machine learning tasks, the use of kernel methods has still not gone out of trend because of its potential to beat deep learning performances at a number of occasions. Given the potential of kernel techniques, prior works have also proposed the use of hybrid approaches combining deep learning with kernel learning to complement their respective strengths and weaknesses. This work develops this idea further by introducing an improved version of Fisher kernels derived from the deep Boltzmann machines (DBM). Our improved deep Fisher kernel (IDFK) utilises an approximation of the Fisher information matrix to derive improved Fisher vectors. We show IDFK can be utilised to retain a high degree of class separability, making it appropriate for classification and retrieval tasks. The efficacy of the proposed approach is evaluated on three benchmark data sets: MNIST, USPS and Alphanumeric, showing an improvement in classification performance over existing kernel approaches, and comparable performance to deep learning methods, but with much reduced computational costs. Using explainable AI methods, we also demonstrate why our IDFK leads to better classification performance.
Yazdanpanah, Vahid; Gerding, Enrico; Stein, Sebastian; Dastani, Mehdi; Jonker, Catholijn M; Norman, Timothy; Ramchurn, Sarvapali
Reasoning About Responsibility in Autonomous Systems: Challenges and Opportunities Journal Article
In: AI & Society, 2022.
@article{soton471971,
title = {Reasoning About Responsibility in Autonomous Systems: Challenges and Opportunities},
author = {Vahid Yazdanpanah and Enrico Gerding and Sebastian Stein and Mehdi Dastani and Catholijn M Jonker and Timothy Norman and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/471971/},
year = {2022},
date = {2022-11-01},
journal = {AI & Society},
abstract = {Ensuring the trustworthiness of autonomous systems and artificial intelligenceensuremath
is an important interdisciplinary endeavour. In this position paper, we argue thatensuremath
this endeavour will benefit from technical advancements in capturing various forms of responsibility, and we present a comprehensive research agenda to achieve this. In particular, we argue that ensuring the reliability of autonomous system can take advantage of technical approaches for quantifying degrees of responsibility and for coordinating tasks based on that. Moreover, we deem that, in certifying the legality of an AI system, formal and computationally implementable notions of responsibility, blame, accountability, and liability are applicable for addressing potential responsibility gaps (i.e., situations in which a group is responsible, but individuals? responsibility may be unclear). This is a call to enable AI systems themselves, as well as those involved in the design, monitoring, and governance of AI systems, to represent and reason about who can be seen as responsible in prospect (e.g., for completing a task in future) and who can be seen as responsible retrospectively (e.g., for a failure that has already occurred). To that end, in this work, we show that across all stages of the design, development, and deployment of Trustworthy Autonomous Systems (TAS), responsibility reasoning should play a key role. This position paper is the first step towards establishing a road-map and research agenda on how the notion of responsibility can provide novel solution concepts for ensuring the reliability and legality of TAS and, as a result, enables an effective embedding of AI technologies into society.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ensuring the trustworthiness of autonomous systems and artificial intelligenceensuremath<br/ensuremath>is an important interdisciplinary endeavour. In this position paper, we argue thatensuremath<br/ensuremath>this endeavour will benefit from technical advancements in capturing various forms of responsibility, and we present a comprehensive research agenda to achieve this. In particular, we argue that ensuring the reliability of autonomous system can take advantage of technical approaches for quantifying degrees of responsibility and for coordinating tasks based on that. Moreover, we deem that, in certifying the legality of an AI system, formal and computationally implementable notions of responsibility, blame, accountability, and liability are applicable for addressing potential responsibility gaps (i.e., situations in which a group is responsible, but individuals? responsibility may be unclear). This is a call to enable AI systems themselves, as well as those involved in the design, monitoring, and governance of AI systems, to represent and reason about who can be seen as responsible in prospect (e.g., for completing a task in future) and who can be seen as responsible retrospectively (e.g., for a failure that has already occurred). To that end, in this work, we show that across all stages of the design, development, and deployment of Trustworthy Autonomous Systems (TAS), responsibility reasoning should play a key role. This position paper is the first step towards establishing a road-map and research agenda on how the notion of responsibility can provide novel solution concepts for ensuring the reliability and legality of TAS and, as a result, enables an effective embedding of AI technologies into society.
Early, Joseph; Deweese, Ying-Jung; Evers, Christine; Ramchurn, Sarvapali
Scene-to-Patch earth observation: multiple instance learning for land cover classification Miscellaneous
2022, (14 pages total (4 main content; 2 acknowledgments + citations; 8 appendices); 8 figures (2 main; 6 appendix); published at "Tackling Climate Change with Machine Learning: Workshop at NeurIPS 2022").
@misc{soton472853,
title = {Scene-to-Patch earth observation: multiple instance learning for land cover classification},
author = {Joseph Early and Ying-Jung Deweese and Christine Evers and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/472853/},
year = {2022},
date = {2022-11-01},
abstract = {Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on fully-annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of LCC. In this study, we propose Scene-to-Patch models: an alternative LCC approach utilising Multiple Instance Learning (MIL) that requires only high-level scene labels. This enables much faster development of new datasets whilst still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using LCC for different scenarios. On the DeepGlobe-LCC dataset, our approach outperforms non-MIL baselines on both scene- and patch-level prediction. This work provides the foundation for expanding the use of LCC in climate change mitigation methods for technology, government, and academia.},
note = {14 pages total (4 main content; 2 acknowledgments + citations; 8 appendices); 8 figures (2 main; 6 appendix); published at "Tackling Climate Change with Machine Learning: Workshop at NeurIPS 2022"},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on fully-annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of LCC. In this study, we propose Scene-to-Patch models: an alternative LCC approach utilising Multiple Instance Learning (MIL) that requires only high-level scene labels. This enables much faster development of new datasets whilst still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using LCC for different scenarios. On the DeepGlobe-LCC dataset, our approach outperforms non-MIL baselines on both scene- and patch-level prediction. This work provides the foundation for expanding the use of LCC in climate change mitigation methods for technology, government, and academia.
