Orchid Project

@article{
  orchid46,
  title = {Living with energy monitoring: implications for designing interactive information ecologies},
  author = {J.E. Fischer and N Pantidi and B Bedwell and J Colley and S Rennick Egglestone and T Rodden},
  year = {2013},
  journal = {Journal of Transactions on Computer-Human Interactions (ToCHI)},
  keywords = {Human Computer Interaction, Applications, Flexible Autonomy},
url = {http://www.orchid.ac.uk/eprints/46/} }

@article{
  orchid30,
  month = {May},
  title = {Deploying the Max-Sum Algorithm for Coordination and Task Allocation of Unmanned Aerial Vehicles for Live Aerial Imagery Collection.},
  author = {Francesco Delle Fave and Alex Rogers and Zhe Xu and Salah Sukkarieh and Nicholas R Jennings},
  publisher = {IEEE, St Pauls, USA},
  year = {2012},
  journal = {Proc. IEEE Int. Conf. on Robotics and Automation (ICRA)},
  keywords = {Decentralised Control, Applications, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/30/1/sample%5fnew.pdf},
url = {http://www.orchid.ac.uk/eprints/30/} }

@inproceedings{
  orchid45,
  booktitle = {ACM Converence on Computer-Supported Cooperative Work},
  month = {February},
  title = {Serious Mixed Reality Games},
  author = {J.E. Fischer and M Flintham and D Price and J Goulding and N Pantidi and T Rodden},
  year = {2012},
  keywords = {Human-Agent Interaction, Applications, Flexible Autonomy},
url = {http://www.orchid.ac.uk/eprints/45/} }

@article{
  orchid23,
  title = {Coalitional stability in structured environments},
  author = {G Chalkiadakis and V Markakis and Nicholas R Jennings},
  publisher = {AAMAS},
  year = {2012},
  journal = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems},
  keywords = {Human-Agent Interaction, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/23/1/cmjCStabStrEnv2012.pdf},
url = {http://www.orchid.ac.uk/eprints/23/} }

@article{
  orchid42,
  title = {Optimal decentralised dispatch of embedded generation in the smart grid},
  author = {Sam Miller and Sarvapali D Ramchurn and Alex Rogers},
  publisher = {AAMAS},
  year = {2012},
  journal = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems.},
  keywords = {Decentralised Control, Applications, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/42/1/AAMAS2012GenCoordCameraReady.pdf},
url = {http://www.orchid.ac.uk/eprints/42/} }

@article{
  orchid11,
  volume = {9},
  number = {1},
  title = {Real-Time Information Processing of Environmental Sensor Network Data},
  author = {Michael A Osborne and Stephen J Roberts and Alex Rogers and Nicholas R Jennings},
  year = {2012},
  journal = {ACM Transactions on Sensor Networks},
  keywords = {Agent-Based Computing, Applications, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/11/1/tosn%5fgp%5frevised.pdf},
  url = {http://www.orchid.ac.uk/eprints/11/},
abstract = {In this paper, we consider the problem faced by a sensor network operator who must infer, in real-time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network. We describe a powerful and generic approach built upon an efficient multi-output Gaussian process that facilitates this information acquisition and processing. Our algorithm allows effective inference even with minimal domain knowledge, and we further introduce a formulation of Bayesian Monte Carlo to permit the principled management of the hyperparameters introduced by our flexible models. We demonstrate how our methods can be applied in cases where the data is delayed, intermittently missing, censored and/or correlated. We validate our approach using data collected from three networks of weather sensors and show that it yields better inference performance than both conventional independent Gaussian processes and the Kalman filter. Finally, we show that our formalism efficiently re-uses previous computations by following an online update procedure as new data sequentially arrives, and that this results in a four-fold increase in computational speed in the largest cases considered.} }

@article{
  orchid22,
  title = {Efficient sharing of conflicting information in large decentralised teams},
  author = {Oleksandr Pryymak and Alex Rogers and Nicholas R Jennings},
  publisher = {AAMAS},
  year = {2012},
  journal = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems},
  keywords = {Decentralised Control, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/22/1/paper%5faamas12.pdf},
url = {http://www.orchid.ac.uk/eprints/22/} }

@incollection{
  orchid32,
  booktitle = {Decision Making with Imperfect Decision Makers},
  title = {Bayesian combination of weak decision makers.},
  author = {Edwin Simpson and Stephen J Roberts and Ioannis Psorakis and Chris Lintott},
  publisher = {Springer},
  year = {2012},
  series = {Intelligient Systems Reference Library},
  keywords = {Machine Learning, Applications, Incentive Engineering},
url = {http://www.orchid.ac.uk/eprints/32/} }

@article{
  orchid28,
  title = {A model-based online mechanism with pre-commitment and its application to electric vehicle charging},
  author = {Sebastian Stein and Enrico H Gerding and Valentin Robu and Nicholas R Jennings},
  publisher = {AAMAS},
  year = {2012},
  journal = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems,},
  keywords = {Mechanism Design, Applications, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/28/1/camera%5fready%5ffinal.pdf},
url = {http://www.orchid.ac.uk/eprints/28/} }

@article{
  orchid26,
  title = {Decentralised channel allocation and information sharing for teams of cooperative agents},
  author = {Sebastian Stein and S Williamson and Nicholas R Jennings},
  publisher = {AAMAS},
  year = {2012},
  journal = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems,},
  keywords = {Decentralised Control, Applications, Agile Teaming, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/26/1/camera%5fready%5ffinal.pdf},
url = {http://www.orchid.ac.uk/eprints/26/} }

