The extraction of logically-independent fragments out of an ontology ABox can be useful for solving the tractability problem of querying ontologies with large ABoxes. In this paper, we propose a formal definition of an ABox module, such that it guarantees complete preservation of facts about a given set of individuals, and thus can be reasoned independently w.r.t. the ontology TBox. With ABox modules of this type, isolated or distributed (parallel) ABox reasoning becomes feasible, and more efficient data retrieval from ontology ABoxes can be attained. To compute such an ABox module, we present a theoretical approach and also an approximation for $\mathcal{SHIQ}$ ontologies. Evaluation of the module approximation on different types of ontologies shows that, on average, extracted ABox modules are significantly smaller than the entire ABox, and the time for ontology reasoning based on ABox modules can be improved significantly.

Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays a central role both statistically and computationally. Unfortunately, real-world data often does not fit well to sparse graphical models. In this paper, we focus on a family of latent variable Gaussian graphical models (LVGGM), where the model is conditionally sparse given latent variables, but marginally non-sparse. In LVGGM, the inverse covariance matrix has a low-rank plus sparse structure, and can be learned in a regularized maximum likelihood framework. We derive novel parameter estimation error bounds for LVGGM under mild conditions in the high-dimensional setting. These results complement the existing theory on the structural learning, and open up new possibilities of using LVGGM for statistical inference.

Shimon Ullman Weizmann Institute of Science shimon.ullman@weizmann.ac.il Abstract We consider the problem of learning from a similarity matrix (such as spectral clustering and low-dimensional embedding), when computing pairwise similarities are costly, and only a limited number of entries can be observed. We provide a theoretical analysis using standard notions of graph approximation, significantly generalizing previous results (which focused on spectral clustering with two clusters). We also propose a new algorithmic approach based on adaptive sampling, which experimentally matches or improves on previous methods, while being considerably more general and computationally cheaper. 1 Introduction Many unsupervised learning algorithms, such as spectral clustering [18], [2] and low-dimensional embedding via Laplacian eigenmaps and diffusion maps [3],[16], need as input a matrix of pairwise similaritiesW between the different objects in our data. In some cases, obtaining the full matrix can be a costly matter. For example, w ij may be based on some expensive-to-compute metric such as W2D [5]; based on some physical measurement (such as in some computational biology applications); or is given by a human annotator. In such cases, we would like to have a good approximation of the (initially unknown) matrix, while querying only a limited number of entries. An alternative but equivalent viewpoint is the problem of approximating an unknown weighted undirected graph, by querying a limited number of edges. This question has received previous attention in works such as [17] and [9], which focus on the task of spectral clustering into two clusters, and assuming two such distinct clusters indeed exist (i.e. that there is a big gap between the second and third eigenvalues of the Laplacian matrix). In this work we consider, both theoretically and algorithmically, the question of query-based graph approximation more generally, obtaining results relevant beyond two clusters and beyond spectral clustering. When considering graph approximations, the first question is what notion of approximation to consider. One important notion is cut approximation [14] where we wish for every cut in the approximated graph to have weight close to the weight of the cut in the original graph up to a multiplicative factor. Many machine learning algorithms (and many more general algorithms) such as cut based clustering [11], energy minimization [22], etc. [1] are based on cuts, so this notion of approximation is natural for these uses.

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.

We introduce a Bayesian solution for the problem in forensic speaker recognition, where there may be very little background material for estimating score calibration parameters. We work within the Bayesian paradigm of evidence reporting and develop a principled probabilistic treatment of the problem, which results in a Bayesian likelihood-ratio as the vehicle for reporting weight of evidence. We show in contrast, that reporting a likelihood-ratio distribution does not solve this problem. Our solution is experimentally exercised on a simulated forensic scenario, using NIST SRE'12 scores, which demonstrates a clear advantage for the proposed method compared to the traditional plugin calibration recipe.

There is a strong demand for robots to work in environments, such as aircraft manufacturing, where they share tasks with humans and must quickly adapt to each other's needs. To do so, a robot must both infer the intent of humans, and must adapt accordingly. The literature to date has made great progress on these two tasks - recognition and adaptation - but largely as separate research activities. In this paper, we present a unified approach to these two problems, in which recognition and adaptation occur concurrently and holistically.  Key to our approach is a task representation that uses choice to represent alternative plans for both the human and robot, allowing a single set of algorithms to simultaneously achieve recognition and adaptation. To achieve such fluidity, a labeled propagation mechanism is used where decisions made by the human and robot during execution are propagated to relevant future open choices, as determined by causal link analysis, narrowing the possible options that the human would reasonably take (hence achieving intent recognition) as well as the possible actions the robot could consistently take (adaptation). This paper introduces Pike, an executive for human-robot teamwork that quickly adapts and infers intent based on the preconditions of actions in the plan, temporal constraints, unanticipated disturbances, and choices made previously (by either robot or human).  We evaluate Pike's performance and demonstrate it on a household task in a human-robot team testbed.

