As users of social networking websites expand their network of friends, they are often flooded with newsfeed posts and status updates, most of which they consider to be "unimportant" and not newsworthy. In order to better understand how people judge the importance of their newsfeed, we conducted a study in which Facebook users were asked to rate the importance of their newsfeed posts as well as their friends. We learned classifiers of newsfeed and friend importance to identify predictive sets of features related to social media properties, the message text, and shared background information. For classifying friend importance, the best performing model achieved 85% accuracy and 25% error reduction. By leveraging this model for classifying newsfeed posts, the best newsfeed classifier achieved 64% accuracy and 27% error reduction.

Demand and capacity imbalances in the US national airspace are resolved using traffic management initiatives designed, in current operations, with little collaboration with the airspace users. NASA and its partners have developed a new collaborative concept of operations that requires the users and airspace service provider to work together to choose initiatives that better satisfy the business needs of the users while also ensuring safety to the same standard as today. In this paper, we describe an approach to implementing this concept through a software negotiation framework underpinned by technology developed in the artificial intelligence community. We describe our exploration of peer-to-peer negotiation and how the number of conversation threads and the time sensitivity of offer acceptance led us to a centralized approach. The centralized approach uses hill climbing to evaluate airport slot allocations from a user perspective and a linear programming solver to seek solutions compatible across the user community. Our experiments with full sized problems identify the potential operational benefits as well as limitations, and where future research needs to be focused.

In cooperative pathfinding problems, non-interfering paths that bring each agent from its current state to its goal state must be planned for multiple agents. We present the first practical, admissible, and complete algorithm for solving problems of this kind. First, we propose a technique called operator decomposition, which can be used to reduce the branching factors of many search algorithms, including algorithms for cooperative pathfinding. We then show how a type of independence common in instances of cooperative pathfinding problems can be exploited. Next, we take the idea of exploiting independent subproblems further by adding improvements that allow the algorithm to recognize many more cases of such independence. Finally, we show empirically that these techniques drastically improve the performance of the standard admissible algorithm for the cooperative pathfinding problem, and that their combination results in a complete algorithm capable of optimally solving relatively large problems in milliseconds.

Parimutuel wagering is a significant source of revenue for many state governments. MonitorPlus is a surveillance system for parimutuel operators and regulators. Using industry expertise and best practices, MonitorPlus examines each and every wager and account transaction for evidence of fraud, crime, and money laundering. Alerts are generated in real-time. In forensic discovery mode, MonitorPlus is designed to collaborate with skilled analysts to discover more complex suspicious wagering patterns. MonitorPlus utilizes machine learning, so its risk profiles are current: its knowledge base improves with time. Each alert is accompanied by an automatically generated, rule-based explanation. This is critically important if an event rises to the level where legal action is required. Our development and deployment strategy is based on a new paradigm of a secure surveillance utility, where real-time alerts and dataintensive forensics support multiple regulatory jurisdictions. We believe this surveillance paradigm can be applied to other application domains such as lotteries, casinos, online gaming, and financial services.

Auction processes have been a well-established research Different from one-shot combinatorial auctions, the main theme in economics and recently become an emerging research issue of a dynamic auction is whether the procedure can lead topic in AI due to a set of related computational challenges to an equilibrium state (Walrasian equilibrium) at which all (Cramton et al. 2006). It is well-known that the problem the selling items are effectively allocated to the buyers (equilibrium of winner determination in a combinatorial auction is allocation) and the price of each bundle of items NPcomplete (Rothkopf et al. 1998; Sandholm 2002). However, gives the buyers their best values (equilibrium price). It most of the discussions on the computational issues has been observed that without certain assumptions on buyers' of combinatorial auctions are based on one-shot sealed-bid value functions, there is no guarantee for a dynamic mechanisms. This paper aims to make a contribution towards auction to converge toward an equilibrium (Gul and Stacchetti the discussions on dynamic procedures of combinatorial 1999). Kelso and Crawford (1982) proposed a condition, auctions.

In distributed work systems, individual users perform work for other users. A significant challenge in these systems is to provide proper incentives for users to contribute as much work as they consume, even when monitoring is not possible. We formalize the problem of designing "incentive-compatible accounting mechanisms" that measure the net contributions of users, despite relying on voluntary reports. We introduce the Drop-Edge Mechanism that removes any incentive for a user to manipulate via misreports about work contributed or consumed. We prove that Drop-Edge provides a good approximation to a user's net contribution, and is accurate in the limit as the number of users grows. We demonstrate very good welfare properties in simulation compared to an existing, manipulable mechanism. In closing, we show the power of sybil attacks in accounting mechanisms and discuss our ongoing work, including a real-world implementation and evaluation of the Drop-Edge Mechanism in a BitTorrent client.

The goal of testing is to discriminate between multiple hypotheses about a system - for example, different fault diagnoses - by applying input patterns and verifying or falsifying the hypotheses from the observed outputs. Definitely discriminating tests (DDTs) are those input patterns that are guaranteed to discriminate between different hypotheses of non-deterministic systems. Finding DDTs is important in practice, but can be very expensive. Even more challenging is the problem of finding a DDT that minimizes the cost of the testing process, i.e., an input pattern that can be most cheaply enforced and that is a DDT. This paper addresses both problems. We show how we can transform a given problem into a Boolean structure in decomposable negation normal form (DNNF), and extract from it a Boolean formula whose models correspond to DDTs. This allows us to harness recent advances in both knowledge compilation and satisfiability for efficient and scalable DDT computation in practice. Furthermore, we show how we can generate a DNNF structure compactly encoding all DDTs of the problem and use it to obtain a cost-optimal DDT in time linear in the size of the structure. Experimental results from a real-world application show that our method can compute DDTs in less than 1 second for instances that were previously intractable, and cost-optimal DDTs in less than 20 seconds where previous approaches could not even compute an arbitrary DDT.

In this paper, we review a series of agent behavior synthesis problems under full observability and nondeterminism (partial controllability), ranging from conditional planning, to recently introduced agent planning programs, and to sophisticated forms of agent behavior compositions, and show that all of them can be solved by model checking two-player game structures. These structures are akin to transition systems/Kripke structures, usually adopted in model checking, except that they distinguish (and hence allow to separately quantify) between the actions/moves of two antagonistic players. We show that using them we can implement solvers for several agent behavior synthesis problems.

Consistency properties and algorithms for achieving them are at the heart of the success of Constraint Programming. In this paper, we study the relational consistency property R ( *,m ) C, which is equivalent to m-wise consistency proposed in relational databases. We also define wR ( *,m ) C, a weaker variant of this property. We propose an algorithm for enforcing these properties on a Constraint Satisfaction Problem by tightening the existing relations and without introducing new ones. We empirically show that wR(*,m)C solves in a backtrack-free manner all the instances of some CSP benchmark classes, thus hinting at the tractability of those classes.

Online forums contain valuable human-generated information. End-users looking for information would like to find only those threads in forums where relevant information is present. Due to the distinctive characteristics of forum pages from generic web pages, special techniques are required to organize and search for information in these forums. Threads and pages in forums are different from other webpages in their hyperlinking patterns. Forum posts also have associated social and non-textual metadata. In this paper, we propose a model for online thread retrieval based on inference networks that utilizes the structural properties of forum threads. We also investigate the effects of incorporating various relevance indicators in our model. We empirically show the effectiveness of our proposed model using real-world data.