Existing controller-based approaches for centralized and decentralized POMDPs are based on automata with output known as Moore machines. In this paper, we show that several advantages can be gained by utilizing another type of automata, the Mealy machine. Mealy machines are more powerful than Moore machines, provide a richer structure that can be exploited by solution methods, and can be easily incorporated into current controller-based approaches. To demonstrate this, we adapted some existing controller-based algorithms to use Mealy machines and obtained results on a set of benchmark domains. The Mealy-based approach always outperformed the Moore-based approach and often outperformed the state-of-the-art algorithms for both centralized and decentralized POMDPs. These findings provide fresh and general insights for the improvement of existing algorithms and the development of new ones.

Security games, and important class of Stackelberg games, are used in deployed decision-support tools in use by LAX police and the Federal Air Marshals Service. The algorithms used to solve these games find optimal randomized schedules to allocate security resources for infrastructure protection. Unfortunately, the state of the art algorithms either fail to scale or to provide a correct solution for large problems with arbitrary scheduling constraints. We introduce ASPEN, a branch-and-price approach that overcomes these limitations based on two key contributions: (i) A column-generation approach that exploits a novel network flow representation, avoiding a combinatorial explosion of schedule allocations; (ii) A branch-and-bound algorithm that generates bounds via a fast algorithm for solving security games with relaxed scheduling constraints. ASPEN is the first known method for efficiently solving massive security games with arbitrary schedules.

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.

Matrix factorization is a fundamental technique in machine learning that is applicable to collaborative filtering, information retrieval and many other areas. In collaborative filtering and many other tasks, the objective is to fill in missing elements of a sparse data matrix. One of the biggest challenges in this case is filling in a column or row of the matrix with very few observations. In this paper we introduce a Bayesian matrix factorization model that performs regression against side information known about the data in addition to the observations. The side information helps by adding observed entries to the factored matrices. We also introduce a nonparametric mixture model for the prior of the rows and columns of the factored matrices that gives a different regularization for each latent class. Besides providing a richer prior, the posterior distribution of mixture assignments reveals the latent classes. Using Gibbs sampling for inference, we apply our model to the Netflix Prize problem of predicting movie ratings given an incomplete user-movie ratings matrix. Incorporating rating information with gathered metadata information, our Bayesian approach outperforms other matrix factorization techniques even when using fewer dimensions.

We introduce a new discriminative piecewise linear model for classification. A two-step method is developed to construct the model. In the first step, we sample some boundary points that lie between positive and negative data, as well as corresponding directions from negative data to positive data. The sampling result gives a discriminative nonparametric decision surface, which preserves enough information to correctly classify all training data. To simplify this surface, in the second step we propose a nonparametric approach for linear surface segmentation using Dirichlet process mixtures. The final result is a piecewise linear model, in which the number of linear surface pieces is automatically determined by the Bayesian inference according to data. Experiments on both synthetic and real data verify the effectiveness of the proposed model.

As we see later, the mathematical derivations are more elaborate A Bayesian network is a probabilistic graphical model that than those recently introduced for BIC and AIC criteria relies on a structured dependency among random variables (de Campos, Zeng, and Ji 2009), and the reduction in the to represent a joint probability distribution in a compact and search space and cache size are less effective when priors efficient manner. It is composed by a directed acyclic graph are strong, but still relevant. This is expected, as the BIC (DAG) where nodes are associated to random variables and score is known to penalize complex graphs more than BD conditional probability distributions are defined for variables scores do. We show that the search space can be reduced given their parents in the graph. Learning the graph (or without losing the global optimality guarantee and that the structure) of these networks from data is one of the most memory requirements are small in many practical cases.

As the Internet-based crowdsourcing services become more and more popular, learning from multiple teachers or sources has received more attention of the researchers in the machine learning area. In this setting, the learning system is dealing with samples and labels provided by multiple teachers, who in common cases, are non-expert. Their labeling styles and behaviors are usually diverse, some of which are even detrimental to the learning system. Thus, simply putting them together and utilizing the algorithms designed for single-teacher scenario would be not only improper, but also damaging. The problem calls for more specific methods. Our work focuses on a case where the teachers are composed of good ones and irresponsible ones. By irresponsible, we mean the teacher who takes the labeling task not seriously and label the sample at random without inspecting the sample itself. This behavior is quite common when the task is not attractive enough and the teacher just wants to finish it as soon as possible. Sometimes, the irresponsible teachers could take a considerable part among all the teachers. If we do not take out their effects, our learning system would be ruined with no doubt. In this paper, we propose a method for picking out the good teachers with promising experimental results. It works even when the irresponsible teachers are dominating in numbers.

In this paper, we propose a translation from normal first-order logic programs under the answer set semantics to first-order theories on finite structures. Specifically, we introduce ordered completions which are modifications of Clark's completions with some extra predicates added to keep track of the derivation order, and show that on finite structures, classical models of the ordered-completion of a normal logic program correspond exactly to the answer sets (stable models) of the logic program.

Analyzing clickthrough log data is important for improving search performance as well as understanding user behaviors. In this paper, we propose a novel collaborative ranking model to tackle two difficulties in analyzing clickthrough log. First, previous studies have shown that users tend to click top-ranked results even they are less relevant. Therefore, we use pairwise ranking relation to avoid the position bias in clicks. Second, since click data are extremely sparse with respect to each query or user, we construct a collaboration model to eliminate the sparseness problem. We also find that the proposed model and previous popular used click-based models address different aspects of clickthrough log data. We further propose a hybrid model that can achieve significant improvement compared to the baselines on a large-scale real world dataset.

The Embodied Conversational Agents (ECAs) are computergenerated motivation for the study of ECAs, inside PRAIA project, characters whose purpose is to exhibit the same started with the belief that ECAs represent a promising solution properties as humans in face-to-face conversation. The general for responding appropriately to student's in educational goal of researchers in the field of ECAs is to create environments. This work, however, cannot be placed inside agents that can be more natural, believable and easy to use. the "task and Application domains" concentration of the taxonomy Due to the broad scope of research and the multidisciplinary presented above. We are not interested in designing of the field, many other investigations can arise in many different and implementing an ECA to meet the needs and fill a suitable areas, leading researchers to face numerous questions: role within one specific educational environment. We What kind of embodiment to use? What parts of the body to believe that making a general contribution in other concentrations represent? What kind of modalities to explore? What personality will increase the possibilities of future research inside model to consider? Will the ECA have emotions?