Micro Aerial Vehicles (MAVs) are increasingly regarded as a valid low-cost alternative to UAVs and ground robots in surveillance missions and a number of other civil and military applications. Research on autonomous MAVs is still in its infancy and has focused almost exclusively on integrating control and computer vision techniques to achieve reliable autonomous flight. In this paper, we describe our approach to using automated planning in order to elicit high-level intelligent behaviour from autonomous MAVs engaged in surveillance applications. Planning offers effective tools to handle the unique challenges faced by MAVs that relate to their fast and unstable dynamics as well as their low endurance and small payload capabilities. We demonstrate our approach by focusing on the "Parrot AR.Drone2.0" quadcopter and Search-and-Tracking missions, which involve searching for a mobile target and tracking it after it is found.

This paper addresses the challenge of efficiently and accurately predicting an electric vehicle's attainable range. Specifically, our approach accounts for a driver's generalised route preferences to provide up-to-date, personalised information based on estimates of the energy required to reach every possible destination in a map. We frame this task in the context of sequential decision making and show that energy consumption in reaching a particular destination can be formulated as policy evaluation in a Markov Decision Process. In particular, we exploit the properties of the model adopted for predicting likely energy consumption to every possible destination in a realistically sized map in real-time. The policy to be evaluated is learned and, over time, refined using Inverse Reinforcement Learning to provide for a life-long adaptive system. Our approach is evaluated using a publicly available dataset providing real trajectory data of 50 individuals spanning approximately 10,000 miles of travel. We show that by accounting for driver specific route preferences our system significantly reduces the relative error in energy prediction compared to more common, driver-agnostic heuristics such as shortest-path or shortest-time routes.

We study a daily mine planning problem where, given a set of blocks we wishto mine, our task is to generate a mining sequence for the excavators suchthat blending resource constraints are met at various stages of thesequence. Such time-oriented resource constraintsare not traditionally handled well by automated planners. On the other hand,the remaining problem involves finding node-disjoint sequences withstate-dependent travel times on the arcs, which are highly challenging for a Mixed-Integer Program (MIP).In this paper, we address the problem of finding feasible sequences using a combined MIP and planning based decomposition approach. The MIP takes care of the resource constraints, and the planner solves the remaining sequence problem. We extend the notion of finding feasible sequences to finding good feasible sequences, by devising a heuristic objective function in the MIP, which improves the resulting search space for the planner.We empirically analyse the scalability of our approach on a benchmark data set, before demonstrating its effectiveness on a real world case study provided by our industry partner. These results demonstrate that by using a heuristic MIP, it is possible to obtain better makespan results with a suboptimal planner than by using an optimal planner with an uninformed MIP.

Stackelberg games form the core of a number of tools deployed for computing optimal patrolling strategies in adversarial domains, such as the US Federal Air Marshall Service and the US Coast Guard. In traditional Stackelberg security game models the attacker knows only the probability that each target is covered by the defender, but is oblivious to the detailed timing of the coverage schedule. In many real-world situations, however, the attacker can observe the current location of the defender and can exploit this knowledge to reason about the defender’s future moves. We show that this general modeling framework can be captured using adversarial patrolling games (APGs) in which the defender sequentially moves between targets, with moves constrained by a graph, while the attacker can observe the defender’s current location and his (stochastic) policy concerning future moves. We offer a very general model of infinite-horizon discounted adversarial patrolling games. Our first contribution is to show that defender policies that condition only on the previous defense move (i.e., Markov stationary policies) can be arbitrarily suboptimal for general APGs. We then offer a mixed-integer non-linear programming (MINLP) formulation for computing optimal randomized policies for the defender that can condition on history of bounded, but arbitrary, length, as well as a mixed-integer linear programming (MILP) formulation to approximate these, with provable quality guarantees. Additionally, we present a non-linear programming (NLP) formulation for solving zero-sum APGs. We show experimentally that MILP significantly outperforms the MINLP formulation, and is, in turn, significantly outperformed by the NLP specialized to zero-sum games.