Parnell, Katie; Fischer, Joel E; Clark, Jediah R; Bodenmann, Adrian; Trigo, Maria Jose Galvez; Brito, Mario; Soorati, Mohammad Divband; Plant, Katherine; Ramchurn, Sarvapali
Trustworthy UAV relationships: Applying the Schema Action World taxonomy to UAVs and UAV swarm operations Journal Article
In: International Journal of Human-Computer Interaction, 2022.
@article{soton468839,
title = {Trustworthy UAV relationships: Applying the Schema Action World taxonomy to UAVs and UAV swarm operations},
author = {Katie Parnell and Joel E Fischer and Jediah R Clark and Adrian Bodenmann and Maria Jose Galvez Trigo and Mario Brito and Mohammad Divband Soorati and Katherine Plant and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/468839/},
year = {2022},
date = {2022-07-01},
journal = {International Journal of Human-Computer Interaction},
abstract = {Human Factors play a significant role inthe development and integration of avionic systems to ensure that they are trusted and can be used effectively. As Unoccupied Aerial Vehicle (UAV) technology becomes increasingly important to the aviation domain this holds true. This study aims to gain an understanding of UAV operators?trust requirements when piloting UAVs by utilising a popular aviation interview methodology (Schema World Action Research Method), in combination with key questions on trust identified from the literature. Interviews were conducted with six UAVoperators, with a range of experience. This identified the importance of past experience to trust and the expectations that operators hold. Recommendations are made that target training to inform experience, in addition to the equipment, procedures and organisational standards that can aid in developing trustworthy systems. The methodology that was developed shows promise for capturing trust within human-automation interactions},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Human Factors play a significant role inthe development and integration of avionic systems to ensure that they are trusted and can be used effectively. As Unoccupied Aerial Vehicle (UAV) technology becomes increasingly important to the aviation domain this holds true. This study aims to gain an understanding of UAV operators?trust requirements when piloting UAVs by utilising a popular aviation interview methodology (Schema World Action Research Method), in combination with key questions on trust identified from the literature. Interviews were conducted with six UAVoperators, with a range of experience. This identified the importance of past experience to trust and the expectations that operators hold. Recommendations are made that target training to inform experience, in addition to the equipment, procedures and organisational standards that can aid in developing trustworthy systems. The methodology that was developed shows promise for capturing trust within human-automation interactions
Soorati, Mohammad Divband; Gerding, Enrico; Marchioni, Enrico; Naumov, Pavel; Norman, Timothy; Ramchurn, Sarvapali; Rastegari, Baharak; Sobey, Adam; Stein, Sebastian; Tarapore, Danesh; Yazdanpanah, Vahid; Zhang, Jie
From Intelligent Agents to Trustworthy Human-Centred Multiagent Systems Journal Article
In: AI Communications, 2022.
@article{soton467975,
title = {From Intelligent Agents to Trustworthy Human-Centred Multiagent Systems},
author = {Mohammad Divband Soorati and Enrico Gerding and Enrico Marchioni and Pavel Naumov and Timothy Norman and Sarvapali Ramchurn and Baharak Rastegari and Adam Sobey and Sebastian Stein and Danesh Tarapore and Vahid Yazdanpanah and Jie Zhang},
url = {https://eprints.soton.ac.uk/467975/},
year = {2022},
date = {2022-07-01},
journal = {AI Communications},
abstract = {The Agents, Interaction and Complexity research group at the University of Southampton has a long track record of research in multiagent systems (MAS). We have made substantial scientific contributions across learning in MAS, game-theoretic techniques for coordinating agent systems, and formal methods for representation and reasoning. We highlight key results achieved by the group and elaborate on recent work and open research challenges in developing trustworthy autonomous systems and deploying human-centred AI systems that aim to support societal good.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Agents, Interaction and Complexity research group at the University of Southampton has a long track record of research in multiagent systems (MAS). We have made substantial scientific contributions across learning in MAS, game-theoretic techniques for coordinating agent systems, and formal methods for representation and reasoning. We highlight key results achieved by the group and elaborate on recent work and open research challenges in developing trustworthy autonomous systems and deploying human-centred AI systems that aim to support societal good.
Bossens, David; Ramchurn, Sarvapali; Tarapore, Danesh
Resilient robot teams: a review integrating decentralised control, change-detection, and learning Miscellaneous
2022.
@misc{soton457101,
title = {Resilient robot teams: a review integrating decentralised control, change-detection, and learning},
author = {David Bossens and Sarvapali Ramchurn and Danesh Tarapore},
url = {https://eprints.soton.ac.uk/457101/},
year = {2022},
date = {2022-06-01},
journal = {Current Robotics Reports},
abstract = {Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams.ensuremath
ensuremath
Recent findings: Exogenous fault detection methods can provide a generic detection or a specific diagnosis with a recovery solution. Robot teams can perform active and distributed sensing for detecting changes in the environment, including identifying and tracking dynamic anomalies, as well as collaboratively mapping dynamic environments. Resilient methods for decentralised control have been developed in learning perception-action-communication loops, multi-agent reinforcement learning, embodied evolution, offline evolution with online adaptation, explicit task allocation, and stigmergy in swarm robotics.ensuremath
ensuremath
Summary: Remaining challenges for resilient robot teams are integrating change-detection and trial-and-error learning methods, obtaining reliable performance evaluations under constrained evaluation time, improving the safety of resilient robot teams, theoretical results demonstrating rapid adaptation to given environmental perturbations, and designing realistic and compelling case studies.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams.ensuremath<br/ensuremath>ensuremath<br/ensuremath>Recent findings: Exogenous fault detection methods can provide a generic detection or a specific diagnosis with a recovery solution. Robot teams can perform active and distributed sensing for detecting changes in the environment, including identifying and tracking dynamic anomalies, as well as collaboratively mapping dynamic environments. Resilient methods for decentralised control have been developed in learning perception-action-communication loops, multi-agent reinforcement learning, embodied evolution, offline evolution with online adaptation, explicit task allocation, and stigmergy in swarm robotics.ensuremath<br/ensuremath>ensuremath<br/ensuremath>Summary: Remaining challenges for resilient robot teams are integrating change-detection and trial-and-error learning methods, obtaining reliable performance evaluations under constrained evaluation time, improving the safety of resilient robot teams, theoretical results demonstrating rapid adaptation to given environmental perturbations, and designing realistic and compelling case studies.