@article{
  orchid25,
  title = {Decentralised ControlOPS and bandits: Exploration and exploitation in decentralised coordination},
  author = {Ruben Stranders and Long Tran-Thanh and Francesco Delle Fave and Alex Rogers and Nicholas R Jennings},
  publisher = {AAMAS},
  year = {2012},
  journal = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems},
  keywords = {Decentralised Control, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/25/1/mab%5fdcops.pdf},
url = {http://www.orchid.ac.uk/eprints/25/} }

@article{
  orchid29,
  title = {A New Approach to Betweenness Centrality Based on the Shapley Value},
  author = {Piotr Szczepanski and Tomasz Michalak and Talal Rahwan},
  publisher = {AAMAS},
  year = {2012},
  journal = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems,},
  keywords = {Mechanism Design, Agile Teaming},
url = {http://www.orchid.ac.uk/eprints/29/} }

@article{
  orchid27,
  title = {Decentralized Bayesian reinforcement learning for online agent collaboration},
  author = {W L Teacy and G Chalkiadakis and Alessandro Farinelli and Alex Rogers and Nicholas R Jennings and S McClean and G Parr},
  publisher = {AAMAS},
  year = {2012},
  journal = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems,},
  keywords = {Machine Learning, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/27/1/AAMAS2012%5f0089%5f7016861a9.pdf},
url = {http://www.orchid.ac.uk/eprints/27/} }

@article{
  orchid12,
  volume = {25},
  number = {2},
  title = {Long?Term Information Collection with Energy Harvesting Wireless Sensors: A Multi-Armed Bandit Based Approac},
  author = {Long Tran-Thanh and Alex Rogers and Nicholas R Jennings},
  year = {2012},
  pages = {352--394},
  journal = {Journal of Autonomous Agents and Multi-agent Systems},
  keywords = {Agent-Based Computing, Applications, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/12/1/LTT%5fJAAMAS2010.pdf},
  url = {http://www.orchid.ac.uk/eprints/12/},
abstract = {This paper reports on the development of a multi?agent approach to long-term information collection in networks of energy harvesting wireless sensors. In particular, we focus on developing energy management and data routing policies that adapt their behaviour according to the energy that is harvested, in order to maximise the amount of information collected given the available energy budget. In so doing, we introduce a new energy management technique, based on multi?armed bandit learning, that allows each agent to adaptively allocate its energy budget across the tasks of data sampling, receiving and transmitting. By using this approach, each agent can learn the optimal energy budget settings that give it efficient information collection in the long run. Then, we propose two novel decentralised multi?hop algorithms for data routing. The first proveably maximises the information throughput in the network, but can sometimes involve high communication cost. The second algorithm provides near?optimal performance, but with reduced computational and communication costs. Finally, we demonstrate that, by using our approaches for energy management and routing, we can achieve a 120 % improvement in long term information collection against state?of?the?art benchmarks.} }

@article{
  orchid24,
  title = {On coalition formation with sparse synergies},
  author = {Thomas Voice and Sarvapali D Ramchurn and Nicholas R Jennings},
  publisher = {AAMAS},
  year = {2012},
  journal = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems},
  keywords = {Human-Agent Interaction, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/24/1/AAMAS2012%5f0446%5f388b64a15.pdf},
url = {http://www.orchid.ac.uk/eprints/24/} }

@article{
  orchid15,
  month = {December},
  title = {Budget-Balanced and Nearly Efficient Randomized Mechanisms: Public Goods and Beyond},
  author = {M Guo and Victor Naroditskiy and V Conitzer and Amy Greenwald and Nicholas R Jennings},
  year = {2011},
  journal = {Proc. of the 7th Workshop on Internet and Network Economics},
  keywords = {Mechanism Design, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/15/1/paper%5f73.pdf},
  url = {http://www.orchid.ac.uk/eprints/15/},
abstract = {Many scenarios where participants hold private information require payments to encourage truthful revelation. Some of these scenarios have no natural residual claimant who would absorb the budget surplus or cover the deficit. Faltings proposed the idea of excluding one agent uniformly at random and making him the residual claimant. Based on this idea, we propose two classes of public good mechanisms and derive optimal ones within each class: Faltings' mechanism is optimal in one of the classes. We then move on to general mechanism design settings, where we prove guarantees on the social welfare achieved by Faltings' mechanism. Finally, we analyze a modification of the mechanism where budget balance is achieved without designating any agent as the residual claimant.} }

@inproceedings{
  orchid7,
  month = {December},
  author = {Edwin Simpson and Stephen J Roberts and Arfon Smith and Chris Lintott},
  booktitle = {NIPS 2011},
  address = {Oxford},
  title = {Bayesian Combination of Multiple, Imperfect Classifiers},
  publisher = {University of Oxford},
  pages = {1--8},
  year = {2011},
  keywords = {Machine Learning, Applications, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/7/1/vbibcc%5fworkshop.pdf},
  url = {http://www.orchid.ac.uk/eprints/7/},
abstract = {Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications. In many situations, such as when human decisions need to be combined, the base decisions can vary enormously in reliability. A Bayesian approach to such uncertain combination allows us to infer the differences in performance between individuals and to incorporate any available prior knowledge about their abilities when training data is sparse. In this paper we explore Bayesian classifier combination, using the computationally efficient framework of variational Bayesian inference. We apply the approach to real data from a large citizen science project, Galaxy Zoo Supernovae, and show that our method far outperforms other established approaches to imperfect decision combination. We go on to analyse the putative community structure of the decision makers, based on their inferred decision making strategies, and show that natural groupings are formed.} }

@article{
  orchid34,
  month = {November},
  title = {Statistics of Stellar Variability from Kepler - I: Revisiting Quarter 1 with an Astrophysically Robust Systematics Correction. },
  author = {S McQuillan and S Aigrain and Stephen J Roberts},
  year = {2011},
  journal = {Astronomy and Astrophysics},
  keywords = {Machine Learning, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/34/1/1111.5580v2.pdf},
url = {http://www.orchid.ac.uk/eprints/34/} }

