We present a memory-bounded optimization approach for solving infinite-horizon decentralized POMDPs. Policies for each agent are represented by stochastic finite state controllers. We formulate the problem of optimizing these policies as a nonlinear program, leveraging powerful existing nonlinear optimization techniques for solving the problem. While existing solvers only guarantee locally optimal solutions, we show that our formulation produces higher quality controllers than the state-of-the-art approach. We also incorporate a shared source of randomness in the form of a correlation device to further increase solution quality with only a limited increase in space and time. Our experimental results show that nonlinear optimization can be used to provide high quality, concise solutions to decentralized decision problems under uncertainty.

We present a functional framework for automated mechanism design based on a two-stage game model of strategic interaction between the designer and the mechanism participants, and apply it to several classes of two-player infinite games of incomplete information. At the core of our framework is a black-box optimization algorithm which guides the selection process of candidate mechanisms. Our approach yields optimal or nearly optimal mechanisms in several application domains using various objective functions. By comparing our results with known optimal mechanisms, and in some cases improving on the best known mechanisms, we provide evidence that ours is a promising approach to parametric design of indirect mechanisms.

We present a robust multiple manifolds structure learning (RMMSL) scheme to robustly estimate data structures under the multiple low intrinsic dimensional manifolds assumption. In the local learning stage, RMMSL efficiently estimates local tangent space by weighted low-rank matrix factorization. In the global learning stage, we propose a robust manifold clustering method based on local structure learning results. The proposed clustering method is designed to get the flattest manifolds clusters by introducing a novel curved-level similarity function. Our approach is evaluated and compared to state-of-the-art methods on synthetic data, handwritten digit images, human motion capture data and motorbike videos. We demonstrate the effectiveness of the proposed approach, which yields higher clustering accuracy, and produces promising results for challenging tasks of human motion segmentation and motion flow learning from videos.

Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains. What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the field?s energy and attention, and we discuss existing obstacles that must be addressed. We aim to inspire ongoing discussion and focus on ML that matters.

It assumes that the Consider the problem faced by a unmanned aerial vehicle probability distribution over incoming goals is either known (UAV) dispatcher who must plan for a set of UAVs to service or learn-able and employs the technique of optimization a set of observation requests. To service a request, one of the in hindsight, previously developed for online scheduling UAVs must fly over a given strip of land with its observation and recently investigated for planning with stochastic actions equipment turned on. The dispatcher wants to minimize the (Mercier and van Hentenryck 2007; Yoon et al. 2008; time between when a request arrives and when an UAV has 2010). This technique first samples from the distribution of completed the flyover. Even when the actions of the UAV, possible future goal arrivals and then considers which next such as flying particular routes or switching on/off observational action optimizes the expected cost when averaged over the equipment, can be regarded as deterministic, the sampled futures. By using this anticipatory technique, our stochastic arrival of new requests can make for a challenging planner is able to take future goals into account.

List of sponsors of the 2012 ICAPS conference.

Temporal planning methods usually focus on the objective of minimizing makespan. Unfortunately, this misses a large class of planning problems where it is important to consider a wider variety of temporal and non-temporal preferences, making makespan lower-order concern. In this paper we consider modeling and reasoning with plan quality metrics that are not directly correlated with plan makespan, building on the planner POPF. We begin with the preferences defined in PDDL3, and present a mixed integer programming encoding to manage the the interaction between the hard temporal constraints for plan steps, and soft temporal constraints for preferences. To widen the support of metrics that can be expressed directly in PDDL, we then discuss an extension to soft-deadlines with continuous cost functions, avoiding the need to approximate these with several PDDL3 discrete-cost preferences. We demonstrate the success of our new planner on the benchmark temporal planning problems with preferences, showing that it is the state-of-the-art for such problems. We then analyze the benefits of reasoning with continuous (versus discretized) models of domains with continuous cost functions, showing the improvement in solution quality afforded through making the continuous cost function directly available to the planner.

We propose a novel language-independent approach for improving machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X_1 into a resource-rich language Y given a bi-text containing a limited number of parallel sentences for X_1-Y and a larger bi-text for X_2-Y for some resource-rich language X_2 that is closely related to X_1. This is achieved by taking advantage of the opportunities that vocabulary overlap and similarities between the languages X_1 and X_2 in spelling, word order, and syntax offer: (1) we improve the word alignments for the resource-poor language, (2) we further augment it with additional translation options, and (3) we take care of potential spelling differences through appropriate transliteration. The evaluation for Indonesian- >English using Malay and for Spanish -> English using Portuguese and pretending Spanish is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points, respectively, which is an improvement over the best rivaling approaches, while using much less additional data. Overall, our method cuts the amount of necessary "real'' training data by a factor of 2--5.

Analyzing international political behavior based on similar precedent circumstances is one of the basic techniques that policymakers use to monitor and assess current situations. Our goal is to investigate how to analyze geopolitical conflicts as sequences of events and to determine what probabilistic models are suitable to perform these analyses. In this paper, we evaluate the performance of N-grams models on the problem of forecasting political conflicts from sequences of events. For the current phase of the project, we focused on event data collected from the Balkans war in the 1990's. Our experimental results indicate that N-gram models have impressive results when applied to this data set, with accuracies above 90\% for most configurations.

Our motivation believe our results will be of special interest to researchers for this work comes from the fact that with the large from the field of medical image analysis, as these images amounts of data that the SDO mission started transmitting, seem to be the closest to our dataset [21. We also identify hand labeling (commonly used by solar physicist in the last some interesting combinations of dimensionality reduction decades) of these images is simply impossible. There have methods and classifiers that behave differently across the been several successful CBIR systems for medical images presented datasets. Our research problem in Solar physics is [2] as well as in other domains [3]; none of them, however, of great practical relevance for Earth's climate since solar have dealt with the volume of data that the SDO mission flares endanger the lives of passengers on commercial airline generates. This NASA mission, only with its Atmospheric routes going over the poles, interrupt radio communications Imaging Assembly (AIA), generates eight 4096 pixels x in bands the military uses, can (and have) knocked 4096 pixels images every 10 seconds. This leads to a data down power grids. The systematic feature recognition and transmission rate of approximately 700 gigabytes per day the study of the metadata, is a key component of the ultimate only from the AIA component (the entire mission is expected prediction of solar activity (space weather).