Early, Joseph; Bewley, Tom; Evers, Christine; Ramchurn, Sarvapali
Non-markovian reward modelling from trajectory labels via interpretable multiple instance learning Journal Article
In: arXiv, 2022, (20 pages (9 main content; 2 references; 9 appendix). 11 figures (8 main content; 3 appendix)).
@article{soton458023,
title = {Non-markovian reward modelling from trajectory labels via interpretable multiple instance learning},
author = {Joseph Early and Tom Bewley and Christine Evers and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/458023/},
year = {2022},
date = {2022-05-01},
journal = {arXiv},
abstract = {We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in a trajectory independently when providing feedback on agent behaviour. In this work, we remove this assumption, extending RM to include hidden state information that captures temporal dependencies in human assessment of trajectories. We then show how RM can be approached as a multiple instance learning (MIL) problem, and develop new MIL models that are able to capture the time dependencies in labelled trajectories. We demonstrate on a range of RL tasks that our novel MIL models can reconstruct reward functions to a high level of accuracy, and that they provide interpretable learnt hidden information that can be used to train high-performing agent policies.},
note = {20 pages (9 main content; 2 references; 9 appendix). 11 figures (8 main content; 3 appendix)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in a trajectory independently when providing feedback on agent behaviour. In this work, we remove this assumption, extending RM to include hidden state information that captures temporal dependencies in human assessment of trajectories. We then show how RM can be approached as a multiple instance learning (MIL) problem, and develop new MIL models that are able to capture the time dependencies in labelled trajectories. We demonstrate on a range of RL tasks that our novel MIL models can reconstruct reward functions to a high level of accuracy, and that they provide interpretable learnt hidden information that can be used to train high-performing agent policies.
Buermann, Jan; Georgiev, Dimitar; Gerding, Enrico; Hill, Lewis; Malik, Obaid; Pop, Alexandru; Pun, Matthew; Ramchurn, Sarvapali; Salisbury, Elliot; Stojanovic, Ivan
An agent-based simulator for maritime transport decarbonisation: Demonstration track Proceedings Article
In: 21st International Conference on Autonomous Agents and Multiagent Systems (09/05/22 - 13/05/22), pp. 1890–1892, 2022.
@inproceedings{soton456716,
title = {An agent-based simulator for maritime transport decarbonisation: Demonstration track},
author = {Jan Buermann and Dimitar Georgiev and Enrico Gerding and Lewis Hill and Obaid Malik and Alexandru Pop and Matthew Pun and Sarvapali Ramchurn and Elliot Salisbury and Ivan Stojanovic},
url = {https://eprints.soton.ac.uk/456716/},
year = {2022},
date = {2022-05-01},
booktitle = {21st International Conference on Autonomous Agents and Multiagent Systems (09/05/22 - 13/05/22)},
pages = {1890--1892},
abstract = {Greenhouse gas (GHG) emission reduction is an important and necessary goal; currently, different policies to reduce GHG emissions in maritime transport are being discussed. Amongst policies, like carbon taxes or carbon intensity targets, it is hard to determine which policies can successfully reduce GHG emissions while allowing the industry to be profitable. We introduce an agent-based maritime transport simulator to investigate the effectiveness of two decarbonisation policies by simulating a maritime transport operator?s trade pattern and fleet make-up changes as a reaction to taxation and fixed targets. This first of its kind simulator allows to compare and quantify the difference of carbon reduction policies and how they affect shipping operations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Greenhouse gas (GHG) emission reduction is an important and necessary goal; currently, different policies to reduce GHG emissions in maritime transport are being discussed. Amongst policies, like carbon taxes or carbon intensity targets, it is hard to determine which policies can successfully reduce GHG emissions while allowing the industry to be profitable. We introduce an agent-based maritime transport simulator to investigate the effectiveness of two decarbonisation policies by simulating a maritime transport operator?s trade pattern and fleet make-up changes as a reaction to taxation and fixed targets. This first of its kind simulator allows to compare and quantify the difference of carbon reduction policies and how they affect shipping operations.
Rigas, Emmanouil S; Gerding, Enrico H; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick
Mechanism design for efficient offline and online allocation of electric vehicles to charging stations Journal Article
In: Energies, vol. 15, no. 5, 2022, (Funding Information: Funding: This research study was co-financed by Greece and the European Union (European Social Fund--ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Reinforcement of Postdoctoral Researchers-2nd Cycle? (MIS-5033021), implemented by State Scholarships Foundation (IKY). Copyright 2022 Elsevier B.V., All rights reserved.).
