We try to keep an up to date list of all our publications. If you are interested in a PDF that we have not uploaded yet, feel free to send us an email to get a copy. You can view all publications of the current members of the Artificial Intelligence and Sustainability group. For other listings, please see:
The area under the ROC curve (AUC) has been advocated as an evaluation criterion for the bipartite ranking problem. We study large deviation properties of the AUC; in particular, we derive a distribution-free large deviation bound for the AUC which serves to bound the expected accuracy of a ranking function in terms of its empirical AUC on an independent test sequence. A comparison of our result with a corresponding large deviation result for the classification error rate suggests that the test sample size required to obtain an epsilon-accurate estimate of the expected accuracy of a ranking function with delta-confidence is larger than that required to obtain an epsilon-accurate estimate of the expected error rate of a classification function with the same confidence. A simple application of the union bound allows the large deviation bound to be extended to learned ranking functions chosen from finite function classes.
We apply reinforcement learning to the problem of finding good policies for a fighting agent in a commercial computer game. The learning agent is trained using the SARSA algorithm for on-policy learning of an action-value function represented by linear and neural network function approximators. We discuss the selection and construction of features, actions, and rewards as well as other design choices necessary to integrate the learning process into the game. The learning agent is trained against the built-in AI of the game with different rewards encouraging aggressive or defensive behaviour. We show that the learning agent finds interesting (and partly near optimal) policies in accordance with the reward functions provided. We also discuss the particular challenges arising in the application of reinforcement learning to the domain of computer games.
Poisson-Networks : A Model for Structured Poisson Processes. Rajaram, Shyamsundar; Graepel, Thore; Herbrich, Ralf (2004). 277–284.
Modelling structured multivariate point process data has wide ranging applications like understanding neural activity, developing faster file access systems and learning dependencies among servers in large networks. In this paper, we develop the Poisson network model for representing multivariate structured Poisson processes. In our model each node of the network represents a Poisson process. The novelty of our work is that waiting times of a process are modelled by an exponential distribution with a piecewise constant rate function that depends on the event counts of its parents in the network in a generalised linear way. Our choice of model allows to perform exact sampling from arbitrary structures. We adopt a Bayesian approach for learning the network structure. Further, we discuss fixed point and sampling based approximations for performing inference of rate functions in Poisson networks.
Artificial Intelligence and Sustainability
Our research group investigates both the use of energy in developing artificial intelligence (AI) as well as the use of AI in generating, storing and managing energy. This includes research into energy-efficient algorithms for solving basic AI tasks such as classification, ranking or planning & search, as well as the development and application of AI methods to refined modeling of batteries in order to extend their working lifetime, and the control of domestic energy consumption.