@article{
  orchid21,
  month = {October},
  title = {On the Existence of Pure Strategy Nash Equilibria in Integer-Splittable Weighted Congestion Games},
  author = {Long Tran-Thanh and Maria Polukarov and Archie C Chapman and Alex Rogers and Nicholas R Jennings},
  year = {2011},
  pages = {236--253},
  journal = {Proc. of 4th International Symposium on Algorithmic Game Theory (SAGT),},
  keywords = {Mechanism Design, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/21/1/SAGT2011cmrd.pdf},
  url = {http://www.orchid.ac.uk/eprints/21/},
abstract = {We study the existence of pure strategy Nash equilibria (PSNE) in integer?splittable weighted congestion games (ISWCGs), where agents can strategically assign different amounts of demand to different resources, but must distribute this demand in fixed-size parts. Such scenarios arise in a wide range of application domains, including job scheduling and network routing, where agents have to allocate multiple tasks and can assign a number of tasks to a particular selected resource. Specifically, in an ISWCG, an agent has a certain total demand (aka weight) that it needs to satisfy, and can do so by requesting one or more integer units of each resource from an element of a given collection of feasible subsets.1 Each resource is associated with a unit?cost function of its level of congestion; as such, the cost to an agent for using a particular resource is the product of the resource unit?cost and the number of units the agent requests. While general ISWCGs do not admit PSNE (Rosenthal, 1973b), the restricted subclass of these games with linear unit?cost functions has been shown to possess a potential function (Meyers, 2006), and hence, PSNE. However, the linearity of costs may not be necessary for the existence of equilibria in pure strategies. Thus, in this paper we prove that PSNE always exist for a larger class of convex and monotonically increasing unit?costs. On the other hand, our result is accompanied by a limiting asumption on the structure of agents? strategy sets: specifically, each agent is associated with its set of accessible resources, and can distribute its demand across any subset of these resources. Importantly, we show that neither monotonicity nor convexity on its own guarantees this result. Moreover, we give a counterexample with monotone and semi?convex cost functions, thus distinguishing ISWCGs from the class of infinitely?splittable congestion games for which the conditions of monotonicity and semi?convexity have been shown to be sufficient for PSNE existence (Rosen, 1965). Furthermore, we demonstrate that the finite improvement path property (FIP) does not hold for convex increasing ISWCGs. Thus, in contrast to the case with linear costs, a potential function argument cannot be used to prove our result. Instead, we provide a procedure that converges to an equilibrium from an arbitrary initial strategy profile, and in doing so show that ISWCGs with convex increasing unit?cost functions are weakly acyclic.} }

@article{
  orchid20,
  volume = {175},
  number = {14},
  month = {September},
  author = {Sebastian Stein and Enrico H Gerding and Alex Rogers and Kate Larson and Nicholas R Jennings},
  title = {Algorithms and mechanisms for procuring services with uncertain durations using redundancy },
  publisher = {Elsevier},
  year = {2011},
  journal = {Artificial Intelligence},
  pages = {2021--2060},
  keywords = {Mechanism Design, Applications, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/20/1/SteinAIJ.pdf},
  url = {http://www.orchid.ac.uk/eprints/20/},
abstract = {In emerging service-oriented systems, such as computational clouds or grids, software agents are able to automatically procure distributed services to complete computational tasks. However, service execution times are often highly uncertain and service providers may have incentives to lie strategically about this uncertainty to win more customers. In this paper, we argue that techniques from the field of artificial intelligence are instrumental to addressing these challenges. To this end, we first propose a new decision-theoretic algorithm that allows a single service consumer agent to procure services for a computational task with a strict deadline. Crucially, this algorithm uses redundancy in a principled manner to mitigate uncertain execution times and maximise the consumer[modifier letter apostrophe]s expected utility. We present both an optimal variant that uses a novel branch-and-bound formulation, and a fast heuristic that achieves near-optimal performance. Using simulations, we demonstrate that our algorithms outperform approaches that do not employ redundancy by up to 130 % in some settings. Next, as the algorithms require private information about the providers[modifier letter apostrophe] capabilities, we show how techniques from mechanism design can be used to incentivise truthfulness. As no existing work in this area deals with uncertain execution times and redundant invocations, we extend the state of the art by proposing a number of payment schemes for these settings. In a detailed analysis, we prove that our mechanisms fulfil a range of desirable economic properties, including incentive compatibility, and we discuss suboptimal variants that scale to realistic settings with hundreds of providers. We show experimentally that our mechanisms extract a high surplus and that even our suboptimal variants typically achieve a high efficiency (95 % or more in a wide range of settings).} }

@inproceedings{
  orchid10,
  booktitle = {The 3rd Human Computation Workshop},
  month = {August},
  title = {CollabMap: Augmenting Maps using the Wisdom of Crowds },
  author = {Ruben Stranders and Sarvapali D Ramchurn and Bing Shi and Nicholas R Jennings},
  year = {2011},
  keywords = {Crowdsourcing, Accountable Information Architecture, Applications, Flexible Autonomy},
  howpublished = {http://www.orchid.ac.uk/eprints/10/1/collabmap.pdf},
url = {http://www.orchid.ac.uk/eprints/10/} }