@article{soton455806,
title = {Mechanism design for efficient offline and online allocation of electric vehicles to charging stations},
author = {Emmanouil S Rigas and Enrico H Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades},
url = {https://eprints.soton.ac.uk/455806/},
year = {2022},
date = {2022-03-01},
journal = {Energies},
volume = {15},
number = {5},
abstract = {ensuremathThe industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total COensuremath2ensuremath emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey?Clark?Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs? aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we observed that they had satisfactory scalability. Additionally, the VCG mechanism achieved an up to 2.2% improvement in terms of the number of vehicles that were charged compared to the fixed-price one and, in cases where the stations were congested, it calculated higher prices for the EVs and provided a higher profit for the stations, but lower utility to the EVs. However, in a theoretical evaluation, we proved that the variant of the VCG mechanism being proposed in this paper still guaranteed truthful reporting of the EVs? preferences. In contrast, the fixed-price one was found to be vulnerable to agents? strategic behavior as non-truthful EVs can charge instead of truthful ones. Finally, we observed the online algorithms to be, on average, at 95.6% of the offline ones in terms of the average number of serviced EVs.ensuremath },
note = {Funding Information:
Funding: This research study was co-financed by Greece and the European Union (European Social Fund--ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Reinforcement of Postdoctoral Researchers-2nd Cycle? (MIS-5033021), implemented by State Scholarships Foundation (IKY).
Copyright 2022 Elsevier B.V., All rights reserved.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total COensuremath<subensuremath>2ensuremath</subensuremath> emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey?Clark?Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs? aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we observed that they had satisfactory scalability. Additionally, the VCG mechanism achieved an up to 2.2% improvement in terms of the number of vehicles that were charged compared to the fixed-price one and, in cases where the stations were congested, it calculated higher prices for the EVs and provided a higher profit for the stations, but lower utility to the EVs. However, in a theoretical evaluation, we proved that the variant of the VCG mechanism being proposed in this paper still guaranteed truthful reporting of the EVs? preferences. In contrast, the fixed-price one was found to be vulnerable to agents? strategic behavior as non-truthful EVs can charge instead of truthful ones. Finally, we observed the online algorithms to be, on average, at 95.6% of the offline ones in terms of the average number of serviced EVs.ensuremath</pensuremath>
Early, Joseph; Evers, Christine; Ramchurn, Sarvapali
Model agnostic interpretability for multiple instance learning Proceedings Article
In: International Conference on Learning Representations 2022 (25/04/22 - 29/04/22), 2022, (25 pages (9 content, 2 acknowledgement + references, 14 appendix). 16 figures (3 main content, 13 appendix). Submitted and accepted to ICLR 22, see http://openreview.net/forum?id=KSSfF5lMIAg . Revision: added additional acknowledgements).
@inproceedings{soton454952,
title = {Model agnostic interpretability for multiple instance learning},
author = {Joseph Early and Christine Evers and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/454952/},
year = {2022},
date = {2022-01-01},
booktitle = {International Conference on Learning Representations 2022 (25/04/22 - 29/04/22)},
abstract = {In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems.},
note = {25 pages (9 content, 2 acknowledgement + references, 14 appendix). 16 figures (3 main content, 13 appendix). Submitted and accepted to ICLR 22, see http://openreview.net/forum?id=KSSfF5lMIAg . Revision: added additional acknowledgements},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems.
2021
Ramchurn, Sarvapali; Middleton, Stuart; McAuley, Derek; Webb, Helena; Hyde, Richard; Lisinska, Justyna
A Response to Draft Online Safety Bill: A call for evidence from the Joint Committee Technical Report
no. 10.18742/pub01-060, 2021.
@techreport{soton451428,
title = {A Response to Draft Online Safety Bill: A call for evidence from the Joint Committee},
author = {Sarvapali Ramchurn and Stuart Middleton and Derek McAuley and Helena Webb and Richard Hyde and Justyna Lisinska},
url = {https://eprints.soton.ac.uk/451428/},
year = {2021},
date = {2021-09-01},
number = {10.18742/pub01-060},
abstract = {This report is the Trustworthy Autonomous Hub (TAS-hub) response to the call for evidence from the Joint Committee on the Draft Online Safety Bill. The Joint Committee was established to consider the Government's draft Bill to establish a new regulatory framework to tackle harmful content online.},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
This report is the Trustworthy Autonomous Hub (TAS-hub) response to the call for evidence from the Joint Committee on the Draft Online Safety Bill. The Joint Committee was established to consider the Government's draft Bill to establish a new regulatory framework to tackle harmful content online.
Ramchurn, Sarvapali; Stein, Sebastian; Jennings, Nicholas R
Trustworthy human-AI partnerships Journal Article
In: iScience, vol. 24, no. 8, 2021.
@article{soton450597,
title = {Trustworthy human-AI partnerships},
author = {Sarvapali Ramchurn and Sebastian Stein and Nicholas R Jennings},
url = {https://eprints.soton.ac.uk/450597/},
year = {2021},
date = {2021-08-01},
journal = {iScience},
volume = {24},
number = {8},
abstract = {In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organisations trust their machine counterparts individually or as a collective (e.g., as robot teams or groups of software agents). We also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. By so doing, we will trigger new avenues of research that will address the key barriers to the adoption of AI-based systems more widely in our daily lives and in industry.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organisations trust their machine counterparts individually or as a collective (e.g., as robot teams or groups of software agents). We also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. By so doing, we will trigger new avenues of research that will address the key barriers to the adoption of AI-based systems more widely in our daily lives and in industry.
Ramchurn, Sarvapali; Mousavi, Mohammad Reza; Toliyat, Seyed Mohammad Hossein; Kleinman, Mark; Lisinska, Justyna; Sempreboni, Diego; Stein, Sebastian; Gerding, Enrico; Gomer, Richard; DÁmore, Francesco
The future of connected and automated mobility in the UK: call for evidence Technical Report
no. 10.5258/SOTON/P0097, 2021, (The UKRI TAS Hub assembles a team from the Universities of Southampton, Nottingham and King?s College London. The Hub sits at the centre of the pounds33M Trustworthy Autonomous Systems Programme, funded by the UKRI Strategic Priorities Fund. The role of the TAS Hub is to coordinate and work with six research nodes to establish a collaborative platform for the UK to enable the development of socially beneficial autonomous systems that are both trustworthy in principle and trusted in practice by individuals, society and government. Read more about the TAS Hub at https://www.tas.ac.uk/aboutus/overview/).
