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2013
Alam, Muddasser; Rogers, Alex; Ramchurn, Sarvapali D.
Interdependent multi-issue negotiation for energy exchange in remote communities Proceedings Article
In: International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE), 2013.
Links | BibTeX | Tags: cooperative exchange, Energy, home energy management, mas, Multi-agent scheduling, smart home
@inproceedings{eps357186,
title = {Interdependent multi-issue negotiation for energy exchange in remote communities},
author = {Muddasser Alam and Alex Rogers and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/357186/},
year = {2013},
date = {2013-01-01},
booktitle = {International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE)},
keywords = {cooperative exchange, Energy, home energy management, mas, Multi-agent scheduling, smart home},
pubstate = {published},
tppubtype = {inproceedings}
}
Fischer, Joel E.; Ramchurn, Sarvapali D.; Osborne, Michael A.; Parson, Oliver; Huynh, Trung Dong; Alam, Muddasser; Pantidi, Nadia; Moran, Stuart; Bachour, Khaled; Reece, Steven; Costanza, Enrico; Rodden, Tom; Jennings, Nicholas R.
Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling Proceedings Article
In: International Conference on Intelligent User Interfaces, pp. 383–394, 2013.
Abstract | Links | BibTeX | Tags: Energy, hai, home energy management, human-agent interaction
@inproceedings{eps346991,
title = {Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling},
author = {Joel E. Fischer and Sarvapali D. Ramchurn and Michael A. Osborne and Oliver Parson and Trung Dong Huynh and Muddasser Alam and Nadia Pantidi and Stuart Moran and Khaled Bachour and Steven Reece and Enrico Costanza and Tom Rodden and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/346991/},
year = {2013},
date = {2013-01-01},
booktitle = {International Conference on Intelligent User Interfaces},
pages = {383–394},
abstract = {We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.},
keywords = {Energy, hai, home energy management, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.
Truong, Ngoc Cuong; McInerney, James; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Forecasting multi-appliance usage for smart home energy management Proceedings Article
In: 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), 2013.
Links | BibTeX | Tags: Energy, hai, home energy management, human-agent interaction
@inproceedings{eps351242,
title = {Forecasting multi-appliance usage for smart home energy management},
author = {Ngoc Cuong Truong and James McInerney and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351242/},
year = {2013},
date = {2013-01-01},
booktitle = {23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)},
keywords = {Energy, hai, home energy management, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Towards appliance usage prediction for home energy management Proceedings Article
In: ACM E-Energy 2013, 2013.
Links | BibTeX | Tags: home energy management, usage prediction
@inproceedings{eps351240,
title = {Towards appliance usage prediction for home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351240/},
year = {2013},
date = {2013-01-01},
booktitle = {ACM E-Energy 2013},
keywords = {home energy management, usage prediction},
pubstate = {published},
tppubtype = {inproceedings}
}
2012
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
Abstract | Links | BibTeX | Tags: electricity, Energy, hai, home energy management, human-agent interaction
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {electricity, Energy, hai, home energy management, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
Richardson, Darren P.; Costanza, Enrico; Ramchurn, Sarvapali D.
Evaluating semi-automatic annotation of domestic energy consumption as a memory aid Proceedings Article
In: UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 613–614, 2012.
Abstract | Links | BibTeX | Tags: Energy, hai, home energy management, human-agent interaction
@inproceedings{eps349083,
title = {Evaluating semi-automatic annotation of domestic energy consumption as a memory aid},
author = {Darren P. Richardson and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/349083/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {613–614},
abstract = {Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.},
keywords = {Energy, hai, home energy management, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
Abstract | Links | BibTeX | Tags: electricity, Energy, home energy management
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {electricity, Energy, home energy management},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
1.
Alam, Muddasser; Rogers, Alex; Ramchurn, Sarvapali D.
Interdependent multi-issue negotiation for energy exchange in remote communities Proceedings Article
In: International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE), 2013.
