When an optimal solution is not required, satisficing search methods such as greedy best-first search are often used to find solutions quickly. In work on satisficing search, there has been substantial attention devoted to how to solve problems associated with local minima or plateaus in the heuristic function. One technique that has been shown to be quite promising is using an alternative heuristic function that does not estimate cost-to-go, but rather estimates distance-to-go. There is currently little beyond intuition to explain its superiority. We begin by empirically showing that the success of the distance-to-go heuristic appears related to its having smaller local minima. We then discuss a reasonable theoretical model of heuristics and show that, under this model, the expected size of local minima is higher for a cost-to-go heuristic than a distance-to-go heuristic, offering a possible explanation as to why distance-to-go heuristics tend to outperform cost-to-go heuristics.

Graphical models, such as Bayesian Networks and Markov networks play an important role in artificial intelligence and machine learning. Inference is a central problem to be solved on these networks. This, and other problems on these graph models are often known to be hard to solve in general, but tractable on graphs with bounded Treewidth. Therefore, finding or approximating the Treewidth of a graph is a fundamental problem related to inference in graphical models. In this paper, we study the approximability of a number of graph problems: Treewidth and Pathwidth of graphs, Minimum Fill-In, and a variety of different graph layout problems such as Minimum Cut Linear Arrangement. We show that, assuming Small Set Expansion Conjecture, all of these problems are NP-hard to approx- imate to within any constant factor in polynomial time.

We are witnessing a paradigm shift in human language technology that may well have an impact on the field comparable to the statistical revolution: acquiring large-scale resources by exploiting collective intelligence. An illustration of this approach is Phrase Detectives, an interactive online game-with-a-purpose for creating anaphorically annotated resources that makes use of a highly distributed population of contributors with different levels of expertise. The paper gives an overview of all aspects of Phrase Detectives, from the design of the game and the methods used, to the results obtained so far. It furthermore summarises the lessons that have been learnt in developing the game to help other researchers assess and implement the approach.

But growing up, in conflict with her father We present a graphic novel project aiming at illustrating current research results and issues regarding the creative process and its relation with artificial intelligence. The main character, Max Order, is an artist who symbolizes the difficulty of coming up with new, creative ideas, giving up imitation of others and finding one's own style.

With the prevalence of mobile search nowadays, the benefits of mobile query recommendation are well recognized, which provide formulated queries sticking to users’ search intent. In this paper, we introduce the problem of query recommendation on mobile devices and model the user-location-query relations with a tensor representation. Unlike previous studies based on tensor decomposition, we study this problem via tensor function learning. That is, we learn the tensor function from the side information of users, locations and queries, and then predict users’ search intent. We develop an efficient alternating direction method of multipliers (ADMM) scheme to solve the introduced problem. We empirically evaluate our approach based on the mobile query dataset from Bing search engine in the city of Beijing, China, and show that our method can outperform several state-of-the-art approaches.

Principal component analysis (PCA) is an unsupervised method for learning low-dimensional features with orthogonal projections. Multilinear PCA methods extend PCA to deal with multidimensional data (tensors) directly via tensor-to-tensor projection or tensor-to-vector projection (TVP). However, under the TVP setting, it is difficult to develop an effective multilinear PCA method with the orthogonality constraint. This paper tackles this problem by proposing a novel Semi-Orthogonal Multilinear PCA (SO-MPCA) approach. SO-MPCA learns low-dimensional features directly from tensors via TVP by imposing the orthogonality constraint in only one mode. This formulation results in more captured variance and more learned features than full orthogonality. For better generalization, we further introduce a relaxed start (RS) strategy to get SO-MPCA-RS by fixing the starting projection vectors, which increases the bias and reduces the variance of the learning model. Experiments on both face (2D) and gait (3D) data demonstrate that SO-MPCA-RS outperforms other competing algorithms on the whole, and the relaxed start strategy is also effective for other TVP-based PCA methods.

Tensor factorization methods provide a useful way to extract latent factors from complex multirelational data, and also for predicting missing data. Developing tensor factorization methods for massive tensors, especially when the data are binary- or count-valued (which is true of most real-world tensors), however, remains a challenge. We develop a scalable probabilistic tensor factorization framework that enables us to perform efficient factorization of massive binary and count tensor data. The framework is based on (i) the Polya-Gamma augmentation strategy which makes the model fully locally conjugate and allows closed-form parameter updates when data are binary- or count-valued; and (ii) an efficient online Expectation Maximization algorithm, which allows processing data in small mini-batches, and facilitates handling massive tensor data. Moreover, various types of constraints on the factor matrices (e.g., sparsity, non-negativity) can be incorporated under the proposed framework, providing good interpretability, which can be useful for qualitative analyses of the results. We apply the proposed framework on analyzing several binary- and count-valued real-world data sets.

For instance, within the time relations, one can say that an interval A meets another interval B at a coarse granularity In this paper, we propose a qualitative formalism (i.e., looking at it with a general point of view), but for representing and reasoning about time at different that A is before B at a fine granularity (i.e., with a closer point scales. It extends the algebra of Euzenat [2001] of view). The usual algebras cannot process this knowledge and overcomes its major limitations, allowing one without leading to an inconsistency. To solve this problem, to reason about relations between points and intervals. Euzenat has proposed a granular extension of the point algebra Our approach is more expressive than of Vilain et al. and one of Allen's interval algebra [Euzenat, the other algebras of temporal relations: for instance, 2001], each one providing a table describing how relations some relations are more relaxed than those change when considered at a finer granularity (downward in Allen's [1983] algebra, while others are stricter.

Propositional Typicality Logic (PTL) is a recently proposed logic, obtained by enriching classical propositional logic with a typicality operator. In spite of the non-monotonic features introduced by the semantics adopted for the typicality operator, the obvious Tarskian definition of entailment for PTL remains monotonic and is therefore not appropriate. We investigate different (semantic) versions of entailment for PTL, based on the notion of Rational Closure as defined by Lehmann and Magidor for KLM-style conditionals, and constructed using minimality. Our first important result is an impossibility theorem showing that a set of proposed postulates that at first all seem appropriate for a notion of entailment with regard to typicality cannot be satis- fied simultaneously. Closer inspection reveals that this result is best interpreted as an argument for advocating the development of more than one type of PTL entailment. In the spirit of this interpretation, we define two primary forms of entailment for PTL and discuss their advantages and disadvantages

In recent times, social media has become a popular medium for many election campaigns. It not only allows candidates to reach out to a large section of the electorate, it is also a potent medium for people to express their opinion on the proposed policies and promises of candidates. Analyzing social media data is challenging as the text can be noisy, sparse and even multilingual. In addition, the information may not be completely trustworthy, particularly in the presence of propaganda, promotions and rumors. In this paper we describe our work for analyzing election campaigns using social media data. Using data from the 2012 US presidential elections and the 2013 Philippines General elections, we provide detailed experiments on our methods that use granger causality to identify topics that were most “causal” for public opinion and which in turn, give an interpretable insight into “elections topics” that were most important. Our system was deployed by the largest media organization in the Philippines during the 2013 General elections and using our work, the media house able to identify and report news stories much faster than competitors and reported higher TRP ratings during the election.