@techreport{soton450228,
title = {The future of connected and automated mobility in the UK: call for evidence},
author = {Sarvapali Ramchurn and Mohammad Reza Mousavi and Seyed Mohammad Hossein Toliyat and Mark Kleinman and Justyna Lisinska and Diego Sempreboni and Sebastian Stein and Enrico Gerding and Richard Gomer and Francesco DÁmore},
editor = {Wassim Dbouk},
url = {https://eprints.soton.ac.uk/450228/},
year = {2021},
date = {2021-07-01},
number = {10.5258/SOTON/P0097},
publisher = {University of Southampton},
abstract = {This report is a response to the call for evidence from the Department for Business, Energy & Industrial Strategy and the Centre for Connected and Autonomous Vehicles on the future of connected and automated mobility in the UK.ensuremath
Executive Summary:Despite relative weaknesses in global collaboration and co-creation platforms, smart road and communication infrastructure, urban planning, and public awareness, the United Kingdom (UK) has a substantial strength in the area of Connected and Automated Mobility (CAM) by investing in research and innovation platforms for developing the underlying technologies, creating impact, and co-creation leading to innovative solutions. Many UK legal and policymaking initiatives in this domain are world leading. To sustain the UK?s leading position, we make the following recommendations:? The development of financial and policy-related incentive schemes for research and innovation in the foundations and applications of autonomous systems as well as schemes for proof of concepts, and commercialisation.? Supporting policy and standardisation initiatives as well as engagement and community-building activities to increase public awareness and trust.? Giving greater attention to integrating CAM/Connected Autonomous Shared Electric vehicles (CASE) policy with related government priorities for mobility, including supporting active transport and public transport, and improving air quality.? Further investment in updating liability and risk models and coming up with innovative liability schemes covering the Autonomous Vehicles (AVs) ecosystem.? Investing in training and retraining of the work force in the automotive, mobility, and transport sectors, particularly with skills concerningArtificial Intelligence (AI), software and computer systems, in order to ensure employability and an adequate response to the drastically changing industrial landscape},
note = {The UKRI TAS Hub assembles a team from the Universities of Southampton, Nottingham and King?s College London. The Hub sits at the centre of the pounds33M Trustworthy Autonomous Systems Programme, funded by the UKRI Strategic Priorities Fund.
The role of the TAS Hub is to coordinate and work with six research nodes to establish a collaborative platform for the UK to enable the development of socially beneficial autonomous systems that are both trustworthy in principle and trusted in practice by individuals, society and government. Read more about the TAS Hub at https://www.tas.ac.uk/aboutus/overview/},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
This report is a response to the call for evidence from the Department for Business, Energy & Industrial Strategy and the Centre for Connected and Autonomous Vehicles on the future of connected and automated mobility in the UK.ensuremath<br/ensuremath>Executive Summary:Despite relative weaknesses in global collaboration and co-creation platforms, smart road and communication infrastructure, urban planning, and public awareness, the United Kingdom (UK) has a substantial strength in the area of Connected and Automated Mobility (CAM) by investing in research and innovation platforms for developing the underlying technologies, creating impact, and co-creation leading to innovative solutions. Many UK legal and policymaking initiatives in this domain are world leading. To sustain the UK?s leading position, we make the following recommendations:? The development of financial and policy-related incentive schemes for research and innovation in the foundations and applications of autonomous systems as well as schemes for proof of concepts, and commercialisation.? Supporting policy and standardisation initiatives as well as engagement and community-building activities to increase public awareness and trust.? Giving greater attention to integrating CAM/Connected Autonomous Shared Electric vehicles (CASE) policy with related government priorities for mobility, including supporting active transport and public transport, and improving air quality.? Further investment in updating liability and risk models and coming up with innovative liability schemes covering the Autonomous Vehicles (AVs) ecosystem.? Investing in training and retraining of the work force in the automotive, mobility, and transport sectors, particularly with skills concerningArtificial Intelligence (AI), software and computer systems, in order to ensure employability and an adequate response to the drastically changing industrial landscape
Worrawichaipat, Phuriwat; Gerding, Enrico; Kaparias, Ioannis; Ramchurn, Sarvapali
Resilient intersection management with multi-vehicle collision avoidance Journal Article
In: Frontiers in Sustainable Cities, vol. 3, 2021.
@article{soton449675,
title = {Resilient intersection management with multi-vehicle collision avoidance},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/449675/},
year = {2021},
date = {2021-06-01},
journal = {Frontiers in Sustainable Cities},
volume = {3},
abstract = {In this paper, we propose a novel decentralised agent-based mechanism for road intersection management for connected autonomous vehicles. In our work we focus on road obstructions causing major traffic delays. In doing so, we propose the first decentralised mechanism able to maximise the overall vehicle throughput at intersections in the presence of obstructions. The distributed algorithm transfers most of the computational cost from the intersection manager to the driving agents, thereby improving scalability. Our realistic empirical experiments using SUMO show that, when an obstacle is located at the entrance or in the middle of the intersection, existing state of the art algorithms and traffic lights show a reduced throughput of 65?90% from the optimal point without obstructions while our mechanism can maintain the throughput upensuremath
Q7 to 94?99%.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose a novel decentralised agent-based mechanism for road intersection management for connected autonomous vehicles. In our work we focus on road obstructions causing major traffic delays. In doing so, we propose the first decentralised mechanism able to maximise the overall vehicle throughput at intersections in the presence of obstructions. The distributed algorithm transfers most of the computational cost from the intersection manager to the driving agents, thereby improving scalability. Our realistic empirical experiments using SUMO show that, when an obstacle is located at the entrance or in the middle of the intersection, existing state of the art algorithms and traffic lights show a reduced throughput of 65?90% from the optimal point without obstructions while our mechanism can maintain the throughput upensuremath<br/ensuremath>Q7 to 94?99%.
