Blogosphere and sites such as for social networking, There has been a variety of work on the voter model. Dynamical knowledge-sharing and media-sharing in the World Wide properties of the basic model, including how the degree Web have enabled to form various kinds of large social distribution and the network size affect the mean time networks, through which behaviors, ideas and opinions to reach consensus, have been extensively studied (Liggett can spread. Thus, substantial attention has been directed 1999; Sood and Redner 2005) from mathematical point to investigating the spread of influence in these networks of view. Several variants of the voter model are also investigated (Leskovec, Adamic, and Huberman 2007; Crandall et al.

Temporal conceptual data models (TCMs) can be encoded Conceptual data modelling formalisms such as the Entity-in various temporal description logics (TDLs), which Relationship model (ER) and Unified Modelling Language have been designed and investigated since the seminal paper (UML) have become a de facto standard in database design (Schild 1993) with the aim of understanding the computational by providing visual means to describe application domains price of introducing a temporal dimension in DLs; in a declarative and reusable way. On the other hand, both see (Lutz, Wolter, & Zakharyaschev 2008) for a recent survey. ER and UML turned out to be closely connected with description A general conclusion one can draw from the obtained logics (DLs) developed in the area of knowledge results is that--as far as there is nontrivial interaction between representation, underpinned by formal semantics and thus the temporal and DL components--TDLs based on capable of providing services for effective reasoning over full-fledged DLs like ALC turn out to be too complex for conceptual models; see, e.g., (Berardi, Calvanese, & De Giacomo effective reasoning (see the end of this section for details).

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.

Collusion is a major unsolved security problem in online bridge: by illicitly exchanging card information over the telephone, instant messenger or the like, cheaters can gain huge advantages over honest players. It is very hard if not impossible to prevent collusion from happening. Instead, we motivate an AI-based detection approach and discuss its challenges. We challenge the AI community to create automated methods for detecting collusive traces left in game records with an accuracy that can be achieved by human masters.

Different solvers for computationally difficult problems such as satisfiability (SAT) perform best on different instances. Algorithm portfolios exploit this phenomenon by predicting solvers' performance on specific problem instances, then shifting computational resources to the solvers that appear best suited. This paper develops a new approach to the problem of making such performance predictions: natural generative models of solver behavior. Two are proposed, both following from an assumption that problem instances cluster into latent classes: a mixture of multinomial distributions, and a mixture of Dirichlet compound multinomial distributions. The latter model extends the former to capture burstiness, the tendency of solver outcomes to recur. These models are integrated into an algorithm portfolio architecture and used to run standard SAT solvers on competition benchmarks. This approach is found competitive with the most prominent existing portfolio, SATzilla, which relies on domain-specific, hand-selected problem features; the latent class models, in contrast, use minimal domain knowledge. Their success suggests that these models can lead to more powerful and more general algorithm portfolio methods.

Graph-based semi-supervised learning methods are based on some smoothness assumption about the data. As a discrete approximation of the data manifold, the graph plays a crucial role in the success of such graph-based methods. In most existing methods, graph construction makes use of a predefined weighting function without utilizing label information even when it is available. In this work, by incorporating label information, we seek to enhance the performance of graph-based semi-supervised learning by learning the graph and label inference simultaneously. In particular, we consider a particular setting of semi-supervised learning called transductive learning. Using the LogDet divergence to define the objective function, we propose an iterative algorithm to solve the optimization problem which has closed-form solution in each step. We perform experiments on both synthetic and real data to demonstrate improvement in the graph and in terms of classification accuracy.

We study methods to specify preferences among subsets of a set (a universe ). The methods we focus on are of two types. The first one assumes the universe comes with a preference relation on its elements and attempts to lift that relation to subsets of the universe. That approach has limited expressivity but results in orderings that capture interesting general preference principles. The second method consists of developing formalisms allowing the user to specify "atomic" improvements, and generating from them preferences on the powerset of the universe. We show that the particular formalism we propose is expressive enough to capture the lifted preference relations of the first approach, and generalizes propositional CP-nets. We discuss the importance of domain-independent methods for specifying preferences on sets for knowledge representation formalisms, selecting the formalism of argumentation frameworks as an illustrative example.

However, there are many domains, work that is perhaps closest to ours is the "dead-end heuristic" such as map-based searches (common in GPS navigation, introduced by Björnsson and Halldórsson (2006). They computer games, and robotics) where the entire use a preprocessing phase to identify areas that are deadends, state-space is given explicitly. Optimal paths for such domains and create an abstract graph whose nodes are these can be found relatively quickly with simple heuristics, areas. Initially, the search is performed on the abstracted especially when compared to the time it takes to explore graph. The areas that were not visited during the search exponentially large combinatorial problems. Relative on the abstracted graph are then ignored when the search is quickness, however, might still not be fast enough in certain performed in the original search space. In addition to identifying real-time applications, where further improvement towards dead-ends, our approach also identifies (and prunes, high-speed performance is especially valued.

Previous work on machine scheduling has considered the case of agents who control the scheduled jobs and attempt to minimize their own completion time. We argue that in cloud and grid computing settings, different machines cannot be considered to be fully cooperative as they may belong to competing economic entities, and that agents can easily move their jobs between competing providers. We therefore consider a setting in which the machines are also controlled by selfish agents, and attempt to maximize their own gains by strategically selecting their scheduling policy. We analyze the equilibria that arise due to competition in this 2-sided setting. In particular, not only do we require that the jobs will be in equilibrium with one another, but also that the schedulers' policies will be in equilibrium. We also consider different mixtures of classic deterministic scheduling policies and random scheduling policies.