@inproceedings{eps357186,
title = {Interdependent multi-issue negotiation for energy exchange in remote communities},
author = {Muddasser Alam and Alex Rogers and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/357186/},
year = {2013},
date = {2013-01-01},
booktitle = {International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2.
Fischer, Joel E.; Ramchurn, Sarvapali D.; Osborne, Michael A.; Parson, Oliver; Huynh, Trung Dong; Alam, Muddasser; Pantidi, Nadia; Moran, Stuart; Bachour, Khaled; Reece, Steven; Costanza, Enrico; Rodden, Tom; Jennings, Nicholas R.
Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling Proceedings Article
In: International Conference on Intelligent User Interfaces, pp. 383–394, 2013.
@inproceedings{eps346991,
title = {Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling},
author = {Joel E. Fischer and Sarvapali D. Ramchurn and Michael A. Osborne and Oliver Parson and Trung Dong Huynh and Muddasser Alam and Nadia Pantidi and Stuart Moran and Khaled Bachour and Steven Reece and Enrico Costanza and Tom Rodden and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/346991/},
year = {2013},
date = {2013-01-01},
booktitle = {International Conference on Intelligent User Interfaces},
pages = {383–394},
abstract = {We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.
3.
Truong, Ngoc Cuong; McInerney, James; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Forecasting multi-appliance usage for smart home energy management Proceedings Article
In: 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), 2013.
@inproceedings{eps351242,
title = {Forecasting multi-appliance usage for smart home energy management},
author = {Ngoc Cuong Truong and James McInerney and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351242/},
year = {2013},
date = {2013-01-01},
booktitle = {23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
4.
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Towards appliance usage prediction for home energy management Proceedings Article
In: ACM E-Energy 2013, 2013.
@inproceedings{eps351240,
title = {Towards appliance usage prediction for home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351240/},
year = {2013},
date = {2013-01-01},
booktitle = {ACM E-Energy 2013},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
5.
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
6.
Richardson, Darren P.; Costanza, Enrico; Ramchurn, Sarvapali D.
Evaluating semi-automatic annotation of domestic energy consumption as a memory aid Proceedings Article
In: UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 613–614, 2012.
@inproceedings{eps349083,
title = {Evaluating semi-automatic annotation of domestic energy consumption as a memory aid},
author = {Darren P. Richardson and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/349083/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {613–614},
abstract = {Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.
7.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
2013
Alam, Muddasser; Rogers, Alex; Ramchurn, Sarvapali D.
Interdependent multi-issue negotiation for energy exchange in remote communities Proceedings Article
In: International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE), 2013.
Links | BibTeX | Tags: cooperative exchange, Energy, home energy management, mas, Multi-agent scheduling, smart home
@inproceedings{eps357186,
title = {Interdependent multi-issue negotiation for energy exchange in remote communities},
author = {Muddasser Alam and Alex Rogers and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/357186/},
year = {2013},
date = {2013-01-01},
booktitle = {International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE)},
keywords = {cooperative exchange, Energy, home energy management, mas, Multi-agent scheduling, smart home},
pubstate = {published},
tppubtype = {inproceedings}
}
Fischer, Joel E.; Ramchurn, Sarvapali D.; Osborne, Michael A.; Parson, Oliver; Huynh, Trung Dong; Alam, Muddasser; Pantidi, Nadia; Moran, Stuart; Bachour, Khaled; Reece, Steven; Costanza, Enrico; Rodden, Tom; Jennings, Nicholas R.
Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling Proceedings Article
In: International Conference on Intelligent User Interfaces, pp. 383–394, 2013.