Capezzuto, Luca; Tarapore, Danesh; Ramchurn, Sarvapali
Large-scale, dynamic and distributed coalition formation with spatial and†temporal constraints Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 108–125, 2021.
@article{soton452050,
title = {Large-scale, dynamic and distributed coalition formation with spatial and†temporal constraints},
author = {Luca Capezzuto and Danesh Tarapore and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/452050/},
year = {2021},
date = {2021-05-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {108--125},
abstract = {The†Coalition Formation with Spatial and Temporal constraints Problem†(CFSTP) is a multi-agent task allocation problem in which few agents have to perform many tasks, each with its deadline and workload. To maximize the number of completed tasks, the agents need to cooperate by forming, disbanding and reforming coalitions. The original mathematical programming formulation of the CFSTP is difficult to implement, since it is lengthy and based on the problematic Big-M method. In this paper, we propose a compact and easy-to-implement formulation. Moreover, we design D-CTS, a distributed version of the state-of-the-art CFSTP algorithm. Using public London Fire Brigade records, we create a dataset with 347588 tasks and a test framework that simulates the mobilization of firefighters in dynamic environments. In problems with up†to 150 agents and 3000 tasks, compared to DSA-SDP, a state-of-the-art distributed algorithm, D-CTS completes†3.79%$pm$[42.22%,1.96%]†more tasks, and is one order of magnitude more efficient in terms of communication overhead and time complexity. D-CTS sets the first large-scale, dynamic and distributed CFSTP benchmark.ensuremath
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The†Coalition Formation with Spatial and Temporal constraints Problem†(CFSTP) is a multi-agent task allocation problem in which few agents have to perform many tasks, each with its deadline and workload. To maximize the number of completed tasks, the agents need to cooperate by forming, disbanding and reforming coalitions. The original mathematical programming formulation of the CFSTP is difficult to implement, since it is lengthy and based on the problematic Big-M method. In this paper, we propose a compact and easy-to-implement formulation. Moreover, we design D-CTS, a distributed version of the state-of-the-art CFSTP algorithm. Using public London Fire Brigade records, we create a dataset with 347588 tasks and a test framework that simulates the mobilization of firefighters in dynamic environments. In problems with up†to 150 agents and 3000 tasks, compared to DSA-SDP, a state-of-the-art distributed algorithm, D-CTS completes†3.79%$pm$[42.22%,1.96%]†more tasks, and is one order of magnitude more efficient in terms of communication overhead and time complexity. D-CTS sets the first large-scale, dynamic and distributed CFSTP benchmark.ensuremath<br/ensuremath>
Capezzuto, Luca; Tarapore, Danesh; Ramchurn, Sarvapali D
Anytime and efficient multi-agent coordination for disaster response Journal Article
In: SN Computer Science, vol. 2, no. 3, 2021.
@article{soton467373,
title = {Anytime and efficient multi-agent coordination for disaster response},
author = {Luca Capezzuto and Danesh Tarapore and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/467373/},
year = {2021},
date = {2021-03-01},
journal = {SN Computer Science},
volume = {2},
number = {3},
abstract = {The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks. To maximise the number of completed tasks, the agents may have to schedule coalitions. The state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define a variant, called Coalition Formation with Improved Look-Ahead (CFLA2), which achieves better performance. Since we cannot eliminate the limitations of CFLA in CFLA2, we also develop a novel algorithm to solve the CFSTP, the first to be simultaneously anytime, efficient and with convergence guarantee, called Cluster-based Task Scheduling (CTS). In tests where the look-ahead technique is highly effective, CTS completes up to 30% (resp. 10%) more tasks than CFLA (resp. CFLA2) while being up to 4 orders of magnitude faster. We also propose S-CTS, a simplified but parallel variant of CTS with even lower time complexity. Using scenarios generated by the RoboCup Rescue Simulation, we show that S-CTS is at most 10% less performing than high-performance algorithms such as Binary Max-Sum and DSA, but up to 2 orders of magnitude faster. Our results affirm CTS as the new state-of-the-art algorithm to solve the CFSTP.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks. To maximise the number of completed tasks, the agents may have to schedule coalitions. The state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define a variant, called Coalition Formation with Improved Look-Ahead (CFLA2), which achieves better performance. Since we cannot eliminate the limitations of CFLA in CFLA2, we also develop a novel algorithm to solve the CFSTP, the first to be simultaneously anytime, efficient and with convergence guarantee, called Cluster-based Task Scheduling (CTS). In tests where the look-ahead technique is highly effective, CTS completes up to 30% (resp. 10%) more tasks than CFLA (resp. CFLA2) while being up to 4 orders of magnitude faster. We also propose S-CTS, a simplified but parallel variant of CTS with even lower time complexity. Using scenarios generated by the RoboCup Rescue Simulation, we show that S-CTS is at most 10% less performing than high-performance algorithms such as Binary Max-Sum and DSA, but up to 2 orders of magnitude faster. Our results affirm CTS as the new state-of-the-art algorithm to solve the CFSTP.
Ortega, Andre P; Ramchurn, Sarvapali; Tran-Thanh, Long; Merrett, Geoff
Partner selection in self-organised wireless sensor networks for opportunistic energy negotiation: A multi-armed bandit based approach Journal Article
In: Ad Hoc Networks, vol. 112, 2021.