@article{
  orchid18,
  month = {May},
  title = {Resource-Aware Junction Trees for Efficient Multi-Agent Coordination },
  author = {Nicolas Stefanovich and Alessandro Farinelli and Alex Rogers and Nicholas R Jennings},
  year = {2011},
  pages = {363--370},
  journal = {The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011)},
  keywords = {Agent-Based Computing, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/18/1/main%5fa11.pdf},
  url = {http://www.orchid.ac.uk/eprints/18/},
abstract = {In this paper we address efficient decentralised coordination of cooperative multi-agent systems by taking into account the actual computation and communication capabilities of the agents. We consider coordination problems that can be framed as Distributed Constraint Optimisation Problems, and as such, are suitable to be deployed on large scale multi-agent systems such as sensor networks or multiple unmanned aerial vehicles. Specifically, we focus on techniques that exploit structural independence among agents? actions to provide optimal solutions to the coordination problem, and, in particular, we use the Generalized Distributive Law (GDL) algorithm. In this settings, we propose a novel resource aware heuristic to build junction trees and to schedule GDL computations across the agents. Our goal is to minimise the total running time of the coordination process, rather than the theoretical complexity of the computation, by explicitly considering the computation and communication capabilities of agents. We evaluate our proposed approach against DPOP, RDPI and a centralized solver on a number of benchmark coordination problems, and show that our approach is able to provide optimal solutions for Decentralised ControlOPs faster than previous approaches. Specifically, in the settings considered, when resources are scarce our approach is up to three times faster than DPOP (which proved to be the best among the competitors in our settings).} }

@article{
  orchid17,
  volume = {175},
  number = {2},
  month = {February},
  author = {Alex Rogers and Alessandro Farinelli and Ruben Stranders and Nicholas R Jennings},
  title = {Bounded Approximate Decentralised Coordination via the Max-Sum Algorithm },
  publisher = {Elsevier},
  year = {2011},
  journal = {Artificial Intelligence},
  pages = {730--759},
  keywords = {Mechanism Design, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/17/1/4136.pdf},
  url = {http://www.orchid.ac.uk/eprints/17/},
abstract = {In this paper we propose a novel approach to decentralised coordination, that is able to efficiently compute solutions with a guaranteed approximation ratio. Our approach is based on a factor graph representation of the constraint network. It builds a tree structure by eliminating dependencies between the functions and variables within the factor graph that have the least impact on solution quality. It then uses the max-sum algorithm to optimally solve the resulting tree structured constraint network, and provides a bounded approximation specific to the particular problem instance. In addition, we present two generic pruning techniques to reduce the amount of computation that agents must perform when using the max-sum algorithm. When this is combined with the above mentioned approximation algorithm, the agents are able to solve decentralised coordination problems that have very large action spaces with a low computation and communication overhead. We empirically evaluate our approach in a mobile sensor domain, where mobile agents are used to monitor and predict the state of spatial phenomena (e.g., temperature or gas concentration). Such sensors need to coordinate their movements with their direct neighbours to maximise the collective information gain, while predicting measurements at unobserved locations. When applied in this domain, our approach is able to provide solutions which are guaranteed to be within 2 % of the optimal solution. Moreover, the two pruning techniques are extremely effective in decreasing the computational effort of each agent by reducing the size of the search space by up to 92 %.} }

@article{
  orchid33,
  title = {A GPU-based survey for millisecond radio transients using ARTEMIS.},
  author = {W Armour and A Karastergiou and M Giles and C Williams and K Magro and S Zagkouris and Stephen J Roberts and S Salvini and F Dulwich and B Mort},
  publisher = {ADASS},
  year = {2011},
  journal = {Proc. of ADASS XXI},
  keywords = {Machine Learning, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/33/1/1111.6399v1.pdf},
url = {http://www.orchid.ac.uk/eprints/33/} }

@techreport{
  orchid36,
  number = {PARG-11-01},
  author = {Jan-P Calliess and D Lyons and U.D. Hanebeck},
  address = {Oxford},
  title = {Lazy auctions for multi-robot collision avoidance and motion control under uncertainty.},
  type = {Technical Report},
  publisher = {University of Oxford},
  year = {2011},
  institution = {University of Oxford},
  journal = {ACM Transactions of Sensor Networks},
  keywords = {Machine Learning, Agile Teaming, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/36/1/PARG%5f01%5f11.pdf},
url = {http://www.orchid.ac.uk/eprints/36/} }

@article{
  orchid31,
  title = {Cooperatives of Distributed Energy Resources for Efficient Virtual Power Plants},
  author = {G Chalkiadakis and Valentin Robu and R Kota and Alex Rogers and Nicholas R Jennings},
  publisher = {AAMAS},
  year = {2011},
  pages = {787--794},
  journal = {Proc. Tenth Int. Conference on Autonomous Agents and Multiagent Systems},
  keywords = {Decentralised Control, Applications, Agile Teaming, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/31/1/ccrjVPPcoop.pdf},
url = {http://www.orchid.ac.uk/eprints/31/} }

@article{
  orchid13,
  title = {A unifying framework for iterative approximate best? response algorithms for distributed constraint optimisation problems},
  author = {Archie C Chapman and Alex Rogers and David Leslie and Nicholas R Jennings},
  year = {2011},
  journal = {The Knowledge Engineering Review},
  keywords = {Agent-Based Computing, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/13/1/ChapmanEtalKER09%5fpreprint.pdf},
  url = {http://www.orchid.ac.uk/eprints/13/},
abstract = {Distributed constraint optimisation problems (Decentralised ControlOPs) are important in many areas of computer science and optimisation. In a Decentralised ControlOP, each variable is controlled by one of many autonomous agents, who together have the joint goal of maximising a global objective function. A wide variety of techniques have been explored to solve such problems, and here we focus on one of the main families, namely iterative approximate best?response algorithms used as local search algorithms for Decentralised ControlOPs. We define these algorithms as those in which, at each iteration, agents communicate only the states of the variables under their control to their neighbours on the constraint graph, and that reason about their next state based on the messages received from their neighbours. These algorithms include the distributed stochastic algorithm and stochastic coordination algorithms, the maximum?gain messaging algorithms, the families of fictitious play and adaptive play algorithms, and algorithms that use regret?based heuristics. This family of algorithms is commonly employed in real world systems, as they can be used in domains where communication is difficult or costly, where it is appropriate to trade timeliness off against optimality, or where hardware limitations render complete or more computationally intensive algorithms unusable. However, until now, no overarching framework has existed for analysing this broad family of algorithms, resulting in similar and overlapping work being published independently in several different literatures. The main contribution of this paper, then, is the development of a unified analytical framework for studying such algorithms. This framework is built on our insight that when formulated as noncooperative games, Decentralised ControlOPs form a subset of the class of potential games. This result allows us to prove convergence properties of iterative approximate best?response algorithms developed in the computer science literature using game theoretic methods (which also shows that such algorithms can also be applied to the more general problem of finding Nash equilibria in potential games), and, conversely, also allows us to show that many game?theoretic algorithms can be used to solve Decentralised ControlOPs. By so doing, our framework can assist system designers by making the pros and cons of, and the synergies between, the various iterative approximate best?response Decentralised ControlOP algorithm components clear.} }

