Macros have long been studied in AI planning [9, 18]. Many domain-dependent applications of macros have been exhibited and studied [15, 17, 12]; also, a number of domain-independent methods for learning, inferring, filtering, and applying macros have been the topic of research continuing up to the present [2, 7, 20]. In this paper, we present a domain-independent algorithm that computes macros in a novel way. Our algorithm computes macros "on-the-fly" for a given set of states and does not require previously learned or inferred information, nor does it need any prior domain knowledge. We exhibit the power of our algorithm by using it to define new domain-independent tractable classes of classical planning that strictly extend previously defined such classes [6], and can be proved to include Blocksworld-arm 1 and Towers of Hanoi. We believe that this is notable as theoretically defined, domainindependent tractable classes have generally struggled to incorporate construction-type domains such as these two. We hence give theoretically grounded evidence of the computational value of macros in planning.

We propose a hierarchy for approximate inference based on the Dobrushin, Lanford, Ruelle (DLR) equations. This hierarchy includes existing algorithms, such as belief propagation, and also motivates novel algorithms such as factorized neighbors (FN) algorithms and variants of mean field (MF) algorithms. In particular, we show that extrema of the Bethe free energy correspond to approximate solutions of the DLR equations. In addition, we demonstrate a close connection between these approximate algorithms and Gibbs sampling. Finally, we compare and contrast various of the algorithms in the DLR hierarchy on spin-glass problems. The experiments show that algorithms higher up in the hierarchy give more accurate results when they converge but tend to be less stable.

In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data (Spirtes et al. 2000; Pearl 2000). Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, we show how to discover the complete causal structure of continuous-valued data, under the assumptions that (a) the data generating process is linear, (b) there are no unobserved confounders, and (c) disturbance variables have non-gaussian distributions of non-zero variances. The solution relies on the use of the statistical method known as independent component analysis (ICA), and does not require any pre-specified time-ordering of the variables. We provide a complete Matlab package for performing this LiNGAM analysis (short for Linear Non-Gaussian Acyclic Model), and demonstrate the effectiveness of the method using artificially generated data.

Polarimetric Synthetic Aperture Radar (PolSAR) images are establishing as an important source of information in remote sensing applications. The most complete format this type of imaging produces consists of complex-valued Hermitian matrices in every image coordinate and, as such, their visualization is challenging. They also suffer from speckle noise which reduces the signal-to-noise ratio. Smoothing techniques have been proposed in the literature aiming at preserving different features and, analogously, projections from the cone of Hermitian positive matrices to different color representation spaces are used for enhancing certain characteristics. In this work we propose the use of stochastic distances between models that describe this type of data in a Nagao-Matsuyama-type of smoothing technique. The resulting images are shown to present good visualization properties (noise reduction with preservation of fine details) in all the considered visualization spaces.

Echo state network (ESN) is viewed as a temporal non-orthogonal expansion with pseudo-random parameters. Such expansions naturally give rise to regressors of various relevance to a teacher output. We illustrate that often only a certain amount of the generated echo-regressors effectively explain the variance of the teacher output and also that sole local regularization is not able to provide in-depth information concerning the importance of the generated regressors. The importance is therefore determined by a joint calculation of the individual variance contributions and Bayesian relevance using locally regularized orthogonal forward regression (LROFR) algorithm. This information can be advantageously used in a variety of ways for an in-depth analysis of an ESN structure and its state-space parameters in relation to the unknown dynamics of the underlying problem. We present locally regularized linear readout built using LROFR. The readout may have a different dimensionality than an ESN model itself, and besides improving robustness and accuracy of an ESN it relates the echo-regressors to different features of the training data and may determine what type of an additional readout is suitable for a task at hand. Moreover, as flexibility of the linear readout has limitations and might sometimes be insufficient for certain tasks, we also present a radial basis function (RBF) readout built using LROFR. It is a flexible and parsimonious readout with excellent generalization abilities and is a viable alternative to readouts based on a feed-forward neural network (FFNN) or an RBF net built using relevance vector machine (R VM). Introduction ESNs are a novel class of recurrent neural networks (RNN) [1]. Their easy construction and simple training procedure are appealing and have attracted the attention of many researchers. Vector function f is applied element-wise to its arguments. The most common choice forf is either a vector of sigmoid or identity functions. The expansion is carried out so that diverse echoes of an input and teacher signal are generated (hence the name echo-state). This diversity, which should appropriately "explain" a variance of a teacher signal, is the key to the successful training of an ESN.

Twitter introduced user lists in late 2009, allowing users to be grouped according to meaningful topics or themes. Lists have since been adopted by media outlets as a means of organising content around news stories. Thus the curation of these lists is important - they should contain the key information gatekeepers and present a balanced perspective on a story. Here we address this list curation process from a recommender systems perspective. We propose a variety of criteria for generating user list recommendations, based on content analysis, network analysis, and the "crowdsourcing" of existing user lists. We demonstrate that these types of criteria are often only successful for datasets with certain characteristics. To resolve this issue, we propose the aggregation of these different "views" of a news story on Twitter to produce more accurate user recommendations to support the curation process.

The focus of this paper is the calculation of similarity between two concepts from an ontology for a Human-Like Interaction system. In order to facilitate this calculation, a similarity function is proposed based on five dimensions (sort, compositional, essential, restrictive and descriptive) constituting the structure of ontological knowledge. The paper includes a proposal for computing a similarity function for each dimension of knowledge. Later on, the similarity values obtained are weighted and aggregated to obtain a global similarity measure. In order to calculate those weights associated to each dimension, four training methods have been proposed. The training methods differ in the element to fit: the user, concepts or pairs of concepts, and a hybrid approach. For evaluating the proposal, the knowledge base was fed from WordNet and extended by using a knowledge editing toolkit (Cognos). The evaluation of the proposal is carried out through the comparison of system responses with those given by human test subjects, both providing a measure of the soundness of the procedure and revealing ways in which the proposal may be improved.

This article is the first report in the best of AI in Japan series. This series will focus on the prominent accomplishments made in the AI field, not only the research and development but also the AI-related events in society. As the first in the forthcoming series, this opening article features a historical background and the contemporary AI-research activities in Japan. It then highlights some recent prominent results from the industry. Finally, a future perspective is given.

The AAAI-12 technical Christos Papadimtriou (University of abstracts, and poker competition program will kick off with the opening California, Berkeley) will deliver the posters.

The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to reduce bipartite ranking to pairwise classification; in particular, it is well known that the bipartite ranking regret can be formulated as a pairwise classification regret, which in turn can be upper bounded using usual regret bounds for classification problems. Recently, Kotlowski et al. (2011) showed regret bounds for bipartite ranking in terms of the regret associated with balanced versions of the standard (non-pairwise) logistic and exponential losses. In this paper, we show that such (non-pairwise) surrogate regret bounds for bipartite ranking can be obtained in terms of a broad class of proper (composite) losses that we term as strongly proper. Our proof technique is much simpler than that of Kotlowski et al. (2011), and relies on properties of proper (composite) losses as elucidated recently by Reid and Williamson (2010, 2011) and others. Our result yields explicit surrogate bounds (with no hidden balancing terms) in terms of a variety of strongly proper losses, including for example logistic, exponential, squared and squared hinge losses as special cases. We also obtain tighter surrogate bounds under certain low-noise conditions via a recent result of Clemencon and Robbiano (2011).