Abstract | Links | BibTeX | Tags: Energy, hai, home energy management, human-agent interaction
@inproceedings{eps346991,
title = {Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling},
author = {Joel E. Fischer and Sarvapali D. Ramchurn and Michael A. Osborne and Oliver Parson and Trung Dong Huynh and Muddasser Alam and Nadia Pantidi and Stuart Moran and Khaled Bachour and Steven Reece and Enrico Costanza and Tom Rodden and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/346991/},
year = {2013},
date = {2013-01-01},
booktitle = {International Conference on Intelligent User Interfaces},
pages = {383–394},
abstract = {We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.},
keywords = {Energy, hai, home energy management, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.
Truong, Ngoc Cuong; McInerney, James; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Forecasting multi-appliance usage for smart home energy management Proceedings Article
In: 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), 2013.
Links | BibTeX | Tags: Energy, hai, home energy management, human-agent interaction
@inproceedings{eps351242,
title = {Forecasting multi-appliance usage for smart home energy management},
author = {Ngoc Cuong Truong and James McInerney and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351242/},
year = {2013},
date = {2013-01-01},
booktitle = {23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)},
keywords = {Energy, hai, home energy management, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Towards appliance usage prediction for home energy management Proceedings Article
In: ACM E-Energy 2013, 2013.
Links | BibTeX | Tags: home energy management, usage prediction
@inproceedings{eps351240,
title = {Towards appliance usage prediction for home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351240/},
year = {2013},
date = {2013-01-01},
booktitle = {ACM E-Energy 2013},
keywords = {home energy management, usage prediction},
pubstate = {published},
tppubtype = {inproceedings}
}
2012
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
Abstract | Links | BibTeX | Tags: electricity, Energy, hai, home energy management, human-agent interaction
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {electricity, Energy, hai, home energy management, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
Richardson, Darren P.; Costanza, Enrico; Ramchurn, Sarvapali D.
Evaluating semi-automatic annotation of domestic energy consumption as a memory aid Proceedings Article
In: UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 613–614, 2012.
Abstract | Links | BibTeX | Tags: Energy, hai, home energy management, human-agent interaction
@inproceedings{eps349083,
title = {Evaluating semi-automatic annotation of domestic energy consumption as a memory aid},
author = {Darren P. Richardson and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/349083/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {613–614},
abstract = {Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.},
keywords = {Energy, hai, home energy management, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
Abstract | Links | BibTeX | Tags: electricity, Energy, home energy management
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {electricity, Energy, home energy management},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
1.
Alam, Muddasser; Rogers, Alex; Ramchurn, Sarvapali D.
Interdependent multi-issue negotiation for energy exchange in remote communities Proceedings Article
In: International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE), 2013.
@inproceedings{eps357186,
title = {Interdependent multi-issue negotiation for energy exchange in remote communities},
author = {Muddasser Alam and Alex Rogers and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/357186/},
year = {2013},
date = {2013-01-01},
booktitle = {International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2.
Fischer, Joel E.; Ramchurn, Sarvapali D.; Osborne, Michael A.; Parson, Oliver; Huynh, Trung Dong; Alam, Muddasser; Pantidi, Nadia; Moran, Stuart; Bachour, Khaled; Reece, Steven; Costanza, Enrico; Rodden, Tom; Jennings, Nicholas R.
Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling Proceedings Article
In: International Conference on Intelligent User Interfaces, pp. 383–394, 2013.
@inproceedings{eps346991,
title = {Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling},
author = {Joel E. Fischer and Sarvapali D. Ramchurn and Michael A. Osborne and Oliver Parson and Trung Dong Huynh and Muddasser Alam and Nadia Pantidi and Stuart Moran and Khaled Bachour and Steven Reece and Enrico Costanza and Tom Rodden and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/346991/},
year = {2013},
date = {2013-01-01},
booktitle = {International Conference on Intelligent User Interfaces},
pages = {383–394},
abstract = {We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.
3.
Truong, Ngoc Cuong; McInerney, James; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Forecasting multi-appliance usage for smart home energy management Proceedings Article
In: 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), 2013.