@inproceedings{
  orchid2,
  booktitle = {Proc 10th Int Conf on Autonomous Agents and Multi-Agent Systems},
  title = {Bounded Decentralised Coordination over Multiple Objectives},
  author = {Francesco Delle Fave and Ruben Stranders and Alex Rogers and Nicholas R Jennings},
  year = {2011},
  pages = {371--378},
  keywords = {Agent-Based Computing, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/2/1/bounded%5fdecentralised%5fcoordination.pdf},
  url = {http://www.orchid.ac.uk/eprints/2/},
abstract = {We propose the bounded multi-objective max-sum algorithm (B-MOMS), the first decentralised coordination algorithm for multi-objective optimisation problems. B-MOMS extends the max-sum message-passing algorithm for decentralised coordination to compute bounded approximate solutions to multi-objective decentralised constraint optimisation problems (MO-Decentralised ControlOPs). Specifically, we prove the optimality of B-MOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Furthermore, we empirically evaluate its performance on a multi-objective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds 2, and is typically less than 1:5 for less-constrained graphs. Moreover, the runtime required by B-MOMS on the problem instances we considered never exceeds 30 minutes, even for maximally constrained graphs with 100 agents. Thus, B-MOMS brings the problem of multi-objective optimisation well within the boundaries of the limited capabilities of embedded agents.} }

@article{
  orchid40,
  title = {A tutorial on variational Bayesian inference},
  author = {Charles W. Fox and Stephen J. Roberts},
  year = {2011},
  journal = {Artificial Intelligence Review},
  keywords = {Machine Learning, Incentive Engineering},
url = {http://www.orchid.ac.uk/eprints/40/} }

@article{
  orchid14,
  title = {Online Mechanism Design for Electric Vehicle Charging},
  author = {Enrico H Gerding and Valentin Robu and Sebastian Stein and David Parkes and Alex Rogers and Nicholas R Jennings},
  year = {2011},
  journal = {Proc. of The Tenth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2011)},
  keywords = {Mechanism Design, Applications, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/14/1/AAMAS440%5fcameraready.pdf},
url = {http://www.orchid.ac.uk/eprints/14/} }

@article{
  orchid35,
  title = {A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy. },
  author = {N.P. Gibson and S Aigrain and Stephen J Roberts and T.M. Evans and Michael A Osborne and F Pont},
  year = {2011},
  journal = {Monthly Notices of the Royal Astronomical Society},
  keywords = {Machine Learning, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/35/1/1109.3251v2.pdf},
  url = {http://www.orchid.ac.uk/eprints/35/},
abstract = {Transmission spectroscopy, which consists of measuring the wavelength-dependent absorption of starlight by a planet's atmosphere during a transit, is a powerful probe of atmospheric composition. However, the expected signal is typically orders of magnitude smaller than instrumental systematics, and the results are crucially dependent on the treatment of the latter. In this paper, we propose a new method to infer transit parameters in the presence of systematic noise using Gaussian processes, a technique widely used in the machine learning community for Bayesian regression and classification problems. Our method makes use of auxiliary information about the state of the instrument, but does so in a non-parametric manner, without imposing a specific dependence of the systematics on the instrumental parameters, and naturally allows for the correlated nature of the noise. We give an example application of the method to archival NICMOS transmission spectroscopy of the hot Jupiter HD 189733, which goes some way towards reconciling the controversy surrounding this dataset in the literature. Finally, we provide an appendix giving a general introduction to Gaussian processes for regression, in order to encourage their application to a wider range of problems. } }

@article{
  orchid41,
  volume = {415},
  number = {1},
  title = {A transient component in the pulse profile of PSR J0738?4042},
  author = {A. Karastergiou and S. J. Roberts and S. Johnston and H. Lee and P. Weltevrede and M. Kramer},
  year = {2011},
  pages = {251--256},
  journal = {Monthly Notices of the Royal Astronomical Society},
  keywords = {Machine Learning, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/41/1/1103.2247v1.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/41/} }

