No data available.

http://trec.nist.gov/pubs/trec21/papers/HAC.crowd.final.pdf

@inproceedings{DBLP:conf/trec/SimpsonRPR12,
title = {Using a Bayesian Model to Combine LDA Features with Crowdsourced Responses},
author = {Edwin Simpson and Steven Reece and Antonio Penta and Sarvapali D Ramchurn},
url = {http://trec.nist.gov/pubs/trec21/papers/HAC.crowd.final.pdf},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of The Twenty-First Text REtrieval Conference, TREC 2012, Gaithersburg, Maryland, USA, November 6-9, 2012},
crossref = {DBLP:conf/trec/2012},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings} }

We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our solution imposes additional constraints on negotiation such that it reduces a complex interdependent multi-issue problem to one that is tractable. We prove that using our protocol, agents can reach a Pareto-optimal, dominant strategy equilibrium in a decentralized and timely fashion. We empirically evaluate our approach in a realistic setting. In this case, we show that energy exchange can be useful in reducing the capacity of the energy storage devices in homes by close to 40%

http://eprints.soton.ac.uk/272479/

@inproceedings{eps272479,
title = {A negotiation protocol for multiple interdependent issues negotiation over energy exchange},
author = {Muddasser Alam and Alex Rogers and Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/272479/},
year = {2011},
date = {2011-01-01},
booktitle = {IJCAI Workshop on AI for an Intelligent Planet},
abstract = {We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our solution imposes additional constraints on negotiation such that it reduces a complex interdependent multi-issue problem to one that is tractable. We prove that using our protocol, agents can reach a Pareto-optimal, dominant strategy equilibrium in a decentralized and timely fashion. We empirically evaluate our approach in a realistic setting. In this case, we show that energy exchange can be useful in reducing the capacity of the energy storage devices in homes by close to 40%},
note = {Event Dates: July-16},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings} }

We introduce a novel distributed algorithm for multi-agent task allocation problems where the sets of tasks and agents constantly change over time. We build on an existing anytime algorithm (fast-max-sum), and give it significant new capa- bilities: namely, an online pruning procedure that simplifies the problem, and a branch-and-bound technique that reduces the search space. This allows us to scale to problems with hundreds of tasks and agents. We empirically evaluate our algorithm against established benchmarks and find that, even in such large environments, a solution is found up to 31% faster, and with up to 23% more utility, than state-of-the-art approximation algorithms. In addition, our algorithm sends up to 30% fewer messages than current approaches when the set of agents or tasks changes.

http://eprints.soton.ac.uk/272233/

@inproceedings{eps272233,
title = {A Distributed Anytime Algorithm for Dynamic Task Allocation in Multi-Agent Systems},
author = {Kathryn Macarthur and Ruben Stranders and Sarvapali Ramchurn and Nick Jennings},
url = {http://eprints.soton.ac.uk/272233/},
year = {2011},
date = {2011-01-01},
booktitle = {Twenty-Fifth Conference on Artificial Intelligence (AAAI)},
pages = {701--706},
publisher = {AAAI Press},
abstract = {We introduce a novel distributed algorithm for multi-agent task allocation problems where the sets of tasks and agents constantly change over time. We build on an existing anytime algorithm (fast-max-sum), and give it significant new capa- bilities: namely, an online pruning procedure that simplifies the problem, and a branch-and-bound technique that reduces the search space. This allows us to scale to problems with hundreds of tasks and agents. We empirically evaluate our algorithm against established benchmarks and find that, even in such large environments, a solution is found up to 31% faster, and with up to 23% more utility, than state-of-the-art approximation algorithms. In addition, our algorithm sends up to 30% fewer messages than current approaches when the set of agents or tasks changes.},
note = {Event Dates: August 7-11, 2011},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings} }

