The American Football betting market provides a particularly attractive domain to study the nexus between public sentiment and the wisdom of crowds. In this paper, we present the first substantial study of the relationship between the NFL betting line and public opinion expressed in blogs and microblogs (Twitter). We perform a large-scale study of four distinct text streams: LiveJournal blogs, RSS blog feeds captured by Spinn3r, Twitter, and traditional news media. Our results show interesting disparities between the first and second halves of each season. We present evidence showing usefulness of sentiment on NFL betting. We demonstrate that a strategy betting roughly 30 games per year identified winner roughly 60% of the time from 2006 to 2009, well beyond what is needed to overcome the bookie's typical commission(53%).

IEA/AIE-10 will be held June 1-4, 2010, in Cordoba, Spain. AI Twelfth International Conference Magazine also maintains a calendar listing that includes nonaffiliated conferences on Enterprise Information Systems. ICEIS 2010 will be held June 8-12, 2010, in Funchal, Portugal. AAAI-12 and Seventh International Conference Fourth International Conference on IAAI-12 will be held July 22-26, 2012, on Informatics in Control, Automation Weblogs and Social Media. AAAI-10 and IAAI-10 will be held July and Reasoning.

The Japanese fixed-property tax is imposed by municipalities on the owners of land, buildings, and depreciation assets (all hereinafter referred to as "fixed assets") on January 1 of every year by calculating the tax sum according to current asset values. This identification work is contracted out to survey companies. The identification of such en over a scale that can cover an actual area of 800 changes is entrusted to survey companies who hire by 600 meters or 500 by 600 meters (variable a large number of workers (figure 1, left). However, depending on the municipality), and every municipality reliance on human labor has led to problems has several hundred photographs that must detailed in the following paragraphs. Under these circumstances, the incentives for It takes about 10 hours to read and interpret a single the municipalities to overcome such challenges by photograph, and the average municipality automating or systematizing the photograph-reading must perform this work for several hundred photographs.

This paper proposes a layered graph model for representing the internal structure of complex plane regions, where each node represents the closure of a connected component of the interior or exterior of a complex region. The model provides a complete representation in the sense that the (global) nine-intersections between the interiors, the boundaries, and the exteriors of two complex regions can be determined by the (local) RCC8 relations between associated simple regions. 

In this paper, we consider propositional Topological logics are a family of languages for representing topological logics with connectedness, i.e. topological and reasoning about topological data. The non-logical logics in which the only logical connectives are the usual primitives of these languages stand for various topological Boolean operators, but where there is a non-logical primitive relations and operations, and their valid formulas encode our expressing the property of topological connectedness knowledge about those relations and operations. Consider, (or a variant thereof). We show that such topological logics for example, the six relations illustrated in Figure 1. By em-are typically sensitive both to the spaces they are interpreted over and--more particularly--to the subsets of those spaces over which their variables are allowed to range.

We study logical principles connecting two relations: independence, which is known as nondeducibility in the study of information flow, and functional dependence. Two different epistemic interpretations for these relations are discussed: semantics of secrets and probabilistic semantics. A logical system sound and complete with respect to both of these semantics is introduced and is shown to be decidable.

We present a novel algorithm and a new understanding of reasoning about a sequence of deterministic actions with a probabilistic prior. When the initial state of a dynamic system is unknown, a probability distribution can be still specified over the initial states. Estimating the posterior distribution over states filtering after some deterministic actions occurred is a problem relevant to AI planning, natural language processing (NLP), and robotics among others. Current approaches to filtering deterministic actions are not tractable even if the distribution over the initial system state is represented compactly. The reason is that state variables become correlated after a few steps. The main innovation in this paper is a method for sidestepping this problem by redefining state variables dynamically at each time step such that the posterior for time t is represented in a factored form. This update is done using a progression algorithm as a subroutine, and our algorithm's tractability follows when that subroutine is tractable. Our results are for general deterministic actions and in particular, our algorithm is tractable for one-to-one and STRIPS actions. We apply our reasoning algorithm about deterministic actions to reasoning about sequences of probabilistic actions and improve the efficiency of the current probabilistic reasoning approaches. We demonstrate the efficiency of the new algorithm empirically over AI-Planning data sets.

In constraint or preference reasoning, a typical task is to compute a solution, or an optimal solution. However, when one has already a solution, it may be important to produce the next solution following the given one in a linearization of the solution ordering where more preferred solutions are ordered first. In this paper, we study the computational complexity of finding the next solution in some common preference-based representation formalisms. We show that this problem is hard in general CSPs, but it can be easy in tree-shaped CSPs and tree-shaped fuzzy CSPs. However, it is difficult in weighted CSPs, even if we restrict the shape of the constraint graph. We also consider CP-nets, showing that the problem is easy in acyclic CP-nets, as well as in constrained acyclic CP-nets where the (soft) constraints are tree-shaped and topologically compatible with the CP-net.

Handling exceptions in a knowledge-based system has been considered as an important issue in many domains of applications, such as medical domain. In this paper, we propose several preferential semantics for plausible subsumption to deal with exceptions in description logic-based knowledge bases. Our preferential semantics are defined in the framework of possibility theory, which is an uncertainty theory devoted to the handling of incomplete information. We consider the properties of these semantics and their relationships. Entailment of these plausible subsumption relative to a knowledge base is also considered. We show the close relationship between two of our semantics and the mutually dual preferential semantics given by Britz, Heidema and Meyer. Finally, we show that our semantics for plausible subsumption can be reduced to standard semantics of an expressive description logic. Thus, the problem of plausible subsumption checking under our semantics can be reduced to the problem of subsumption checking under the classical semantics.

A plan with rich control structures like branches and loops can usually serve as a general solution that solves multiple planning instances in a domain. However, the correctness of such generalized plans is non-trivial to define and verify, especially when it comes to whether or not a plan works for all of the infinitely many instances of the problem. In this paper, we give a precise definition of a generalized plan representation called an FSA plan, with its semantics defined in the situation calculus. Based on this, we identify a class of infinite planning problems, which we call one-dimensional (1d), and prove a correctness result that 1d problems can be verified by finite means. We show that this theoretical result leads to a practical algorithm that does this verification practically, and a planner based on this verification algorithm efficiently generates provably correct plans for 1d problems.