@article{
  orchid37,
  title = {Chance-constrained Model Predictive Control for Multi-Agent Systems.},
  author = {D Lyons and Jan-P Calliess and U.D. Hanebeck},
  publisher = {arXiv},
  year = {2011},
  journal = {arXiv},
  keywords = {Agent-Based Computing, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/37/1/1104.5384v3.pdf},
  howpublished = {http://www.orchid.ac.uk/eprints/37/},
abstract = {We consider stochastic model predictive control of a multi-agent systems with constraints on the probabilities of inter-agent collisions. We first study a sample-based approximation of the collision probabilities and use this approximation to formulate constraints for the stochastic control problem. This approximation will converge as the number of samples goes to infinity, however, the complexity of the resulting control problem is so high that this approach proves unsuitable for control under real-time requirements. To alleviate the computational burden we propose a second approach that uses probabilistic bounds to determine regions with increased probability of presence for each agent and formulate constraints for the control problem that guarantee that these regions will not overlap. We prove that the resulting problem is conservative for the original problem with probabilistic constraints, ie. every control strategy that is feasible under our new constraints will automatically be feasible for the original problem. Furthermore we show in simulations in a UAV path planning scenario that our proposed approach grants significantly better run-time performance compared to a controller with the sample-based approximation with only a small degree of sub-optimality resulting from the conservativeness of our new approach. } }

@inproceedings{
  orchid3,
  booktitle = {Twenty-Fifth Conference on Artificial Intelligence},
  title = {A Distributed Anytime Algorithm for Dynamic Task Allocation in Multi-Agent Systems},
  author = {Kathryn S Macarthur and Ruben Stranders and Sarvapali D Ramchurn and Nicholas R Jennings},
  year = {2011},
  keywords = {Agent-Based Computing, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/3/1/A%5fDistributed%5fAnytime%5fAlgorithm%5ffor%5fDynamic%5fTask%5fAllocation.pdf},
  howpublished = {http://www.orchid.ac.uk/eprints/3/},
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 capabilities: 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.} }

@article{
  orchid16,
  volume = {175},
  number = {2},
  title = {Mechanism Design for the Truthful Elicitation of Costly Probabilistic Estimates in Distributed Information Systems},
  author = {Athanasios Papakonstantinou and Alex Rogers and Enrico H Gerding and Nicholas R Jennings},
  year = {2011},
  pages = {648--672},
  journal = {Artificial Intelligence},
  keywords = {Mechanism Design, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/16/1/4120.pdf},
  howpublished = {http://www.orchid.ac.uk/eprints/16/},
  abstract = {This paper reports on the design of a novel two-stage mechanism, based on {$ backslash$}textit {strictly proper scoring rules },
that allows a centre to acquire a costly forecast of a future event (such as a meteorological phenomenon or a probabilistic estimate of a specific parameter such as the quality of an expected service), with a specified minimum precision, from one or more agents. In particular, this is the first mechanism that can be applied in a setting where the centre has no knowledge about the actual costs involved in the generation of the agents' estimates and {$ backslash$}textit {also } has no means of evaluating the quality and accuracy of the estimates it receives. En route to this mechanism, we first consider a setting in which any single agent can provide an estimate of the required precision, and the centre can evaluate this estimate by comparing it with the outcome which is observed at a later stage. This mechanism is then extended, so that it can be applied in a setting where the agents' different capabilities are reflected in the maximum precision of the estimates that they can provide, and hence the centre may need to select multiple agents and combine their individual results in order to obtain an estimate of the required precision. For all three mechanisms, we prove their economic properties (i.e. incentive compatibility and individual rationality) and then present specific empirical results. For the single agent mechanism we compare the quadratic, spherical and logarithmic scoring rules with a parametric family of scoring rules. We show that although the logarithmic scoring rule minimises both the mean and variance of the centre's total payments, using this rule means that an agent may face an unbounded penalty if it provides an estimate of extremely poor quality. We show that this is not the case for the parametric family, and thus, we suggest that the parametric scoring rule is the best candidate in our setting. Furthermore, we show that the `multiple agent' extension describes a family of possible approaches to select agents in the first stage of our mechanism, and we show empirically and prove analytically that there is one approach that dominates all others. Finally, we compare our novel contribution and with the peer prediction mechanism introduced by {$ backslash$}cite {trustsr1 } and show that the centre's total expected payment is the same in both mechanisms (and is equal to total expected payment in the case that the estimates can be compared to the actual outcome), while the variance in these payments is significantly reduced within our mechanism.} }

@article{
  orchid43,
  title = {Using hidden Markov models for iterative non-intrusive appliance monitoring.},
  author = {Oliver Parson and Siddharta Ghosh and M Weal and Alex Rogers},
  publisher = {NIPS},
  year = {2011},
  journal = {Neural Information Processing Systems workshop on Machine Learning for Sustainability},
  keywords = {Decentralised Control, Applications, Flexible Autonomy},
  howpublished = {http://www.orchid.ac.uk/eprints/43/1/Machine LearningSUST2011.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/43/} }

@inproceedings{
  orchid4,
  booktitle = {Workshop on Link Analysis in Heterogeneous Information Networks (IJCAI-11)},
  title = {Efficient Sharing of Conflicting Opinions with Minimal Communication in Large Decentralised Teams},
  author = {Oleksandr Pryymak and Alex Rogers and Nicholas R Jennings},
  year = {2011},
  keywords = {Agent-Based Computing, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/4/1/Efficient%5fSharing%5fof%5fConflicting%5fOpinions.pdf},
  howpublished = {http://www.orchid.ac.uk/eprints/4/},
abstract = {In large decentralised teams agents often share uncertain and conflicting information across the network, and it is a major challenge for team members to reach accurate conclusions individually. Previously, this problem was approached by introducing a communication overhead in order to reason about the accuracy of information or to reach agreements interactively. We address the more challenging problem of improving the accuracy in settings where communication is strictly limited to sharing opinions about the real state of the common subject of interest. We do so by presenting a novel decentralised algorithm, AAT, which reaches the settings of emergent behaviour in a team where agents? opinions becomes dramatically more accurate. We show that our solution significantly outperforms the existing algorithm, DACOR, and delivers an accuracy of opinions close to a team pretuned for the highest performance by empirical exploration of its parameters. Moreover, in contrast to the message-passing DACOR, our algorithm has a minimal communication requirement, where only opinions are shared, as well as significantly lower computational expenses. Finally, AAT delivers a high accuracy of opinions in settings where up to half of the team does not participate in optimising sharing parameters.} }