@article{soton445733,
title = {Partner selection in self-organised wireless sensor networks for opportunistic energy negotiation: A multi-armed bandit based approach},
author = {Andre P Ortega and Sarvapali Ramchurn and Long Tran-Thanh and Geoff Merrett},
url = {https://eprints.soton.ac.uk/445733/},
year = {2021},
date = {2021-03-01},
journal = {Ad Hoc Networks},
volume = {112},
abstract = {The proliferation of ?Things? over a network creates the Internet of Things (IoT), where sensors integrate to collect data from the environment over long periods of time. The growth of IoT applications will inevitably involve co-locating multiple wireless sensor networks, each serving different applications with, possibly, different needs and constraints. Since energy is scarce in sensor nodes equipped with non-rechargeable batteries, energy harvesting technologies have been the focus of research in recent years. However, new problems arise as a result of their wide spatio-temporal variation. Such a shortcoming can be avoided if co-located networks cooperate with each other and share their available energy. Due to their unique characteristics and different owners, recently, we proposed a negotiation approach to deal with conflict of preferences. Unfortunately, negotiation can be impractical with a large number of participants, especially in an open environment. Given this, we introduce a new partner selection technique based on multi-armed bandits (MAB), that enables each node to learn the strategy that optimises its energy resources in the long term. Our results show that the proposed solution allows networks to repeatedly learn the current best energy partner in a dynamic environment. The performance of such a technique is evaluated through simulation and shows that a network can achieve an efficiency of 72% against the optimal strategy in the most challenging scenario studied in this work.},
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The proliferation of ?Things? over a network creates the Internet of Things (IoT), where sensors integrate to collect data from the environment over long periods of time. The growth of IoT applications will inevitably involve co-locating multiple wireless sensor networks, each serving different applications with, possibly, different needs and constraints. Since energy is scarce in sensor nodes equipped with non-rechargeable batteries, energy harvesting technologies have been the focus of research in recent years. However, new problems arise as a result of their wide spatio-temporal variation. Such a shortcoming can be avoided if co-located networks cooperate with each other and share their available energy. Due to their unique characteristics and different owners, recently, we proposed a negotiation approach to deal with conflict of preferences. Unfortunately, negotiation can be impractical with a large number of participants, especially in an open environment. Given this, we introduce a new partner selection technique based on multi-armed bandits (MAB), that enables each node to learn the strategy that optimises its energy resources in the long term. Our results show that the proposed solution allows networks to repeatedly learn the current best energy partner in a dynamic environment. The performance of such a technique is evaluated through simulation and shows that a network can achieve an efficiency of 72% against the optimal strategy in the most challenging scenario studied in this work.
Yazdanpanah, Vahid; Gerding, Enrico H; Stein, Sebastian; Dastani, Mehdi; Jonker, Catholijn M; Norman, Timothy J; Ramchurn, Sarvapali D
Responsibility ascription in trustworthy autonomous systems Proceedings Article
In: Embedding AI in Society (18/02/21 - 19/02/21), 2021.
@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},
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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.},
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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.
Beal, Ryan James; Middleton, Stuart; Norman, Timothy; Ramchurn, Sarvapali
Combining machine learning and human experts to predict match outcomes in football: A baseline model Proceedings Article
In: The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (02/02/21 - 09/02/21), 2021.
@inproceedings{soton445607,
title = {Combining machine learning and human experts to predict match outcomes in football: A baseline model},
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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.},
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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.
Lhopital, Sacha; Aknine, Samir; Ramchurn, Sarvapali; Thavonekham, Vincent; Vu, Huan
Decentralised control of intelligent devices: a healthcare facility study Proceedings Article
In: Bassiliades, Nick; Chalkiadakis, Georgios; de Jonge, Dave (Ed.): Multi-Agent Systems and Agreement Technologies - 17th European Conference, EUMAS 2020, and 7th International Conference, AT 2020, Revised Selected Papers, pp. 20–36, Springer, 2021.
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abstract = {ensuremathWe 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 },
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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>
Ryan, James Beal; Chalkiadakis, Georgios; Norman, Timothy; Ramchurn, Sarvapali
Optimising long-term outcomes using real-world fluent objectives: an application to football Proceedings Article
In: 20th International Conference on Autonomous Agents and Multiagent Systems (03/05/21 - 07/05/21), pp. 196–204, 2021.
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title = {Optimising long-term outcomes using real-world fluent objectives: an application to football},
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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
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%.},
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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%.
Merhej, Charbel; Ryan, James Beal; Matthews, Tim; Ramchurn, Sarvapali
What happened next? Using deep learning to value defensive actions in football event-data Proceedings Article
In: KDD 2021 (14/08/21 - 18/08/21), pp. 3394–3403, 2021.
@inproceedings{soton449656,
title = {What happened next? Using deep learning to value defensive actions in football event-data},
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url = {https://eprints.soton.ac.uk/449656/},
year = {2021},
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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.},
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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.
2020
Beal, Ryan James; Norman, Timothy; Ramchurn, Sarvapali
Optimising daily fantasy sports teams with artificial intelligence Journal Article
In: International Journal of Computer Science in Sport, vol. 19, no. 2, 2020.
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abstract = {This paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season},
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This paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season
Beal, Ryan James; Norman, Timothy; Ramchurn, Sarvapali
A critical comparison of machine learning classifiers to predict match outcomes in the NFL Journal Article
In: International Journal of Computer Science in Sport, vol. 19, no. 2, 2020.
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title = {A critical comparison of machine learning classifiers to predict match outcomes in the NFL},
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year = {2020},
date = {2020-12-01},
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abstract = {In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem presented by American Football. Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). We test the nine different classifier techniques using a total of 42 features for each team and we find that the best performing algorithms are able to improve one previous published works. The algoriothms achieve an accuracy of between 44.64% for a Guassian Process classifier to 67.53% with a Na"ive Bayes classifer. We also test each classifier on a year by year basis and compare our results to those of the bookmakers and other leading academic papers.},
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In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem presented by American Football. Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). We test the nine different classifier techniques using a total of 42 features for each team and we find that the best performing algorithms are able to improve one previous published works. The algoriothms achieve an accuracy of between 44.64% for a Guassian Process classifier to 67.53% with a Na"ive Bayes classifer. We also test each classifier on a year by year basis and compare our results to those of the bookmakers and other leading academic papers.
