We present a novel approach to low-dimensional neighbor embedding for visualization, based on formulating an information retrieval based neighborhood preservation cost function as Maximum satisfiability on a discretized output display. The method has a rigorous interpretation as optimal visualization based on the cost function. Unlike previous low-dimensional neighbor embedding methods, our formulation is guaranteed to yield globally optimal visualizations, and does so reasonably fast. Unlike previous manifold learning methods yielding global optima of their cost functions, our cost function and method are designed for low-dimensional visualization where evaluation and minimization of visualization errors are crucial. Our method performs well in experiments, yielding clean embeddings of datasets where a state-of-the-art comparison method yields poor arrangements. In a real-world case study for semi-supervised WLAN signal mapping in buildings we outperform state-of-the-art methods.

Multi-agent learning is a challenging open task in artificial intelligence. It is known an interesting connection between multi-agent learning algorithms and evolutionary game theory, showing that the learning dynamics of some algorithms can be modeled as replicator dynamics with a mutation term. Inspired by the recent sequence-form replicator dynamics, we develop a new version of the Q-learning algorithm working on the sequence form of an extensive-form game allowing thus an exponential reduction of the dynamics length w.r.t. those of the normal form. The dynamics of the proposed algorithm can be modeled by using the sequence-form replicator dynamics with a mutation term. We show that, although sequence-form and normal-form replicator dynamics are realization equivalent, the Q-learning algorithm applied to the two forms have non-realization equivalent dynamics. Originally from the previous works on evolutionary game theory models form multi-agent learning, we produce an experimental evaluation to show the accuracy of the model.

We present three new filtering algorithms for the Disjunctive constraint that all have a linear running time complexity in the number of tasks. The first algorithm filters the tasks according to the rules of the time tabling. The second algorithm performs an overload check that could also be used for the Cumulative constraint. The third algorithm enforces the rules of detectable precedences. The two last algorithms use a new data structure that we introduce and that we call the time line. This data structure provides many constant time operations that were previously implemented in logarithmic time by the Theta-tree data structure. Experiments show that these new algorithms are competitive even for a small number of tasks and outperform existing algorithms as the number of tasks increases.

Backdoors measure the distance to tractable fragments and have become an important tool to find fixed-parameter tractable (fpt) algorithms. Despite their success, backdoors have not been used for planning, a central problem in AI that has a high computational complexity. In this work, we introduce two notions of backdoors building upon the causal graph. We analyze the complexity of finding a small backdoor (detection) and using the backdoor to solve the problem (evaluation) in the light of planning with (un)bounded plan length/domain of the variables. For each setting we present either an fpt-result or rule out the existence thereof by showing parameterized intractability. In three cases we achieve the most desirable outcome: detection and evaluation are fpt.

Dictionary learning (DL) has now become an important feature learning technique that owns state-of-the-art recognition performance. Due to sparse characteristic of data in real-world applications, DL uses a set of learned dictionary bases to represent the linear decomposition of a data point. Fisher discrimination DL (FDDL) is a representative supervised DL method, which constructs a structured dictionary whose atoms correspond to the class labels. Recent years have witnessed a growing interest in multi-view (more than two views) feature learning techniques. Although some multi-view (or multi-modal) DL methods have been presented, there still exists much room for improvement. How to enhance the total discriminability of dictionaries and reduce their redundancy is a crucial research topic. To boost the performance of multi-view DL technique, we propose an uncorrelated multi-view discrimination DL (UMDDL) approach for recognition. By making dictionary atoms correspond to the class labels such that the obtained reconstruction error is discriminative, UMDDL aims to jointly learn multiple dictionaries with totally favorable discriminative power. Furthermore, we design the uncorrelated constraint for multi-view DL, so as to reduce the redundancy among dictionaries learned from different views. Experiments on several public datasets demonstrate the effectiveness of the proposed approach.

Automatic speech recognition (ASR) technology has been developed to such a level that off-the-shelf distributed speech recognition services are available (free of cost), which allow researchers to integrate speech into their applications with little development effort or expert knowledge leading to better results compared with previously used open-source tools. Often, however, such services do not accept language models or grammars but process free speech from any domain. While results are very good given the enormous size of the search space, results frequently contain out-of-domain words or constructs that cannot be understood by subsequent domain-dependent natural language understanding (NLU) components. We present a versatile post-processing technique based on phonetic distance that integrates domain knowledge with open-domain ASR results, leading to improved ASR performance. Notably, our technique is able to make use of domain restrictions using various degrees of domain knowledge, ranging from pure vocabulary restrictions via grammars or N-Grams to restrictions of the acceptable utterances. We present results for a variety of corpora (mainly from human-robot interaction) where our combined approach significantly outperforms Google ASR as well as a plain open-source ASR solution.

A wide range of robotic missions contain actions that exhibit looping behavior. Examples of these actions include picking fruit in agriculture, pick-and-place tasks in manufacturing and search patterns in robotic search or survey missions. These looping actions often have a range of acceptable values for the number of loops and a preference function over them. For example, during robotic survey missions, the information gain is expected to increase with the number of loops in a search pattern. Since these looping actions also take time, which is typically bounded, there is a challenge of maximizing utility while respecting time constraints. In this paper, we introduce the Looping Temporal Problem with Preference (LTPP) as a simple parameterized extension of a simple temporal problem. In addition, we introduce a scheduling algorithm for LTPPs which leverages the structure of the problem to find the optimal solution efficiently. We show more than an order of magnitude improvement in run-time over current scheduling techniques and framing a LTPP as a MINLP.

In this paper, we present a hybrid grammar formalism designed to learn structured models of natural iconic gesture performances that allow for compressed representation and robust recognition. We analyze a dataset of iconic gestures and show how the proposed Feature-based Stochastic Context-Free Grammar (FSCFG) can generalize over both structural and feature-based variations among different gesture performances.

Exchangeability is a central notion in statistics and probability theory. The assumption that an infinite sequence of data points is exchangeable is at the core of Bayesian statistics. However, finite exchangeability as a statistical property that renders probabilistic inference tractable is less well-understood. We develop a theory of finite exchangeability and its relation to tractable probabilistic inference. The theory is complementary to that of independence and conditional independence. We show that tractable inference in probabilistic models with high treewidth and millions of variables can be explained with the notion of finite (partial) exchangeability. We also show that existing lifted inference algorithms implicitly utilize a combination of conditional independence and partial exchangeability.

We examine how to use emerging far-infrared imager ensembles to detect certain objects of interest (e.g., faces, hands, people and animals) in synchronized RGB video streams at very low power. We formulate the problem as one of selecting subsets of sensing elements (among many thousand possibilities) from the ensembles for tests. The subset selection problem is naturally adaptive and online: testing certain elements early can obviate the need for testing many others later, and selection policies must be updated at inference time. We pose the ensemble sensor selection problem as a structured extension of test-cost-sensitive classification, propose a principled suite of techniques to exploit ensemble structure to speed up processing and show how to re-estimate policies fast. We estimate reductions in power consumption of roughly 50x relative to even highly optimized implementations of face detection, a canonical object-detection problem. We also illustrate the benefits of adaptivity and online estimation.