@article{
  orchid38,
  volume = {83},
  number = {6},
  title = {Overlapping Community Detection using Bayesian Nonnegative Matrix Factorization.},
  author = {Ioannis Psorakis and Stephen J Roberts and Mark Ebden and Ben Sheldon},
  year = {2011},
  journal = {Physical Review E},
  keywords = {Machine Learning, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/38/},
abstract = {Identifying overlapping communities in networks is a challenging task. In this work we present a probabilistic approach to community detection that utilizes a Bayesian non-negative matrix factorization model to extract overlapping modules from a network. The scheme has the advantage of soft-partitioning solutions, assignment of node participation scores to modules, and an intuitive foundation. We present the performance of the method against a variety of benchmark problems and compare and contrast it to several other algorithms for community detection.} }

@inproceedings{
  orchid6,
  booktitle = {Proc. 25th Conference on AI (AAAI)},
  title = {Constrained Coalition Formation},
  author = {Talal Rahwan and Tomasz Michalak and Edith Elkind and Piotr Faliszewski and Jacek Sroka and Michael Wooldridge and Nicholas R Jennings},
  year = {2011},
  pages = {719--725},
  keywords = {Agent-Based Computing, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/6/1/Constrained%5fCoalition%5fFormation.pdf},
  howpublished = {http://www.orchid.ac.uk/eprints/6/},
abstract = {The conventional model of coalition formation considers every possible subset of agents as a potential coalition. However, in many real-world applications, there are inherent constraints on feasible coalitions: for instance, certain agents may be prohibited from being in the same coalition, or the coalition structure may be required to consist of coalitions of the same size. In this paper, we present the first systematic study of constrained coalition formation (CCF). We propose a general framework for this problem, and identify an important class of CCF settings, where the constraints specify which groups of agents should/should not work together. We describe a procedure that transforms such constraints into a structured input that allows coalition formation algorithms to identify, without any redundant computations, all the feasible coalitions. We then use this procedure to develop an algorithm for generating an optimal (welfare-maximizing) constrained coalition structure, and show that it outperforms existing state-of-the-art approaches by several orders of magnitude.} }

@inproceedings{
  orchid5,
  booktitle = {IJCAI 11},
  title = {Minimum Search To EstablishWorst-Case Guarantees in Coalition Structure Generation},
  author = {Talal Rahwan and Tomasz Michalak and Nicholas R Jennings},
  year = {2011},
  pages = {338--343},
  keywords = {Agent-Based Computing, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/5/1/Rahwan%5fMichalak%5fJennings%5f1338%5fcamera%5fready.pdf},
  howpublished = {http://www.orchid.ac.uk/eprints/5/},
abstract = {Coalition formation is a fundamental research topic in multi-agent systems. In this context, while it is desirable to generate a coalition structure that maximizes the sum of the values of the coalitions, the space of possible solutions is often too large to allow exhaustive search. Thus, a fundamental open question in this area is the following: Can we search through only a subset of coalition structures, and be guaranteed to find a solution that is within a desirable bound ? from optimum? If so, what is the minimum such subset? To date, the above question has only been partially answered by Sandholm et al. in their seminal work on anytime coalition structure generation [Sandholm et al., 1999]. More specifically, they identified minimum subsets to be searched for two particular bounds: ? = n and ? = dn=2e. Nevertheless, the question remained open for other values of ?. In this paper} }

@article{
  orchid39,
  title = {Determining intent using hard/soft data and Gaussian process classifiers.},
  author = {Steven Reece and Stephen J Roberts and David Nicholson and C Lloyd},
  year = {2011},
  journal = {Proc. of the 14th International Conference on Information Fusion 2011},
  keywords = {Machine Learning, Incentive Engineering},
howpublished = {http://www.orchid.ac.uk/eprints/39/} }

@article{
  orchid19,
  volume = {4},
  number = {2},
  title = {Robust Execution of Service Workflows using Redundancy and Advance Reservations },
  author = {Sebastian Stein and Terry Payne and Nicholas R Jennings},
  year = {2011},
  pages = {125--139},
  journal = {IEEE Transactions on Services Computing,},
  keywords = {Mechanism Design, Applications},
  howpublished = {http://www.orchid.ac.uk/eprints/19/1/tsc2011020125.pdf},
  howpublished = {http://www.orchid.ac.uk/eprints/19/},
abstract = {In this paper, we develop a novel algorithm that allows service consumers to execute business processes (or workflows) of interdependent services in a dependable manner within tight time-constraints. In particular, we consider large inter-organisational service-oriented systems, where services are offered by external organisations that demand financial remuneration and where their use has to be negotiated in advance using explicit service-level agreements (as is common in Grids and cloud computing). Here, different providers often offer the same type of service at varying levels of quality and price. Furthermore, some providers may be less trustworthy than others, possibly failing to meet their agreements. To control this unreliability and ensure end-to-end dependability while maximising the profit obtained from completing a business process, our algorithm automatically selects the most suitable providers. Moreover, unlike existing work, it reasons about the dependability properties of a workflow, and it controls these by using service redundancy for critical tasks and by planning for contingencies. Finally, our algorithm reserves services for only parts of its workflow at any time, in order to retain flexibility when failures occur. We show empirically that our algorithm consistently outperforms existing approaches, achieving up to a 35-fold increase in profit and successfully completing most workflows, even when the majority of providers fail.} }

@article{
  orchid47,
  title = {Optimal payments in dominant-strategy mechanisms for single-parameter domains.},
  author = {Victor Naroditskiy and Maria Polukarov and Nicholas R Jennings},
  year = {2012},
  journal = {ACM Transactions on Economics and Computation},
  keywords = {Mechanism Design, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/47/1/optimal%20payments.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/47/} }