@inproceedings{eps351242,
title = {Forecasting multi-appliance usage for smart home energy management},
author = {Ngoc Cuong Truong and James McInerney and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351242/},
year = {2013},
date = {2013-01-01},
booktitle = {23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
4.
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Towards appliance usage prediction for home energy management Proceedings Article
In: ACM E-Energy 2013, 2013.
@inproceedings{eps351240,
title = {Towards appliance usage prediction for home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351240/},
year = {2013},
date = {2013-01-01},
booktitle = {ACM E-Energy 2013},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
5.
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
6.
Richardson, Darren P.; Costanza, Enrico; Ramchurn, Sarvapali D.
Evaluating semi-automatic annotation of domestic energy consumption as a memory aid Proceedings Article
In: UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 613–614, 2012.
@inproceedings{eps349083,
title = {Evaluating semi-automatic annotation of domestic energy consumption as a memory aid},
author = {Darren P. Richardson and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/349083/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {613–614},
abstract = {Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.
7.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
Selected Publications
Multi-agent signal-less intersection management with dynamic platoon formation
No data available.
https://eprints.soton.ac.uk/478647/
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response
No data available.
https://eprints.soton.ac.uk/477553/
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The effect of data visualisation quality and task density on human-swarm interaction
No data available.
https://eprints.soton.ac.uk/479970/
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Demonstrating performance benefits of human-swarm teaming
Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.
https://eprints.soton.ac.uk/479903/
@inproceedings{soton479903,
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2013
Alam, Muddasser; Rogers, Alex; Ramchurn, Sarvapali D.
Interdependent multi-issue negotiation for energy exchange in remote communities Proceedings Article
In: International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE), 2013.
@inproceedings{eps357186,
title = {Interdependent multi-issue negotiation for energy exchange in remote communities},
author = {Muddasser Alam and Alex Rogers and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/357186/},
year = {2013},
date = {2013-01-01},
booktitle = {International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Fischer, Joel E.; Ramchurn, Sarvapali D.; Osborne, Michael A.; Parson, Oliver; Huynh, Trung Dong; Alam, Muddasser; Pantidi, Nadia; Moran, Stuart; Bachour, Khaled; Reece, Steven; Costanza, Enrico; Rodden, Tom; Jennings, Nicholas R.
Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling Proceedings Article
In: International Conference on Intelligent User Interfaces, pp. 383–394, 2013.
@inproceedings{eps346991,
title = {Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling},
author = {Joel E. Fischer and Sarvapali D. Ramchurn and Michael A. Osborne and Oliver Parson and Trung Dong Huynh and Muddasser Alam and Nadia Pantidi and Stuart Moran and Khaled Bachour and Steven Reece and Enrico Costanza and Tom Rodden and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/346991/},
year = {2013},
date = {2013-01-01},
booktitle = {International Conference on Intelligent User Interfaces},
pages = {383–394},
abstract = {We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.
Truong, Ngoc Cuong; McInerney, James; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Forecasting multi-appliance usage for smart home energy management Proceedings Article
In: 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), 2013.
@inproceedings{eps351242,
title = {Forecasting multi-appliance usage for smart home energy management},
author = {Ngoc Cuong Truong and James McInerney and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351242/},
year = {2013},
date = {2013-01-01},
booktitle = {23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Towards appliance usage prediction for home energy management Proceedings Article
In: ACM E-Energy 2013, 2013.
@inproceedings{eps351240,
title = {Towards appliance usage prediction for home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351240/},
year = {2013},
date = {2013-01-01},
booktitle = {ACM E-Energy 2013},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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2012
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
Richardson, Darren P.; Costanza, Enrico; Ramchurn, Sarvapali D.
Evaluating semi-automatic annotation of domestic energy consumption as a memory aid Proceedings Article
In: UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 613–614, 2012.
@inproceedings{eps349083,
title = {Evaluating semi-automatic annotation of domestic energy consumption as a memory aid},
author = {Darren P. Richardson and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/349083/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {613–614},
abstract = {Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.