Rigas, Emmanouil S; Gerding, Enrico; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick
Mechanism design for efficient allocation of electric vehicles to charging stations Proceedings Article
In: SETN 2020: 11th Hellenic Conference on Artificial Intelligence, pp. 10–15, 2020.
@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},
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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.},
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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.
Oluwasuji, Olabambo Ifeoluwa; Malik, Obaid; Zhang, Jie; Ramchurn, Sarvapali Dyanand
Solving the fair electric load shedding problem in developing countries Journal Article
In: Autonomous Agents and Multi-Agent Systems, vol. 34, no. 1, pp. 12, 2020.
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Oluwasuji, Olabambo Ifeoluwa; Malik, Obaid; Zhang, Jie; Ramchurn, Sarvapali Dyanand
Solving the fair electric load shedding problem in developing countries Journal Article
In: Auton. Agents Multi Agent Syst., vol. 34, no. 1, pp. 12, 2020.
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Seitaridis, Andreas; Rigas, Emmanouil S; Bassiliades, Nick; Ramchurn, Sarvapali D
An agent-based negotiation scheme for the distribution of electric vehicles across a set of charging stations Journal Article
In: Simul. Model. Pract. Theory, vol. 100, pp. 102040, 2020.
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Koufakis, Alexandros; Rigas, Emmanouil S; Bassiliades, Nick; Ramchurn, Sarvapali D
Offline and Online Electric Vehicle Charging Scheduling With V2V Energy Transfer Journal Article
In: IEEE Trans. Intell. Transp. Syst., vol. 21, no. 5, pp. 2128–2138, 2020.
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Beal, Ryan; Changder, Narayan; Norman, Timothy D; Ramchurn, Sarvapali D
Learning the Value of Teamwork to Form Efficient Teams Proceedings Article
In: 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, pp. 7063–7070, AAAI Press, 2020.
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Changder, Narayan; Aknine, Samir; Ramchurn, Sarvapali D; Dutta, Animesh
ODSS: Efficient Hybridization for Optimal Coalition Structure Generation Proceedings Article
In: 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, pp. 7079–7086, AAAI Press, 2020.
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Beal, Ryan; Chalkiadakis, Georgios; Norman, Timothy J; Ramchurn, Sarvapali D
Optimising Game Tactics for Football Proceedings Article
In: Seghrouchni, Amal El Fallah; Sukthankar, Gita; An, Bo; -, Neil Yorke (Ed.): Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 2020, pp. 141–149, International Foundation for Autonomous Agents and Multiagent Systems, 2020.
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Oluwasuji, Olabambo I; Malik, Obaid; Zhang, Jie; Ramchurn, Sarvapali D
Solving the Fair Electric Load Shedding Problem in Developing Countries Proceedings Article
In: Seghrouchni, Amal El Fallah; Sukthankar, Gita; An, Bo; -, Neil Yorke (Ed.): Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 2020, pp. 2155–2157, International Foundation for Autonomous Agents and Multiagent Systems, 2020.
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Wu, Feng; Ramchurn, Sarvapali D
Monte-Carlo Tree Search for Scalable Coalition Formation Proceedings Article
In: Bessiere, Christian (Ed.): Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 407–413, ijcai.org, 2020.
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Rigas, Emmanouil S; Gerding, Enrico; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick
Mechanism design for efficient allocation of electric vehicles to charging stations Proceedings Article
In: Spyropoulos, Constantine D; Varlamis, Iraklis; Androutsopoulos, Ion; Malakasiotis, Prodromos (Ed.): SETN 2020: 11th Hellenic Conference on Artificial Intelligence, Athens, Greece, September 2-4, 2020, pp. 10–15, ACM, 2020.
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Beal, Ryan; Chalkiadakis, Georgios; Norman, Timothy J; Ramchurn, Sarvapali D
Optimising Game Tactics for Football Journal Article
In: CoRR, vol. abs/2003.10294, 2020.
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Capezzuto, Luca; Tarapore, Danesh; Ramchurn, Sarvapali D
Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints Journal Article
In: CoRR, vol. abs/2003.13806, 2020.
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Rigas, Emmanouil; Gerding, Enrico; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick
Mechanism Design for Efficient Online and Offline Allocation of Electric Vehicles to Charging Stations Journal Article
In: CoRR, vol. abs/2007.09715, 2020.
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2019
Abeywickrama, Dhaminda B; Cirstea, Corina; Ramchurn, Sarvapali D
Model Checking Human-Agent Collectives for Responsible AI Proceedings Article
In: 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1–8, IEEE 2019.
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Beal, Ryan; Norman, Timothy J; Ramchurn, Sarvapali D
Artificial intelligence for team sports: a survey Journal Article
In: The Knowledge Engineering Review, vol. 34, 2019.
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Abioye, Ayodeji Opeyemi; Prior, Stephen D; Thomas, Glyn T; Saddington, Peter; Ramchurn, Sarvapali D
Multimodal human aerobotic interaction Book Section
In: Unmanned Aerial Vehicles: Breakthroughs in Research and Practice, pp. 142–165, IGI Global, 2019.
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Fuentes, Carolina; Porcheron, Martin; Fischer, Joel E; Costanza, Enrico; Malilk, Obaid; Ramchurn, Sarvapali D
Tracking the Consumption of Home Essentials Proceedings Article
In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 639, ACM 2019.
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