@inproceedings{
  orchid48,
  booktitle = {IAAI 2012: The Twenty-Fourth Innovative Applications of Artificial Intelligence Conference},
  title = {A methodology for deploying the max-sum algorithm and a case study on unmanned aerial vehicles.},
  author = {Francesco Delle Fave and Alessandro Farinelli and Alex Rogers and Nicholas R Jennings},
  year = {2012},
  journal = {IAAI 2012: The Twenty-Fourth Innovative Applications of Artificial Intelligence Conference},
  keywords = {Disaster Recovery, Applications, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/48/1/delle%5ffave%5fiaai%5f2012.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/48/} }

@inproceedings{
  orchid49,
  booktitle = {Twenty-Sixth Conference on Artificial Intelligence (AAAI-12)},
  title = {Non-intrusive load monitoring using prior models of general appliance types},
  author = {Oliver Parson and Siddharta Ghosh and Mark Weal and Alex Rogers},
  year = {2012},
  keywords = {Energy Management, Applications, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/49/1/nialm.pdf},
  howpublished = {http://www.orchid.ac.uk/eprints/49/},
abstract = {Non-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance's load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. We evaluate the accuracy of our approach using the REDD data set, and show the disaggregation performance when using our training approach is comparable to when sub-metered training data is used. We also present a deployment of our system as a live application and demonstrate the potential for personalised energy saving feedback.} }

@inproceedings{
  orchid50,
  booktitle = {, Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
  title = {Knapsack based optimal policies for budget-limited multi-armed bandits.},
  author = {Long Tran-Thanh and Archie C Chapman and Alex Rogers and Nicholas R Jennings},
  year = {2012},
  keywords = {Mechanism Design, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/50/1/LTT%5fAAAI2012%5fBandit%5ffinalversion.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/50/} }

@article{
  orchid52,
  volume = {186},
  title = {Anytime coalition structure generation in multi-agent systems with positive or negative externalities},
  author = {Talal Rahwan and Tomasz Michalak and Michael Wooldridge and Nicholas R. Jennings},
  year = {2012},
  pages = {95--122},
  journal = {Artificial Intelligence},
  keywords = {Mechanism Design, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/52/1/Published%5fversion.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/52/} }

@inproceedings{
  orchid53,
  booktitle = {Twenty-Sixth Conference on Artificial Intelligence (AAAI-12)},
  title = {Optimizing payments in dominant-strategy mechanisms for multi-parameter domains.},
  author = {Lachlan Dufton and Victor Naroditskiy and Maria Polukarov and Nicholas R Jennings},
  year = {2012},
  keywords = {Mechanism Design, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/53/1/mainpreprint.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/53/} }

@article{
  orchid54,
  title = {Optimal payments in dominant-strategy mechanisms for single-parameter domains.},
  author = {Victor Naroditskiy and Maria Polukarov and Nicholas R Jennings},
  year = {2012},
  journal = {ACM Transactions on Economics and Computation},
  keywords = {Mechanism Design, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/54/1/main.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/54/} }

@inproceedings{
  orchid55,
  booktitle = {26th Conference on Artificial Intelligence (AAAI-12)},
  title = {A hybrid algorithm for coalition structure generation.},
  author = {Talal Rahwan and Tomasz Michalak and Nicholas R Jennings},
  year = {2012},
  keywords = {Mechanism Design, Agile Teaming},
howpublished = {http://www.orchid.ac.uk/eprints/55/} }

@inproceedings{
  orchid56,
  booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
  title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
  author = {Alex Rogers and Sarvapali D Ramchurn and Nicholas R Jennings},
  year = {2012},
  keywords = {Agent-Based Computing, Agile Teaming},
  howpublished = {http://www.orchid.ac.uk/eprints/56/1/smart%5fgrid.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/56/} }

@inproceedings{
  orchid57,
  booktitle = {14th ACM Int. Conf. on Ubiquitous Computing},
  title = {Understanding domestic energy consumption through interactive visualisation: a field study},
  author = {Enrico Costanza and Sarvapali D Ramchurn and Nicholas R Jennings},
  year = {2012},
  keywords = {Human Computer Interaction, Applications, Agile Teaming},
howpublished = {http://www.orchid.ac.uk/eprints/57/} }

@inproceedings{
  orchid58,
  booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
  title = {) Prediction and fault detection of environmental signals with uncharacterised faults.},
  author = {Michael A Osborne and R Garnett and K Swersky and N de Freitas },
  year = {2012},
  keywords = {Machine Learning, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/58/1/Osborne%5fGarnett%5fSwersky%5fde%5fFreitas%5ffault%5fbucket%5faaai%5f2012.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/58/} }

@article{
  orchid59,
  title = {Inferring social network structure in ecological systems from spatio-temporal data streams },
  author = {Ioannis Psorakis and Stephen J Roberts and I Rezek and Ben Sheldon},
  year = {2012},
  journal = {Journal of the Royal Society, Interface},
  keywords = {Mechanism Design, Machine Learning, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/59/1/psorakis%5frsi.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/59/} }

@article{
  orchid60,
  title = {Bayesian quadrature for ratios: now with even more Bayesian quadrature},
  author = {Michael A Osborne and R Garnett and Stephen J Roberts and C Hart and S Aigrain and N.P. Gibson},
  year = {2012},
  journal = {Proceedings of AISTATS 2012},
  keywords = {Machine Learning, Incentive Engineering},
  howpublished = {http://www.orchid.ac.uk/eprints/60/1/BQ%5faistats%5fappendix.pdf},
howpublished = {http://www.orchid.ac.uk/eprints/60/} }