Multi-agent task allocation problems pervade a wide range of real-world applications, such as search and rescue in disaster manage- ment, or grid computing. In these applications, where agents are given tasks to perform in parallel, it is often the case that the performance of all agents is judged based on the time taken by the slowest agent to complete its tasks. Hence, efficient distribution of tasks across het- erogeneous agents is important to ensure a short completion time. An equivalent problem to this can be found in operations research, and is known as scheduling jobs on unrelated parallel machines (also known as Rensuremath|ensuremath|Cmax). In this paper, we draw parallels between unrelated parallel machine scheduling and multi-agent task allocation problems, and, in so doing, we present the decentralised task distribution algorithm (DTDA), the first decentralised solution to Rensuremath|ensuremath|Cmax. Empirical evaluation of the DTDA is shown to generate solutions within 86?97% of the optimal on sparse graphs, in the best case, whilst providing a very good estimate (within 1%) of the global solution at each agent.

http://eprints.soton.ac.uk/272234/

@inproceedings{eps272234,
title = {Decentralised Parallel Machine Scheduling for Multi-Agent Task Allocation},
author = {Kathryn Macarthur and Meritxell Vinyals and Alessandro Farinelli and Sarvapali Ramchurn and Nick Jennings},
url = {http://eprints.soton.ac.uk/272234/},
year = {2011},
date = {2011-01-01},
booktitle = {Fourth International Workshop on Optimisation in Multi-Agent Systems},
abstract = {Multi-agent task allocation problems pervade a wide range of real-world applications, such as search and rescue in disaster manage- ment, or grid computing. In these applications, where agents are given tasks to perform in parallel, it is often the case that the performance of all agents is judged based on the time taken by the slowest agent to complete its tasks. Hence, efficient distribution of tasks across het- erogeneous agents is important to ensure a short completion time. An equivalent problem to this can be found in operations research, and is known as scheduling jobs on unrelated parallel machines (also known as Rensuremath|ensuremath|Cmax). In this paper, we draw parallels between unrelated parallel machine scheduling and multi-agent task allocation problems, and, in so doing, we present the decentralised task distribution algorithm (DTDA), the first decentralised solution to Rensuremath|ensuremath|Cmax. Empirical evaluation of the DTDA is shown to generate solutions within 86?97% of the optimal on sparse graphs, in the best case, whilst providing a very good estimate (within 1%) of the global solution at each agent.},
note = {Event Dates: May 3, 2011},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings} }

No data available.

http://eprints.soton.ac.uk/272746/

@incollection{eps272746,
title = {Gaussian Processes for Time Series Prediction},
author = {Michael A. Osborne and Alex Rogers and Stephen J. Roberts and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/272746/},
year = {2011},
date = {2011-01-01},
booktitle = {Bayesian Time Series Models},
pages = {341--360},
publisher = {Cambridge University Press},
note = {Chapter: 16},
keywords = {},
pubstate = {published},
tppubtype = {incollection} }

With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs.

http://eprints.soton.ac.uk/272015/

@article{eps272015,
title = {Agent-based homeostatic control for green energy in the smart grid},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272015/},
year = {2011},
date = {2011-01-01},
journal = {ACM Transactions on Intelligent Systems and Technology},
volume = {2},
number = {4},
pages = {35:1--35:28},
abstract = {With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs.}, keywords = {},
pubstate = {published},
tppubtype = {article} }

Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e. it exhibits a similar efficiency).

http://eprints.soton.ac.uk/271985/

@inproceedings{eps271985,
title = {Agent-based control for decentralised demand side management in the smart grid},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/271985/},
year = {2011},
date = {2011-01-01},
booktitle = {The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011)},
pages = {5--12},
abstract = {Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e. it exhibits a similar efficiency).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

No data available.

http://eprints.soton.ac.uk/272478/

@inproceedings{eps272478,
title = {CollabMap: Augmenting Maps using the Wisdom of Crowds},
author = {Ruben Stranders and Sarvapali Ramchurn and Bing Shi and Nick Jennings},
url = {http://eprints.soton.ac.uk/272478/},
year = {2011},
date = {2011-01-01},
booktitle = {Third Human Computation Workshop},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}