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 prediction accuracy of any learning algorithm highly depends on the quality of the selected features; but often, the task of feature construction and selection is tedious and non-scalable. In recent years, however, there have been numerous projects with the goal of constructing general-purpose or domain-specific knowledge bases with entity-relationship-entity triples extracted from various Web sources or collected from user communities, e.g., YAGO, DBpedia, Free- base, UMLS, etc. This paper advocates the simple and yet far-reaching idea that the structured knowledge contained in such knowledge bases can be exploited to automatically extract features for general learning tasks. We introduce an expressive graph-based language for extracting features from such knowledge bases and a theoretical framework for constructing feature vectors from the extracted features. Our experimental evaluation on different learning scenarios provides evidence that the features derived through our framework can considerably improve the prediction accuracy, especially when the labeled data at hand is sparse.
Behavioral Game Theory on Online Social Networks: Colonel Blotto is on Facebook. Kohli, Pushmeet; Bachrach, Yoram; Graepel, Thore; Smyth, Gavin; Armstrong, Michael; Stillwell, David; Kearns, Michael (2011).
We show how online social networks such as Facebook can be used in Behavioral Game Theory research. We report the deployment of a Facebook application Project Waterloo that allows users to play the Colonel Blotto game against their friends and strangers. Unlike conventional studies performed in the laboratory environment, which rely on monetary incentives to attract human subjects to play games, our framework does not use money and instead relies on reputation and entertainment incentives. We describe the Facebook application we created for conducting this experiment, and perform a preliminary analysis of the data collected in the game. We conclude by discussing the advantages of our approach and list some ideas for future work.
A Penny for Your Thoughts? The Value of Information in Recommendation Systems. Passos, Alexandre; Van Gael, Juergen; Herbrich, Ralf; Paquet, Ulrich (2011). 9–14.
Most recommendation systems are trained to predict behavioral data and then used to generate more such data by recommending items and receiving feedback on the quality of these recommendations. This data in then fed back into the training process. This creates a feedback loop: as long as the low-cost way to interact with the service is through the recommender, the recommender will only ever see behavioral data on the items it chooses. This process can lead to hidden biases, as it effectively limits how much information the recommender system will ever see. On the other hand, there is a cost to making exploratory recommendations, as they should, myopically, be worse than the best bets of a recommendation system. In this paper we explore the notion that recommender systems are a special kind of active learning agents, with the peculiarity that the cost of asking for the label of an instance depends on its true label, as the cost of showing a bad recommendation when exploring is higher than the cost of showing a good recommendation.
Sociable Killers: Understanding Social Relationships in an Online First-Person Shooter Game. Xu, Yan; Cao, Xiang; Sellen, Abigail; Herbrich, Ralf; Graepel, Thore (2011). 197–206.
Online video games can be seen as medium for the formation and maintenance of social relationships. In this paper, we explore what social relationships mean under the context of online First-Person Shooter (FPS) games, how these relationships influence game experience, and how players manage them. We combine qualitative interview and quantitative game log data, and find that despite the gap between the non-persistent game world and potentially persistent social relationships, a diversity of social relationships emerge and play a central role in the enjoyment of online FPS games. We report the forms, development, and impact of such relationships, and discuss our findings in light of design implications and comparison with other game genres.
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.