In August 2010 Facebook launched Places, a location-based service that allows users to check into points of interest and share their physical whereabouts with friends. The friends who see these events in their News Feed can then respond to these check-ins by liking or commenting on them. These data consisting of the places people go and how their friends react to them are a rich, novel dataset. In this paper we first analyze this dataset to understand the factors that influence where users check in, including previous check-ins, similarity to other places, where their friends check in, time of day, and demographics. We show how these factors can be used to build a predictive model of where users will check in next. Then we analyze how users respond to their friends’ check-ins and which factors contribute to users liking or commenting on them. We show how this can be used to improve the ranking of check-in stories, ensuring that users see only the most relevant updates from their friends and ensuring that businesses derive maximum value from check-ins at their establishments. Finally, we construct a model to predict friendship based on check-in count and show that cocheck-ins has a statistically significant effect on friendship.

Twitter has become exceedingly popular, with hundreds of millions of tweets being posted every day on a wide variety of topics. This has helped make real-time search applications possible with leading search engines routinely displaying relevant tweets in response to user queries. Recent research has shown that a considerable fraction of these tweets are about "events," and the detection of novel events in the tweet-stream has attracted a lot of research interest. However, very little research has focused on properly displaying this real-time information about events. For instance, the leading search engines simply display all tweets matching the queries in reverse chronological order. In this paper we argue that for some highly structured and recurring events, such as sports, it is better to use more sophisticated techniques to summarize the relevant tweets. We formalize the problem of summarizing event-tweets and give a solution based on learning the underlying hidden state representation of the event via Hidden Markov Models. In addition, through extensive experiments on real-world data we show that our model significantly outperforms some intuitive and competitive baselines.

Gold farming and real money trade refer to a set of illicit practices in massively multiplayer online games (MMOGs) whereby players accumulate virtual resources to sell for “real world” money. Prior work has examined trade relationships formed by gold farmers but not the trust relationships which exist between members of these organizations. We adopt a hypergraph approach to model the multi-modal relationships of gold farmers granting other players permission to use and modify objects they own. We argue these permissions reflect underlying trust relationships which can be analyzed using network analysis methods. We compare farmers’ trust networks to the trust networks of both unidentified farmers and typical players. Our results demonstrate that gold farmers’ networks are different from trust networks of normal players whereby farmers trust highly-central non-farmer players but not each other. These findings have implications for augmenting detection methods and re-evaluating theories of clandestine behavior.

We describe a novel binary classification technique called Banded SVM (B-SVM). In the standard C-SVM formulation of Cortes et al. (1995), the decision rule is encouraged to lie in the interval [1, \infty]. The new B-SVM objective function contains a penalty term that encourages the decision rule to lie in a user specified range [\rho_1, \rho_2]. In addition to the standard set of support vectors (SVs) near the class boundaries, B-SVM results in a second set of SVs in the interior of each class.

IT Service Management deals with managing a broad range of items related to complex system environments. As there is both, a close connection to business interests and IT infrastructure, the application of semantic expressions which are seamlessly integrated within applications for managing ITSM environments, can help to improve transparency and profitability. This paper focuses on the challenges regarding the integration of semantics and ontologies within ITSM environments. It will describe the paradigm of relationships and inheritance within complex service trees and will present an approach of ontologically expressing them. Furthermore, the application of SBVR-based rules as executable SQL triggers will be discussed. Finally, the broad range of topics for further research, derived from the findings, will be presented.

We present a description of the PhD thesis which aims to propose a rule-based query answering method for relational data. In this approach we use an additional knowledge which is represented as a set of rules and describes the source data at concept (ontological) level. Queries are posed in the terms of abstract level. We present two methods. The first one uses hybrid reasoning and the second one exploits only forward chaining. These two methods are demonstrated by the prototypical implementation of the system coupled with the Jess engine. Tests are performed on the knowledge base of the selected economic crimes: fraudulent disbursement and money laundering.

A common problem for companies with strong business growth is that it is hard to find enough experienced staff to support expansion needs. This article is a case study of how one of the largest travel agencies in Hong Kong alleviated this problem by using AI to support decision-making and problem-solving so that their planners and controllers can work more effectively and efficiently to sustain business growth while maintaining consistent quality of service. AI is used in a mission critical fleet management system (FMS) that supports the scheduling and management of a fleet of luxury limousines for business travelers. The AI problem was modeled as a constraint satisfaction problem (CSP).

From an AI perspective, finding effective treatments for cancer is a high-dimensional search problem characterized by many molecularly distinct cancer subtypes, many potential targets and drug combinations, and a dearth of high quality data to connect molecular subtypes and treatments to responses. The broadening availability of molecular diagnostics and electronic medical records, presents both opportunities and challenges to apply AI techniques to personalize and improve cancer treatment. We discuss these in the context of Cancer Commons, a "rapid learning" community where patients, physicians, and researchers collect and analyze the molecular and clinical data from every cancer patient, and use these results to individualize therapies. Research opportunities include: adaptively-planning and executing individual treatment experiments across the whole patient population, inferring the causal mechanisms of tumors, predicting drug response in individuals, and generalizing these findings to new cases.

TLP and the field as a whole made great strides in each of these dimensions. Indeed, the program has helped TL become a recognized subdiscipline of machine learning. Other articles in this special issue detail the work accomplished in TLP; this article focuses on a broad framing of the research conducted and an assessment of its progress, limitations, and challenges, from an admittedly personal but DARPAinfluenced perspective. Traditionally every DARPA program has focused its research by requiring a precise measure of progress. The DARPA TLP decided to measure transfer by comparing the learning of tasks A and B versus the learning of B alone. In figure 1 the curve labeled B represents a traditional learning curve of the performance on target task B as a function of the number of training instances.

AIIDE 2010 was held October 11-13, 2010, at Stanford University ajacent to Palo Alto, California. The conference featured 17 paper presentations, 18 posters, 5 demos, 5 invited speakers, a panel on teaching game AI in academe, and the first StarCraft AI competition. Led by the conference chair, Michael Youngblood (University of North Carolina at Charlotte), and the program chair, Vadim Bulitko (University of Alberta), the three days of AIIDE contained a dense and exciting agenda highlighting new research and revealing how AI is applied in many commercial endeavors. The first day was kicked off with an invited talk from Chris Jurney, lead developer of Double Fine Productions, who detailed his work on the nonplayer character pathfinding of Dawn of War II during his time at Relic Entertainment. The morning was completed by research presentations on behavioral techniques with notable work on producing realistic behaviors through alibi generation (Ben Sunshine-Hill and Norman Badler, University of Pennsylvania), which has been widely discussed in the community since, and Ben Weber's (University of California, Santa Cruz) work applying goal-driven autonomy to playing StarCraft (awarded AIIDE 2010 Best Student Paper).